1
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Moreno-Paz S, Schmitz J, Suarez-Diez M. In silico analysis of design of experiment methods for metabolic pathway optimization. Comput Struct Biotechnol J 2024; 23:1959-1967. [PMID: 38736694 PMCID: PMC11087228 DOI: 10.1016/j.csbj.2024.04.062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 04/25/2024] [Accepted: 04/25/2024] [Indexed: 05/14/2024] Open
Abstract
Microbial cell factories allow the production of chemicals presenting an alternative to traditional fossil fuel-dependent production. However, finding the optimal expression of production pathway genes is crucial for the development of efficient production strains. Unlike sequential experimentation, combinatorial optimization captures the relationships between pathway genes and production, albeit at the cost of conducting multiple experiments. Fractional factorial designs followed by linear modeling and statistical analysis reduce the experimental workload while maximizing the information gained during experimentation. Although tools to perform and analyze these designs are available, guidelines for selecting appropriate factorial designs for pathway optimization are missing. In this study, we leverage a kinetic model of a seven-genes pathway to simulate the performance of a full factorial strain library. We compare this approach to resolution V, IV, III, and Plackett Burman (PB) designs. Additionally, we evaluate the performance of these designs as training sets for a random forest algorithm aimed at identifying best-producing strains. Evaluating the robustness of these designs to noise and missing data, traits inherent to biological datasets, we find that while resolution V designs capture most information present in full factorial data, they necessitate the construction of a large number of strains. On the other hand, resolution III and PB designs fall short in identifying optimal strains and miss relevant information. Besides, given the small number of experiments required for the optimization of a pathway with seven genes, linear models outperform random forest. Consequently, we propose the use of resolution IV designs followed by linear modeling in Design-Build-Test-Learn (DBTL) cycles targeting the screening of multiple factors. These designs enable the identification of optimal strains and provide valuable guidance for subsequent optimization cycles.
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Affiliation(s)
- Sara Moreno-Paz
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, 6708WE Wageningen, the Netherlands
| | - Joep Schmitz
- Department of Science and Research - dsm-firmenich, Science & Research, 2600 MA Delft, the Netherlands
| | - Maria Suarez-Diez
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, 6708WE Wageningen, the Netherlands
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2
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Williamson KI, Herr DJC, Mo Y. Mapping the correlations between bandgap, HOMO, and LUMO trends for meta substituted Zn-MOFs. J Comput Chem 2024; 45:2119-2127. [PMID: 38757907 DOI: 10.1002/jcc.27432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 04/11/2024] [Accepted: 04/24/2024] [Indexed: 05/18/2024]
Abstract
Bandgap is a key property that determines electrical and optical properties in materials. Modulating the bandgap thus is critical in developing novel materials particularly semiconductors with improved features. This study examines the bandgap, highest occupied molecular orbital (HOMO), and lowest unoccupied molecular orbital (LUMO) energy level trends in a metal organic framework, metal-organic framework 5 (MOF-5), as a function of Hammett substituent effect (with the constant σm in the meta-position of the benzene ring) and solvent dielectric effect (with the constant ε). Specifically, experimental design and response surface methodologies helped to assess the significance of trends and correlations between these molecular properties with σm and ε. While the HOMO and LUMO decrease with increasing σm, the LUMO exhibits greater sensitivity to the substituent's electron withdrawing capability. The relative difference in these trends helps to explain why the bandgap tends to decrease with increasing σm.
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Affiliation(s)
- Kyle I Williamson
- Department of Nanoscience, Joint School of Nanoscience and Nanoengineering, University of North Carolina at Greensboro, Greensboro, North Carolina, USA
| | - Daniel J C Herr
- Department of Nanoscience, Joint School of Nanoscience and Nanoengineering, University of North Carolina at Greensboro, Greensboro, North Carolina, USA
| | - Yirong Mo
- Department of Nanoscience, Joint School of Nanoscience and Nanoengineering, University of North Carolina at Greensboro, Greensboro, North Carolina, USA
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3
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Tom G, Schmid SP, Baird SG, Cao Y, Darvish K, Hao H, Lo S, Pablo-García S, Rajaonson EM, Skreta M, Yoshikawa N, Corapi S, Akkoc GD, Strieth-Kalthoff F, Seifrid M, Aspuru-Guzik A. Self-Driving Laboratories for Chemistry and Materials Science. Chem Rev 2024; 124:9633-9732. [PMID: 39137296 PMCID: PMC11363023 DOI: 10.1021/acs.chemrev.4c00055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/15/2024]
Abstract
Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.
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Affiliation(s)
- Gary Tom
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Vector Institute
for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, Canada
| | - Stefan P. Schmid
- Department
of Chemistry and Applied Biosciences, ETH
Zurich, Vladimir-Prelog-Weg 1, CH-8093 Zurich, Switzerland
| | - Sterling G. Baird
- Acceleration
Consortium, 80 St. George
St, Toronto, Ontario M5S 3H6, Canada
| | - Yang Cao
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Acceleration
Consortium, 80 St. George
St, Toronto, Ontario M5S 3H6, Canada
| | - Kourosh Darvish
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Vector Institute
for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, Canada
- Acceleration
Consortium, 80 St. George
St, Toronto, Ontario M5S 3H6, Canada
| | - Han Hao
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Acceleration
Consortium, 80 St. George
St, Toronto, Ontario M5S 3H6, Canada
| | - Stanley Lo
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
| | - Sergio Pablo-García
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
| | - Ella M. Rajaonson
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Vector Institute
for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, Canada
| | - Marta Skreta
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Vector Institute
for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, Canada
| | - Naruki Yoshikawa
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Vector Institute
for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, Canada
| | - Samantha Corapi
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
| | - Gun Deniz Akkoc
- Forschungszentrum
Jülich GmbH, Helmholtz Institute
for Renewable Energy Erlangen-Nürnberg, Cauerstr. 1, 91058 Erlangen, Germany
- Department
of Chemical and Biological Engineering, Friedrich-Alexander Universität Erlangen-Nürnberg, Egerlandstr. 3, 91058 Erlangen, Germany
| | - Felix Strieth-Kalthoff
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- School of
Mathematics and Natural Sciences, University
of Wuppertal, Gaußstraße
20, 42119 Wuppertal, Germany
| | - Martin Seifrid
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Department
of Materials Science and Engineering, North
Carolina State University, Raleigh, North Carolina 27695, United States of America
| | - Alán Aspuru-Guzik
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Vector Institute
for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, Canada
- Acceleration
Consortium, 80 St. George
St, Toronto, Ontario M5S 3H6, Canada
- Department
of Chemical Engineering & Applied Chemistry, University of Toronto, Toronto, Ontario M5S 3E5, Canada
- Department
of Materials Science & Engineering, University of Toronto, Toronto, Ontario M5S 3E4, Canada
- Lebovic
Fellow, Canadian Institute for Advanced
Research (CIFAR), 661
University Ave, Toronto, Ontario M5G 1M1, Canada
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4
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Siddiqui H, Usmani T. Interpretable AI and Machine Learning Classification for Identifying High-Efficiency Donor-Acceptor Pairs in Organic Solar Cells. ACS OMEGA 2024; 9:34445-34455. [PMID: 39157121 PMCID: PMC11325493 DOI: 10.1021/acsomega.4c02157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 06/08/2024] [Accepted: 06/13/2024] [Indexed: 08/20/2024]
Abstract
To enhance the efficiency of organic solar cells, accurately predicting the efficiency of new pairs of donor and acceptor materials is crucial. Presently, most machine learning studies rely on regression models, which often struggle to establish clear rules for distinguishing between high- and low-performing donor-acceptor pairs. This study proposes a novel approach by integrating interpretable AI, specifically using Shapely values, with four supervised machine learning classification models, namely, support vector machines, decision trees, random forest, and gradient boosting. These models aim to identify high-efficiency donor-acceptor pairs based solely on chemical structures and to extract important features that establish general design principles for distinguishing between high- and low-efficiency pairs. For validation purposes, an unsupervised machine learning algorithm utilizing loading vectors obtained from the principal component analysis is employed to identify crucial features associated with high-efficiency donor-acceptor pairs. Interestingly, the features identified by the supervised machine learning approach were found to be a subset of those identified by the unsupervised method. Noteworthy features include the van der Waals surface area, partial equalization of orbital electronegativity, Moreau-Broto autocorrelation, and molecular substructures. Leveraging these features, a backward-working model can be developed, facilitating exploration across a wide array of materials used in organic solar cells. This innovative approach will help navigate the vast chemical compound space of donor and acceptor materials essential in creating high-efficiency organic solar cells.
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Affiliation(s)
- Hamza Siddiqui
- Organic PV Lab, Integral University, Lucknow 226026, India
| | - Tahsin Usmani
- Organic PV Lab, Integral University, Lucknow 226026, India
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5
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Karadaghi L, Williamson EM, To AT, Forsberg AP, Crans KD, Perkins CL, Hayden SC, LiBretto NJ, Baddour FG, Ruddy DA, Malmstadt N, Habas SE, Brutchey RL. Multivariate Bayesian Optimization of CoO Nanoparticles for CO 2 Hydrogenation Catalysis. J Am Chem Soc 2024; 146:14246-14259. [PMID: 38728108 PMCID: PMC11117399 DOI: 10.1021/jacs.4c03789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Revised: 04/30/2024] [Accepted: 05/03/2024] [Indexed: 05/12/2024]
Abstract
The hydrogenation of CO2 holds promise for transforming the production of renewable fuels and chemicals. However, the challenge lies in developing robust and selective catalysts for this process. Transition metal oxide catalysts, particularly cobalt oxide, have shown potential for CO2 hydrogenation, with performance heavily reliant on crystal phase and morphology. Achieving precise control over these catalyst attributes through colloidal nanoparticle synthesis could pave the way for catalyst and process advancement. Yet, navigating the complexities of colloidal nanoparticle syntheses, governed by numerous input variables, poses a significant challenge in systematically controlling resultant catalyst features. We present a multivariate Bayesian optimization, coupled with a data-driven classifier, to map the synthetic design space for colloidal CoO nanoparticles and simultaneously optimize them for multiple catalytically relevant features within a target crystalline phase. The optimized experimental conditions yielded small, phase-pure rock salt CoO nanoparticles of uniform size and shape. These optimized nanoparticles were then supported on SiO2 and assessed for thermocatalytic CO2 hydrogenation against larger, polydisperse CoO nanoparticles on SiO2 and a conventionally prepared catalyst. The optimized CoO/SiO2 catalyst consistently exhibited higher activity and CH4 selectivity (ca. 98%) across various pretreatment reduction temperatures as compared to the other catalysts. This remarkable performance was attributed to particle stability and consistent H* surface coverage, even after undergoing the highest temperature reduction, achieving a more stable catalytic species that resists sintering and carbon occlusion.
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Affiliation(s)
- Lanja
R. Karadaghi
- Department
of Chemistry, University of Southern California, Los Angeles, California 90089, United States
| | - Emily M. Williamson
- Department
of Chemistry, University of Southern California, Los Angeles, California 90089, United States
| | - Anh T. To
- Catalytic
Carbon Transformation and Scale-Up Center, National Renewable Energy Laboratory, Golden, Colorado 80401, United States
| | - Allison P. Forsberg
- Department
of Chemistry, University of Southern California, Los Angeles, California 90089, United States
| | - Kyle D. Crans
- Department
of Chemistry, University of Southern California, Los Angeles, California 90089, United States
| | - Craig L. Perkins
- Materials
Science Center, National Renewable Energy
Laboratory, Golden, Colorado 80401, United States
| | - Steven C. Hayden
- Materials
Science Center, National Renewable Energy
Laboratory, Golden, Colorado 80401, United States
| | - Nicole J. LiBretto
- Catalytic
Carbon Transformation and Scale-Up Center, National Renewable Energy Laboratory, Golden, Colorado 80401, United States
| | - Frederick G. Baddour
- Catalytic
Carbon Transformation and Scale-Up Center, National Renewable Energy Laboratory, Golden, Colorado 80401, United States
| | - Daniel A. Ruddy
- Catalytic
Carbon Transformation and Scale-Up Center, National Renewable Energy Laboratory, Golden, Colorado 80401, United States
| | - Noah Malmstadt
- Department
of Chemistry, University of Southern California, Los Angeles, California 90089, United States
- Mork
Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, California 90089, United States
- Department
of Biomedical Engineering, University of
Southern California, Los Angeles, California 90089, United States
- USC Norris
Comprehensive Cancer Center, University
of Southern California, 1441 Eastlake Avenue, Los Angeles, California 90033, United States
| | - Susan E. Habas
- Catalytic
Carbon Transformation and Scale-Up Center, National Renewable Energy Laboratory, Golden, Colorado 80401, United States
| | - Richard L. Brutchey
- Department
of Chemistry, University of Southern California, Los Angeles, California 90089, United States
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6
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Kruger J, Schutter S, Knoshaug EP, Panczak B, Alt H, Sowell A, Van Wychen S, Fowler M, Hirayama K, Thakkar A, Kumar S, Dong T. De-risking Pretreatment of Microalgae To Produce Fuels and Chemical Co-products. ENERGY & FUELS : AN AMERICAN CHEMICAL SOCIETY JOURNAL 2024; 38:8804-8816. [PMID: 38774063 PMCID: PMC11103650 DOI: 10.1021/acs.energyfuels.4c00508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 04/01/2024] [Accepted: 04/02/2024] [Indexed: 05/24/2024]
Abstract
Conversion of microalgae to renewable fuels and chemical co-products by pretreating and fractionation holds promise as an algal biorefinery concept, but a better understanding of the pretreatment performance as a function of algae strain and composition is necessary to de-risk algae conversion operations. Similarly, there are few examples of algae pretreatment at scales larger than the bench scale. This work aims to de-risk algal biorefinery operations by evaluating the pretreatment performance across nine different microalgae samples and five different pretreatment methods at small (5 mL) scale and further de-risk the operation by scaling pretreatment for one species to the 80 L scale. The pretreatment performance was evaluated by solubilization of feedstock carbon and nitrogen [as total organic carbon (TOC) and total nitrogen (TN)] into the aqueous hydrolysate and extractability of lipids [as fatty acid methyl esters (FAMEs)] from the pretreated solids. A range of responses was noted among the algae samples across pretreatments, with the current dilute Brønsted acid pretreatment using H2SO4 being the most consistent and robust. This pretreatment produced TOC yields to the hydrolysate ranging from 27.7 to 51.1%, TN yields ranging from 12.3 to 76.2%, and FAME yields ranging from 57.9 to 89.9%. In contrast, the other explored pretreatments (other dilute acid pretreatments, dilute alkali pretreatment with NaOH, enzymatic pretreatment, and flash hydrolysis) produced lower or more variable yields across the three metrics. In light of the greater consistency across samples for dilute acid pretreatment, this method was scaled to 80 L to demonstrate scalability with microalgae feedstocks.
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Affiliation(s)
- Jacob
S. Kruger
- Renewable
Resources and Enabling Sciences Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, Colorado 80401, United States
| | - Skylar Schutter
- Renewable
Resources and Enabling Sciences Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, Colorado 80401, United States
| | - Eric P. Knoshaug
- BioSciences
Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, Colorado 80401, United States
| | - Bonnie Panczak
- Renewable
Resources and Enabling Sciences Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, Colorado 80401, United States
| | - Hannah Alt
- Renewable
Resources and Enabling Sciences Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, Colorado 80401, United States
| | - Alicia Sowell
- BioSciences
Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, Colorado 80401, United States
| | - Stefanie Van Wychen
- Renewable
Resources and Enabling Sciences Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, Colorado 80401, United States
| | - Matthew Fowler
- BioSciences
Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, Colorado 80401, United States
| | - Kyoko Hirayama
- Department
of Civil and Environmental Engineering, Old Dominion University, Norfolk, Virginia 23529, United States
| | - Anuj Thakkar
- Department
of Civil and Environmental Engineering, Old Dominion University, Norfolk, Virginia 23529, United States
| | - Sandeep Kumar
- Department
of Civil and Environmental Engineering, Old Dominion University, Norfolk, Virginia 23529, United States
| | - Tao Dong
- Catalytic
Carbon Transformation & Scale-Up Center, National Renewable Energy Laboratory, 15013, Denver West Parkway, Golden, Colorado 80401, United States
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7
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Kettlewell B, Boyd D. Inside the Borate Anomaly: Leveraging a Predictive Modelling Approach to Navigate Complex Composition-Structure-Property Relationships in Oxyhalide Borate Glasses. MATERIALS (BASEL, SWITZERLAND) 2024; 17:2073. [PMID: 38730877 PMCID: PMC11084878 DOI: 10.3390/ma17092073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 04/11/2024] [Accepted: 04/19/2024] [Indexed: 05/13/2024]
Abstract
This study employs a systematic and predictive modelling approach to investigate the structure and properties of multi-component borate glasses. In particular, this work is focused on understanding the individual and interaction effects of multiple constituents on several material properties. By leveraging advanced modeling techniques, this work examines how the inclusion and variation of B2O3, CaF2, TiO2, ZnO, and Na2CO3 influence the glass network, with particular attention to modifier fractions ≥ 30 mol%. This research addresses the gap in knowledge regarding the complex behavior of borate glasses in this high modifier fraction range, known as the borate anomaly, where prediction of glass structure and properties becomes particularly challenging. The use of a design of mixtures (DoM) approach facilitated the generation of polynomial equations indicating the influence of mixture components on various responses, enabling the prediction and optimization of glass properties over broad compositional ranges despite being within the anomalous region. This methodical approach not only advances our understanding of borate glass systems but also underscores the importance of predictive modelling in the accelerated design and development of novel glass materials for diverse applications.
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Affiliation(s)
- Brenna Kettlewell
- School of Biomedical Engineering, Dalhousie University, Halifax, NS B3H 4R2, Canada;
| | - Daniel Boyd
- Department of Applied Oral Sciences, Faculty of Dentistry, Dalhousie University, 5981 University Avenue, P.O. Box 15000, Halifax, NS B3H 4R2, Canada
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8
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Romanholo PVV, de Andrade LM, Silva-Neto HA, Coltro WKT, Sgobbi LF. Digitally Controlled Printing of Bioink Barriers for Paper-Based Analytical Devices: An Environmentally Friendly One-Step Approach. Anal Chem 2024; 96:5349-5356. [PMID: 38554084 DOI: 10.1021/acs.analchem.3c03801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/01/2024]
Abstract
The patterning of hydrophilic paper with hydrophobic materials has emerged as an interesting method for the fabrication of paper-based devices (PADs). Herein, we demonstrate a digitally automated, easy, low-cost, eco-friendly, and readily available method to create highly hydrophobic barriers on paper that can be promptly employed with PADs by simply using a bioink made with rosin, a commercially available natural resin obtained from conifer trees. The bioink can be easily delivered with the use of a ballpoint pen to produce water- and organic solvent-resistant barriers, showing superior properties when compared to other methods such as wax-printing or permanent markers. The approach enables the pen to be attached to a commercially available cutting printer to perform the semiautomated fabrication of hydrophobic barriers for PADs. With the aid of digitally controlled optimization, together with features of machine learning and design of experiments, we show a thorough investigation on the barrier strength that can be further adjusted to the desired application's needs. Then, we explored the barrier sturdiness across various uses, such as wide range aqueous pH sensing and the harsh acidic/organic conditions needed for the colorimetric detection of cholecalciferol.
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Affiliation(s)
- Pedro V V Romanholo
- Instituto de Química, Universidade Federal de Goiás, 74690-900 Goiânia, Goiás, Brazil
| | - Larissa M de Andrade
- Instituto de Química, Universidade Federal de Goiás, 74690-900 Goiânia, Goiás, Brazil
| | - Habdias A Silva-Neto
- Instituto de Química, Universidade Federal de Goiás, 74690-900 Goiânia, Goiás, Brazil
| | - Wendell K T Coltro
- Instituto de Química, Universidade Federal de Goiás, 74690-900 Goiânia, Goiás, Brazil
- Instituto Nacional de Ciência e Tecnologia de Bioanalítica, 13083-861 Campinas, São Paulo, Brazil
| | - Livia F Sgobbi
- Instituto de Química, Universidade Federal de Goiás, 74690-900 Goiânia, Goiás, Brazil
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9
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Furtado AI, Bonifácio VDB, Viveiros R, Casimiro T. Design of Molecularly Imprinted Polymers Using Supercritical Carbon Dioxide Technology. Molecules 2024; 29:926. [PMID: 38474438 DOI: 10.3390/molecules29050926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 02/09/2024] [Accepted: 02/11/2024] [Indexed: 03/14/2024] Open
Abstract
The design and development of affinity polymeric materials through the use of green technology, such as supercritical carbon dioxide (scCO2), is a rapidly evolving field of research with vast applications across diverse areas, including analytical chemistry, pharmaceuticals, biomedicine, energy, food, and environmental remediation. These affinity polymeric materials are specifically engineered to interact with target molecules, demonstrating high affinity and selectivity. The unique properties of scCO2, which present both liquid- and gas-like properties and an accessible critical point, offer an environmentally-friendly and highly efficient technology for the synthesis and processing of polymers. The design and the synthesis of affinity polymeric materials in scCO2 involve several strategies. Commonly, the incorporation of functional groups or ligands into the polymer matrix allows for selective interactions with target compounds. The choice of monomer type, ligands, and synthesis conditions are key parameters of material performance in terms of both affinity and selectivity. In addition, molecular imprinting allied with co-polymerization and surface modification are commonly used in these strategies, enhancing the materials' performance and versatility. This review aims to provide an overview of the key strategies and recent advancements in the design of affinity polymeric materials using scCO2.
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Affiliation(s)
- Ana I Furtado
- LAQV-REQUIMTE, Chemistry Department, NOVA School of Science & Technology, NOVA University of Lisbon, 2829-516 Caparica, Portugal
- iBB-Institute for Bioengineering and Biosciences and i4HB-Institute for Health and Bioeconomy, Instituto Superior Técnico, University of Lisbon, 1049-001 Lisboa, Portugal
| | - Vasco D B Bonifácio
- iBB-Institute for Bioengineering and Biosciences and i4HB-Institute for Health and Bioeconomy, Instituto Superior Técnico, University of Lisbon, 1049-001 Lisboa, Portugal
- Bioengineering Department, Instituto Superior Técnico, University of Lisbon, Av. Rovisco Pais, 1049-001 Lisboa, Portugal
| | - Raquel Viveiros
- LAQV-REQUIMTE, Chemistry Department, NOVA School of Science & Technology, NOVA University of Lisbon, 2829-516 Caparica, Portugal
| | - Teresa Casimiro
- LAQV-REQUIMTE, Chemistry Department, NOVA School of Science & Technology, NOVA University of Lisbon, 2829-516 Caparica, Portugal
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10
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Burrow JN, Eichler JE, Martinez WA, Mullins CB. A Data-Driven Approach to Molten Salt Synthesis of N-Rich Carbon Adsorbents for Selective CO 2 Capture. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2306275. [PMID: 37669465 DOI: 10.1002/adma.202306275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 08/09/2023] [Indexed: 09/07/2023]
Abstract
Applying a design of experiments methodology to the molten salt synthesis of nanoporous carbons enables inverse design and optimization of nitrogen (N)-rich carbon adsorbents with excellent CO2 /N2 selectivity and appreciable CO2 capacity for carbon capture via swing adsorption from dilute gas mixtures such as natural gas combined cycle flue gas. This data-driven study reveals fundamental structure-function relationships between the synthesis conditions, physicochemical properties, and achievable selective adsorption performance of N-rich nanoporous carbons derived from molten salt synthesis for CO2 capture. Taking advantage of size-sieving separation of CO2 (3.30 Å) from N2 (3.64 Å) within the turbostratic nanostructure of these N-rich carbons, while limiting deleterious N2 adsorption in a weaker adsorption site that harms selectivity, enables a large CO2 capacity (0.73 mmol g-1 at 30.4 Torr and 30 °C) with noteworthy concurrent CO2 /N2 selectivity as predicted by the ideal adsorbed solution theory (SIAST = 246) with an adsorbed phase purity of 91% from a simulated gas stream containing only 4% CO2 . Optimized N-rich porous carbons, with good physicochemical stability, low cost, and moderate regeneration energy, can achieve performance for selective CO2 adsorption that competes with other classes of advanced porous materials such as chemisorbing zeolites and functionalized metal-organic frameworks.
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Affiliation(s)
- James N Burrow
- John J. McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA
| | - John E Eichler
- Department of Chemistry, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Wuilian A Martinez
- Department of Chemistry, The University of Texas at Austin, Austin, TX, 78712, USA
| | - C Buddie Mullins
- John J. McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA
- Department of Chemistry, The University of Texas at Austin, Austin, TX, 78712, USA
- Texas Materials Institute, The University of Texas at Austin, Austin, TX, 78712, USA
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11
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Lee T, Lee M, Seo H, Kim M, Chun B, Kwak J. Top-Emitting Quantum Dot Light-Emitting Diodes: Theory, Optimization, and Application. SMALL METHODS 2024; 8:e2300266. [PMID: 37183298 DOI: 10.1002/smtd.202300266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/10/2023] [Indexed: 05/16/2023]
Abstract
The superior optical properties of colloidal quantum dots (QDs) have garnered significant broad interest from academia and industry owing to their successful application in self-emitting QD-based light-emitting diodes (QLEDs). In particular, active research is being conducted on QLEDs with top-emission device architectures (TQLEDs) owing to their advantages such as easy integration with conventional backplanes, high color purity, and excellent light extraction. However, due to the complicated optical phenomena and their highly sensitive optoelectrical properties to experimental variations, TQLEDs cannot be optimized easily for practical use. This review summarizes previous studies that have investigated top-emitting device structures and discusses ways to advance the performance of TQLEDs. First, theories relevant to the optoelectrical properties of TQLEDs are introduced. Second, advancements in device optimization are presented, where the underlying theories for each are considered. Finally, multilateral strategies for TQLEDs to enable their wider application to advanced industries are discussed. This work believes that this review can provide valuable insights for realizing commercial TQLEDs applicable to a broad range of applications.
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Affiliation(s)
- Taesoo Lee
- Department of Electrical and Computer Engineering, Inter-university Semiconductor Research Center, and Soft Foundry Institute, Seoul National University, Seoul, 08826, Republic of Korea
| | - Minhyung Lee
- Department of Electrical and Computer Engineering, Inter-university Semiconductor Research Center, and Soft Foundry Institute, Seoul National University, Seoul, 08826, Republic of Korea
| | - Hansol Seo
- Department of Electrical and Computer Engineering, Inter-university Semiconductor Research Center, and Soft Foundry Institute, Seoul National University, Seoul, 08826, Republic of Korea
| | - Minjun Kim
- Department of Electrical and Computer Engineering, Inter-university Semiconductor Research Center, and Soft Foundry Institute, Seoul National University, Seoul, 08826, Republic of Korea
| | - Beomsoo Chun
- Department of Electrical and Computer Engineering, Inter-university Semiconductor Research Center, and Soft Foundry Institute, Seoul National University, Seoul, 08826, Republic of Korea
| | - Jeonghun Kwak
- Department of Electrical and Computer Engineering, Inter-university Semiconductor Research Center, and Soft Foundry Institute, Seoul National University, Seoul, 08826, Republic of Korea
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12
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Hwang J, Joh H, Kim C, Ahn J, Jeon S. Monolithically Integrated Complementary Ferroelectric FET XNOR Synapse for the Binary Neural Network. ACS APPLIED MATERIALS & INTERFACES 2024; 16:2467-2476. [PMID: 38175955 DOI: 10.1021/acsami.3c13945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2024]
Abstract
Neuromorphic computing, which mimics the structure and principles of the human brain, has the potential to facilitate the hardware implementation of next-generation artificial intelligence systems and process large amounts of data with very low power consumption. Among them, the XNOR synapse-based Binary Neural Network (BNN) has been attracting attention due to its compact neural network parameter size and low hardware cost. The previous XNOR synapse has drawbacks, such as a trade-off between cell density and accuracy. In this work, we show nonvolatile XNOR synapses with high density and accuracy using a monolithically stacked complementary ferroelectric field-effect transistor (C-FeFET) composed of a p-type Si MFMIS-FeFET at the bottom and a 3D stackable n-type Al:IZTO MFS-FeTFT, achieving 60F2 per cell (2C-FeFET). For adjusting the threshold voltage and improving the switching speed (100 ns) of n-type ferroelectric TFT, we employed a dual-gate configuration and a unique operation scheme, making it comparable to those of Si-based FeFETs. We performed array-level simulation with a 512 × 512 subarray size and a 3-bit flash ADC, demonstrating that the image recognition accuracies using the MNIST and CIFAR-10 data sets were increased by 3.17 and 14.07%, respectively, in comparison to other nonvolatile XNOR synapses. In addition, we performed system-level analysis on a 512 × 512 XNOR C-FeFET, exhibiting an outstanding throughput of 717.37 GOPS and an energy efficiency of 196.7 TOPS/W. We expect that our approach would contribute to the high-density memory systems, logic-in-memory technology, and hardware implementation of neural networks.
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Affiliation(s)
- Junghyeon Hwang
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291, Daehak-ro, Yuseong-gu, Daejeon 34141, Korea
| | - Hongrae Joh
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291, Daehak-ro, Yuseong-gu, Daejeon 34141, Korea
| | - Chaeheon Kim
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291, Daehak-ro, Yuseong-gu, Daejeon 34141, Korea
| | - Jinho Ahn
- Division of Materials Science and Engineering, Hanyang University, 222, Wangsimni-ro, Seonhdong-gu, Seoul 04763, Korea
| | - Sanghun Jeon
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291, Daehak-ro, Yuseong-gu, Daejeon 34141, Korea
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13
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Lee J, Lee JH, Lee C, Lee H, Jin M, Kim J, Shin JC, Lee E, Kim YS. Machine Learning Driven Channel Thickness Optimization in Dual-Layer Oxide Thin-Film Transistors for Advanced Electrical Performance. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2303589. [PMID: 37985921 PMCID: PMC10754089 DOI: 10.1002/advs.202303589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 10/08/2023] [Indexed: 11/22/2023]
Abstract
Machine learning (ML) provides temporal advantage and performance improvement in practical electronic device design by adaptive learning. Herein, Bayesian optimization (BO) is successfully applied to the design of optimal dual-layer oxide semiconductor thin film transistors (OS TFTs). This approach effectively manages the complex correlation and interdependency between two oxide semiconductor layers, resulting in the efficient design of experiment (DoE) and reducing the trial-and-error. Considering field effect mobility (𝜇) and threshold voltage (Vth ) simultaneously, the dual-layer structure designed by the BO model allows to produce OS TFTs with remarkable electrical performance while significantly saving an amount of experimental trial (only 15 data sets are required). The optimized dual-layer OS TFTs achieve the enhanced field effect mobility of 36.1 cm2 V-1 s-1 and show good stability under bias stress with negligible difference in its threshold voltage compared to conventional IGZO TFTs. Moreover, the BO algorithm is successfully customized to the individual preferences by applying the weight factors assigned to both field effect mobility (𝜇) and threshold voltage (Vth ).
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Affiliation(s)
- Jiho Lee
- Department of Applied Bioengineering, Graduate School of Convergence Science and TechnologySeoul National UniversityGwanak‐ro 1, Gwanak‐guSeoul08826Republic of Korea
| | - Jae Hak Lee
- Program in Nano Science and TechnologyGraduate School of Convergence Science and TechnologySeoul National UniversityGwanak‐ro 1, Gwanak‐guSeoul08826Republic of Korea
- Samsung Display Company, Ltd.1 Samsung‐ro, Giheung‐guYongin‐siGyeonggi‐do17113Republic of Korea
| | - Chan Lee
- Department of Chemical and Biological EngineeringCollege of EngineeringSeoul National UniversityGwanak‐ro 1, Gwanak‐guSeoul08826Republic of Korea
| | - Haeyeon Lee
- Department of Chemical and Biological EngineeringCollege of EngineeringSeoul National UniversityGwanak‐ro 1, Gwanak‐guSeoul08826Republic of Korea
| | - Minho Jin
- Program in Nano Science and TechnologyGraduate School of Convergence Science and TechnologySeoul National UniversityGwanak‐ro 1, Gwanak‐guSeoul08826Republic of Korea
| | - Jiyeon Kim
- Department of Applied Bioengineering, Graduate School of Convergence Science and TechnologySeoul National UniversityGwanak‐ro 1, Gwanak‐guSeoul08826Republic of Korea
| | - Jong Chan Shin
- Department of Chemical and Biological EngineeringCollege of EngineeringSeoul National UniversityGwanak‐ro 1, Gwanak‐guSeoul08826Republic of Korea
| | - Eungkyu Lee
- Department of Electronic EngineeringKyung Hee UniversityYongin‐siGyeonggi‐do17104Republic of Korea
| | - Youn Sang Kim
- Department of Applied Bioengineering, Graduate School of Convergence Science and TechnologySeoul National UniversityGwanak‐ro 1, Gwanak‐guSeoul08826Republic of Korea
- Program in Nano Science and TechnologyGraduate School of Convergence Science and TechnologySeoul National UniversityGwanak‐ro 1, Gwanak‐guSeoul08826Republic of Korea
- Department of Chemical and Biological EngineeringCollege of EngineeringSeoul National UniversityGwanak‐ro 1, Gwanak‐guSeoul08826Republic of Korea
- Institute of Chemical ProcessesCollege of EngineeringSeoul National UniversityGwanak‐ro 1, Gwanak‐guSeoul08826Republic of Korea
- Advanced Institutes of Convergence TechnologyGwanggyo‐ro 145, Yeongtong‐guSuwon16229Republic of Korea
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14
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Szymanski NJ, Nevatia P, Bartel CJ, Zeng Y, Ceder G. Autonomous and dynamic precursor selection for solid-state materials synthesis. Nat Commun 2023; 14:6956. [PMID: 37907493 PMCID: PMC10618174 DOI: 10.1038/s41467-023-42329-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 10/06/2023] [Indexed: 11/02/2023] Open
Abstract
Solid-state synthesis plays an important role in the development of new materials and technologies. While in situ characterization and ab-initio computations have advanced our understanding of materials synthesis, experiments targeting new compounds often still require many different precursors and conditions to be tested. Here we introduce an algorithm (ARROWS3) designed to automate the selection of optimal precursors for solid-state materials synthesis. This algorithm actively learns from experimental outcomes to determine which precursors lead to unfavorable reactions that form highly stable intermediates, preventing the target material's formation. Based on this information, ARROWS3 proposes new experiments using precursors it predicts to avoid such intermediates, thereby retaining a larger thermodynamic driving force to form the target. We validate this approach on three experimental datasets, containing results from over 200 synthesis procedures. In comparison to black-box optimization, ARROWS3 identifies effective precursor sets for each target while requiring substantially fewer experimental iterations. These findings highlight the importance of domain knowledge in optimization algorithms for materials synthesis, which are critical for the development of fully autonomous research platforms.
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Affiliation(s)
- Nathan J Szymanski
- Department of Materials Science and Engineering, UC Berkeley, Berkeley, CA, 94720, USA
- Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Pragnay Nevatia
- Department of Chemical Engineering, UC Berkeley, Berkeley, CA, 94720, USA
| | - Christopher J Bartel
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Yan Zeng
- Department of Materials Science and Engineering, UC Berkeley, Berkeley, CA, 94720, USA.
| | - Gerbrand Ceder
- Department of Materials Science and Engineering, UC Berkeley, Berkeley, CA, 94720, USA.
- Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
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15
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Williamson E, Brutchey RL. Using Data-Driven Learning to Predict and Control the Outcomes of Inorganic Materials Synthesis. Inorg Chem 2023; 62:16251-16262. [PMID: 37767941 PMCID: PMC10565808 DOI: 10.1021/acs.inorgchem.3c02697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Indexed: 09/29/2023]
Abstract
The design of inorganic materials for various applications critically depends on our ability to manipulate their synthesis in a rational, robust, and controllable fashion. Different from the conventional trial-and-error approach, data-driven techniques such as the design of experiments (DoE) and machine learning are an effective and more efficient way to predictably control materials synthesis. Here, we present a Viewpoint on recent progress in leveraging such techniques for predicting and controlling the outcomes of inorganic materials synthesis. We first compare how the design choice (statistical DoE vs machine learning) affects the type of control it can offer over the resulting product attributes, information elucidated, and experimental cost. These attributes are supported by discussing select case studies from the recent literature that highlight the power of these techniques for materials synthesis. The influence of experimental bias is next discussed, followed finally by our perspectives on the major challenges in the widespread implementation of predictable and controllable materials synthesis using data-driven techniques.
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Affiliation(s)
- Emily
M. Williamson
- Department of Chemistry, University of Southern California, Los Angeles, California 90089, United States
| | - Richard L. Brutchey
- Department of Chemistry, University of Southern California, Los Angeles, California 90089, United States
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16
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Williamson E, Sun Z, Tappan BA, Brutchey RL. Predictive Synthesis of Copper Selenides Using a Multidimensional Phase Map Constructed with a Data-Driven Classifier. J Am Chem Soc 2023; 145:17954-17964. [PMID: 37540836 PMCID: PMC10436277 DOI: 10.1021/jacs.3c05490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Indexed: 08/06/2023]
Abstract
Copper selenides are an important family of materials with applications in catalysis, plasmonics, photovoltaics, and thermoelectrics. Despite being a binary material system, the Cu-Se phase diagram is complex and contains multiple crystal structures in addition to several metastable structures that are not found on the thermodynamic phase diagram. Consequently, the ability to synthetically navigate this complex phase space poses a significant challenge. We demonstrate that data-driven learning can successfully map this phase space in a minimal number of experiments. We combine soft chemistry (chimie douce) synthetic methods with multivariate analyses via classification techniques to enable predictive phase determination. A surrogate model was constructed with experimental data derived from a design matrix of four experimental variables: C-Se bond strength of the selenium precursor, time, temperature, and solvent composition. The reactions in the surrogate model resulted in 11 distinct phase combinations of copper selenide. These data were used to train a classification model that predicts the phase with 95.7% accuracy. The resulting decision tree enabled conclusions to be drawn about how the experimental variables affect the phase and provided prescriptive synthetic conditions for specific phase isolation. This guided the accelerated phase targeting in a minimum number of experiments of klockmannite CuSe, which could not be isolated in any of the reactions used to construct the surrogate model. The reaction conditions that the model predicted to synthesize klockmannite CuSe were experimentally validated, highlighting the utility of this approach.
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Affiliation(s)
- Emily
M. Williamson
- Department of Chemistry, University of Southern California, Los Angeles, California 90089, United States
| | - Zhaohong Sun
- Department of Chemistry, University of Southern California, Los Angeles, California 90089, United States
| | - Bryce A. Tappan
- Department of Chemistry, University of Southern California, Los Angeles, California 90089, United States
| | - Richard L. Brutchey
- Department of Chemistry, University of Southern California, Los Angeles, California 90089, United States
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17
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Quinn H, Wang W, Werner JG, Brown KA. Screening for electrically conductive defects in thin functional films using electrochemiluminescence. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023. [PMID: 37466448 DOI: 10.1039/d3ay00687e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2023]
Abstract
Multifunctional thin films in energy-related devices often must be electrically insulating where a single nanoscale defect can result in complete device-scale failure. Locating and characterizing such defects presents a fundamental problem where high-resolution imaging methods are needed to find defects, but imaging with high spatial resolution limits the field of view and thus the measurement throughput. Here, we present a novel high-throughput method for detecting sub-micron defects in insulating thin films by leveraging the electrochemiluminescence (ECL) of luminol. Through a systematic study of reagent concentrations, buffers, voltage, and excitation time, we identify optimized conditions under which it is possible to detect sub-micron defects at high-throughput. Extrapolating from the signal to background observed for detecting 440 nm wide lines and 620 nm diameter circles, we estimate the minimum detectable features to be lines as narrow as 2.5 nm in width and pinholes as small as 70 nm in radius. We further explore this method by using it to characterize a nominally insulating poly(phenylene oxide) film and find conductive defects that are cross-correlated with high-resolution atomic force microscopy to provide feedback to synthesis. Given this assay's inherent parallelizability and scalability, it is expected to have a major impact on the automated discovery of multifunctional films.
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Affiliation(s)
- Harley Quinn
- Division of Materials Science & Engineering, Boston University, Boston, MA, 02215, USA.
| | - Wenlu Wang
- Division of Materials Science & Engineering, Boston University, Boston, MA, 02215, USA.
| | - Jörg G Werner
- Division of Materials Science & Engineering, Boston University, Boston, MA, 02215, USA.
- Department of Mechanical Engineering, Boston University, Boston, MA, 02215, USA
| | - Keith A Brown
- Division of Materials Science & Engineering, Boston University, Boston, MA, 02215, USA.
- Department of Mechanical Engineering, Boston University, Boston, MA, 02215, USA
- Department of Physics, Boston University, Boston, MA, 02215, USA
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18
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Foroughipour M, Nezamzadeh-Ejhieh A. CaTiO 3/g-C 3N 4 heterojunction-based composite photocatalyst: Part I: Experimental design, kinetics, and scavenging agents' effects in photocatalytic degradation of gemifloxacin. CHEMOSPHERE 2023; 334:139019. [PMID: 37236274 DOI: 10.1016/j.chemosphere.2023.139019] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 05/20/2023] [Accepted: 05/22/2023] [Indexed: 05/28/2023]
Abstract
A critical, challenging environmental issue is explored pollution of water supplies by discharging industrial/pharmaceutical/hospital/urban wastewaters into the aquatic environment. These needs introducing/developing novel photocatalysts/adsorbents/procedures for removing or mineralizing various pollutants in wastewater before discharging them into marine environments. Further, optimizing conditions to achieve the highest removal efficiency is an important issue. In this study, CaTiO3/g-C3N4 (CTCN) heterostructure was synthesized and characterized by some identification techniques. The simultaneous interaction effects of the experimental variables on the boosted photocatalytic activity of CTCN in the degradation of gemifloxcacin (GMF) were studied in RSM design. The optimal values for four parameters were: catalyst dosage: 0.63 g L-1, pH: 6.7, CGMF: 1 mg L-1, and irradiation time: 27.5 min, with approximately 78.2% of degradation efficiency. The quenching effects of the scavenging agents were studied to show the reactive species' relative importance in GMF photodegradation. The results illustrate that the reactive •OH plays a significant role, and the electron plays a minor role in the degradation process. The direct Z-scheme mechanism better described the photodegradation mechanism due to the great oxidative and reductive abilities of prepared composite photocatalysts. This mechanism is an approach to efficiently separating photogenerated charge carriers and improving the CaTiO3/g-C3N4 composite photocatalyst activity. The COD has been performed to study the details of the mineralization of GMF. The pseudo-first-order rat (from the Hinshelwood model) constants of 0.046 min-1 (t1/2 = 15.1 min) and 0.048 min-1 (t1/2 = 14.4 min) were respectively obtained from the GMF photodegradation data and COD results. The prepared photocatalyst retained its activity after five reusing runs.
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Affiliation(s)
- Mehnoosh Foroughipour
- Department of Chemistry, Shahreza Branch, Islamic Azad University, P.O. Box 311-86145, Shahreza, Isfahan, Iran.
| | - Alireza Nezamzadeh-Ejhieh
- Department of Chemistry, Shahreza Branch, Islamic Azad University, P.O. Box 311-86145, Shahreza, Isfahan, Iran.
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19
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Shields MD, Gurley K, Catarelli R, Chauhan M, Ojeda-Tuz M, Masters FJ. Active learning applied to automated physical systems increases the rate of discovery. Sci Rep 2023; 13:8402. [PMID: 37225752 DOI: 10.1038/s41598-023-35257-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 05/15/2023] [Indexed: 05/26/2023] Open
Abstract
Active machine learning is widely used in computational studies where repeated numerical simulations can be conducted on high performance computers without human intervention. But translation of these active learning methods to physical systems has proven more difficult and the accelerated pace of discoveries aided by these methods remains as yet unrealized. Through the presentation of a general active learning framework and its application to large-scale boundary layer wind tunnel experiments, we demonstrate that the active learning framework used so successfully in computational studies is directly applicable to the investigation of physical experimental systems and the corresponding improvements in the rate of discovery can be transformative. We specifically show that, for our wind tunnel experiments, we are able to achieve in approximately 300 experiments a learning objective that would be impossible using traditional methods.
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Affiliation(s)
- Michael D Shields
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21212, USA.
| | - Kurtis Gurley
- Department of Civil and Coastal Engineering, University of Florida, Gainesville, FL, 32611, USA
| | - Ryan Catarelli
- Department of Civil and Coastal Engineering, University of Florida, Gainesville, FL, 32611, USA
| | - Mohit Chauhan
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21212, USA
| | - Mariel Ojeda-Tuz
- Department of Civil and Coastal Engineering, University of Florida, Gainesville, FL, 32611, USA
| | - Forrest J Masters
- Department of Civil and Coastal Engineering, University of Florida, Gainesville, FL, 32611, USA
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20
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Ge L, Ke Y, Li X. Machine learning integrated photocatalysis: progress and challenges. Chem Commun (Camb) 2023; 59:5795-5806. [PMID: 37093605 DOI: 10.1039/d3cc00989k] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Discovering efficient photocatalysts has long been the goal of photocatalysis, which has traditionally been driven by serendipitous or try-and-error strategies. Recent developments in photocatalysis integrated with machine learning techniques promise to accelerate the discovery of photocatalysts, but are also facing significant challenges. In this review, advances in machine learning integrated photocatalysis are first presented from the perspective of three main photocatalytic processes: light harvesting, charge generation and separation, and surface redox reactions. Next, progress in using machine learning to understand complex photoactivity-structure relationships and identify the factors governing activity follows. A future photocatalysis paradigm is then provided with the integration of artificial intelligence, robots and automation. Lastly, we discuss the current challenges in machine learning integrated photocatalysis. This review aims to provide a systematic overview and guidelines to the broad scientific community interested in photocatalysis and artificial intelligence for solar fuel synthesis.
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Affiliation(s)
- Luyao Ge
- Key Laboratory of the Ministry of Education for Advanced Catalysis Materials, Zhejiang Key Laboratory for Reactive Chemistry on Solid Surfaces, Zhejiang Normal University, Jinhua 321004, China.
| | - Yuanzhen Ke
- Key Laboratory of the Ministry of Education for Advanced Catalysis Materials, Zhejiang Key Laboratory for Reactive Chemistry on Solid Surfaces, Zhejiang Normal University, Jinhua 321004, China.
| | - Xiaobo Li
- Key Laboratory of the Ministry of Education for Advanced Catalysis Materials, Zhejiang Key Laboratory for Reactive Chemistry on Solid Surfaces, Zhejiang Normal University, Jinhua 321004, China.
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21
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Taylor CJ, Pomberger A, Felton KC, Grainger R, Barecka M, Chamberlain TW, Bourne RA, Johnson CN, Lapkin AA. A Brief Introduction to Chemical Reaction Optimization. Chem Rev 2023; 123:3089-3126. [PMID: 36820880 PMCID: PMC10037254 DOI: 10.1021/acs.chemrev.2c00798] [Citation(s) in RCA: 43] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
From the start of a synthetic chemist's training, experiments are conducted based on recipes from textbooks and manuscripts that achieve clean reaction outcomes, allowing the scientist to develop practical skills and some chemical intuition. This procedure is often kept long into a researcher's career, as new recipes are developed based on similar reaction protocols, and intuition-guided deviations are conducted through learning from failed experiments. However, when attempting to understand chemical systems of interest, it has been shown that model-based, algorithm-based, and miniaturized high-throughput techniques outperform human chemical intuition and achieve reaction optimization in a much more time- and material-efficient manner; this is covered in detail in this paper. As many synthetic chemists are not exposed to these techniques in undergraduate teaching, this leads to a disproportionate number of scientists that wish to optimize their reactions but are unable to use these methodologies or are simply unaware of their existence. This review highlights the basics, and the cutting-edge, of modern chemical reaction optimization as well as its relation to process scale-up and can thereby serve as a reference for inspired scientists for each of these techniques, detailing several of their respective applications.
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Affiliation(s)
- Connor J Taylor
- Astex Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge CB4 0QA, U.K
- Innovation Centre in Digital Molecular Technologies, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
| | - Alexander Pomberger
- Innovation Centre in Digital Molecular Technologies, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
| | - Kobi C Felton
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K
| | - Rachel Grainger
- Astex Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge CB4 0QA, U.K
| | - Magda Barecka
- Chemical Engineering Department, Northeastern University, 360 Huntington Avenue, Boston, Massachusetts 02115, United States
- Chemistry and Chemical Biology Department, Northeastern University, 360 Huntington Avenue, Boston, Massachusetts 02115, United States
- Cambridge Centre for Advanced Research and Education in Singapore, 1 Create Way, 138602 Singapore
| | - Thomas W Chamberlain
- Institute of Process Research and Development, School of Chemistry and School of Chemical and Process Engineering, University of Leeds, Leeds LS2 9JT, U.K
| | - Richard A Bourne
- Institute of Process Research and Development, School of Chemistry and School of Chemical and Process Engineering, University of Leeds, Leeds LS2 9JT, U.K
| | - Christopher N Johnson
- Astex Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge CB4 0QA, U.K
| | - Alexei A Lapkin
- Innovation Centre in Digital Molecular Technologies, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
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22
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Akiyoshi M, Ikemoto K, Isobe H. Tier-grown Expansion of Design-of-Experiments Parameter Spaces for Synthesis of a Nanometer-scale Macrocycle. Chem Asian J 2023; 18:e202201141. [PMID: 36424827 DOI: 10.1002/asia.202201141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 11/24/2022] [Accepted: 11/25/2022] [Indexed: 11/26/2022]
Abstract
A method to find optimum synthetic conditions was devised by combining a data-driven empirical model with a traditional mechanistic model. In this method, an experimental parameter space was empirically obtained by Design-of-Experiments optimizations with machine-learning supplements and was strategically expanded by examination of the mechanistic model of the reaction paths. An extra tier grown on the original 3×3×3 parameter space succeeded in allocating an optimum reaction condition in the expanded 3×3×4 parameter space. The method was specifically devised for the synthesis of a macrocycle, [n]cyclo-meta-phenylenes ([n]CMP), and the largest congener with n=12 was synthesized and fully characterized for the first time. Crystallographic and photophysical analyses revealed favorable features of [12]CMP for the material applications.
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Affiliation(s)
- Misato Akiyoshi
- Department of Chemistry, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Koki Ikemoto
- Department of Chemistry, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Hioyuki Isobe
- Department of Chemistry, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo, 113-0033, Japan
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23
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Zhai H, Yeo J. Computational Design of Antimicrobial Active Surfaces via Automated Bayesian Optimization. ACS Biomater Sci Eng 2023; 9:269-279. [PMID: 36537745 DOI: 10.1021/acsbiomaterials.2c01079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Biofilms pose significant problems for engineers in diverse fields, such as marine science, bioenergy, and biomedicine, where effective biofilm control is a long-term goal. The adhesion and surface mechanics of biofilms play crucial roles in generating and removing biofilm. Designing customized nanosurfaces with different surface topologies can alter the adhesive properties to remove biofilms more easily and greatly improve long-term biofilm control. To rapidly design such topologies, we employ individual-based modeling and Bayesian optimization to automate the design process and generate different active surfaces for effective biofilm removal. Our framework successfully generated optimized functional nanosurfaces for improved biofilm removal through applied shear and vibration. Densely distributed short pillar topography is the optimal geometry to prevent biofilm formation. Under fluidic shearing, the optimal topography is to sparsely distribute tall, slim, pillar-like structures. When subjected to either vertical or lateral vibrations, thick trapezoidal cones are found to be optimal. Optimizing the vibrational loading indicates a small vibration magnitude with relatively low frequencies is more efficient in removing biofilm. Our results provide insights into various engineering fields that require surface-mediated biofilm control. Our framework can also be applied to more general materials design and optimization.
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Affiliation(s)
- Hanfeng Zhai
- Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, New York14850, United States
| | - Jingjie Yeo
- Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, New York14850, United States
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24
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Song B, Liu T, Yang S, Liu J, Chen J. Data-Driven Operation Modeling and Optimal Design for Batch Cooling Crystallization with a Case Study on β-LGA. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c02937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Bo Song
- Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian 116024, China
- Institute of Advanced Control Technology, Dalian University of Technology, Dalian 116024, China
| | - Tao Liu
- Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian 116024, China
- Institute of Advanced Control Technology, Dalian University of Technology, Dalian 116024, China
| | - Siwei Yang
- Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian 116024, China
- Institute of Advanced Control Technology, Dalian University of Technology, Dalian 116024, China
| | - Jingxiang Liu
- Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian 116024, China
- School of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China
| | - Junghui Chen
- Department of Chemical Engineering, Chung-Yuan Christian University, Chung-Li District, Taoyuan 32023, Taiwan
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25
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Jia C, Das P, Zeng Q, Gabriel JCP, Tay CY, Lee JM. Activated recovery of PVC from contaminated waste extension cord-cable using a weak acid. CHEMOSPHERE 2022; 303:134878. [PMID: 35569636 DOI: 10.1016/j.chemosphere.2022.134878] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 05/01/2022] [Accepted: 05/04/2022] [Indexed: 06/15/2023]
Abstract
Waste electronic and electrical equipment are complex mixtures of valuable and/or toxic materials, which pose serious challenges in their recycling or disposal, for example, electrical transmission wires insulated in polyvinyl chloride materials. These materials are frequently found contaminated with toxic chemical elements, such as Pb, Hg, Cr, or Cd, and are discarded without decontamination. To resolve this problem, we developed a microwave-assisted extraction process to remove toxic metals from plastic e-waste. We processed diluted (30 wt%) citric acid at 210 °C for 1 h inside a pressurized vessel heated by microwave, and found it was suitable not only for the extraction of the toxic metals (∼100%) but also for a significant plastic recovery (>50 wt%). To predict an optimized process window, the support vector regression machine learning algorithm was applied, which reduced the amount of experimentation required while still giving accurate results. Conditions optimized for the reference sample also led to maximum extraction of toxic metals from real-life extension cord waste. We also report that the recovered plastic's properties remained intact after the extraction.
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Affiliation(s)
- Chunmiao Jia
- Energy Research Institute @NTU, Nanyang Technological University, 1 CleanTech Loop, 637141, Singapore
| | - Pallab Das
- Energy Research Institute @NTU, Nanyang Technological University, 1 CleanTech Loop, 637141, Singapore
| | - Qiang Zeng
- Energy Research Institute @NTU, Nanyang Technological University, 1 CleanTech Loop, 637141, Singapore
| | - Jean-Christophe P Gabriel
- Energy Research Institute @NTU, Nanyang Technological University, 1 CleanTech Loop, 637141, Singapore; Universite Paris-Saclay, CEA, CNRS, NIMBE, LICSEN, F-91191, Gif-sur-Yvette, France
| | - Chor Yong Tay
- Energy Research Institute @NTU, Nanyang Technological University, 1 CleanTech Loop, 637141, Singapore; School of Materials Science & Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore
| | - Jong-Min Lee
- Energy Research Institute @NTU, Nanyang Technological University, 1 CleanTech Loop, 637141, Singapore; School of Chemical and Biomedical Engineering, Nanyang Technological University, 62 Nanyang Drive, Singapore, 637459, Singapore.
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26
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del Cueto M, Rawski-Furman C, Aragó J, Ortí E, Troisi A. Data-Driven Analysis of Hole-Transporting Materials for Perovskite Solar Cells Performance. THE JOURNAL OF PHYSICAL CHEMISTRY. C, NANOMATERIALS AND INTERFACES 2022; 126:13053-13061. [PMID: 35983311 PMCID: PMC9376947 DOI: 10.1021/acs.jpcc.2c04725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 07/15/2022] [Indexed: 06/15/2023]
Abstract
We have created a dataset of 269 perovskite solar cells, containing information about their perovskite family, cell architecture, and multiple hole-transporting materials features, including fingerprints, additives, and structural and electronic features. We propose a predictive machine learning model that is trained on these data and can be used to screen possible candidate hole-transporting materials. Our approach allows us to predict the performance of perovskite solar cells with reasonable accuracy and is able to successfully identify most of the top-performing and lowest-performing hole-transporting materials in the dataset. We discuss the effect of data biases on the distribution of perovskite families/architectures on the model's accuracy and offer an analysis with a subset of the data to accurately study the effect of the hole-transporting material on the solar cell performance. Finally, we discuss some chemical fragments, like arylamine and aryloxy groups, which present a relatively large positive correlation with the efficiency of the cell, whereas other groups, like thiophene groups, display a negative correlation with power conversion efficiency (PCE).
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Affiliation(s)
- Marcos del Cueto
- Department
of Chemistry, University of Liverpool, Liverpool L69 3BX, U.K.
| | | | - Juan Aragó
- Instituto
de Ciencia Molecular (ICMol), Universidad
de Valencia, Catedrático José Beltrán 2, Paterna 46980, Spain
| | - Enrique Ortí
- Instituto
de Ciencia Molecular (ICMol), Universidad
de Valencia, Catedrático José Beltrán 2, Paterna 46980, Spain
| | - Alessandro Troisi
- Department
of Chemistry, University of Liverpool, Liverpool L69 3BX, U.K.
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27
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Multiamperometric-SERS detection of melamine on gold screen-printed electrodes. J Electroanal Chem (Lausanne) 2022. [DOI: 10.1016/j.jelechem.2022.116478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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28
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Ikemoto K, Akiyoshi M, Mio T, Nishioka K, Sato S, Isobe H. Synthesis of a Negatively Curved Nanocarbon Molecule with an Octagonal Omphalos via Design-of-Experiments Optimizations Supplemented by Machine Learning. Angew Chem Int Ed Engl 2022; 61:e202204035. [PMID: 35603558 DOI: 10.1002/anie.202204035] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Indexed: 12/16/2022]
Abstract
A saddle-shaped nanocarbon molecule was synthesized, which revealed the existence of negative Gauss curvatures on a >3-nm molecular structure possessing 192 π-electrons. The synthesis was facilitated by a protocol developed with Design-of-Experiments optimizations and machine-learning predictions, and spectroscopy and crystallography were used to reveal the saddle-shaped structure of the molecule. Solution-phase analyses showed the presence of dimeric assembly, and crystallographic analyses revealed the stacked dimeric structures. The stacked crystal structure was scrutinized by various methods, including Gauss curvatures derived from the discrete surface theory of geometry, to reveal the important role of the molecular Gauss curvature in dimeric assembly.
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Affiliation(s)
- Koki Ikemoto
- Department of Chemistry, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Misato Akiyoshi
- Department of Chemistry, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Tatsuru Mio
- Department of Chemistry, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Kaito Nishioka
- Department of Chemistry, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Sota Sato
- Department of Chemistry, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo, 113-0033, Japan.,Present address: Department of Applied Chemistry, The University of Tokyo, Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan
| | - Hiroyuki Isobe
- Department of Chemistry, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo, 113-0033, Japan
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29
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van Putten R, Eyke NS, Baumgartner LM, Schultz VL, Filonenko GA, Jensen KF, Pidko EA. Automation and Microfluidics for the Efficient, Fast, and Focused Reaction Development of Asymmetric Hydrogenation Catalysis. CHEMSUSCHEM 2022; 15:e202200333. [PMID: 35470567 PMCID: PMC9401021 DOI: 10.1002/cssc.202200333] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 04/17/2022] [Indexed: 06/14/2023]
Abstract
Automation and microfluidic tools potentially enable efficient, fast, and focused reaction development of complex chemistries, while minimizing resource- and material consumption. The introduction of automation-assisted workflows will contribute to the more sustainable development and scale-up of new and improved catalytic technologies. Herein, the application of automation and microfluidics to the development of a complex asymmetric hydrogenation reaction is described. Screening and optimization experiments were performed using an automated microfluidic platform, which enabled a drastic reduction in the material consumption compared to conventional laboratory practices. A suitable catalytic system was identified from a library of RuII -diamino precatalysts. In situ precatalyst activation was studied with 1 H/31 P nuclear magnetic resonance (NMR), and the reaction was scaled up to multigram quantities in a batch autoclave. These reactions were monitored using an automated liquid-phase sampling system. Ultimately, in less than a week of total experimental time, multigram quantities of the target enantiopure alcohol product were provided by this automation-assisted approach.
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Affiliation(s)
- Robbert van Putten
- Inorganic Systems EngineeringDepartment of Chemical EngineeringDelft University of TechnologyVan der Maasweg 92629HZDelftNetherlands
- Department of Chemical EngineeringMassachusetts Institute of Technology77 Massachusetts Avenue02139CambridgeMassachusettsUnited States
| | - Natalie S. Eyke
- Department of Chemical EngineeringMassachusetts Institute of Technology77 Massachusetts Avenue02139CambridgeMassachusettsUnited States
| | - Lorenz M. Baumgartner
- Department of Chemical EngineeringMassachusetts Institute of Technology77 Massachusetts Avenue02139CambridgeMassachusettsUnited States
| | - Victor L. Schultz
- Department of Chemical EngineeringMassachusetts Institute of Technology77 Massachusetts Avenue02139CambridgeMassachusettsUnited States
| | - Georgy A. Filonenko
- Inorganic Systems EngineeringDepartment of Chemical EngineeringDelft University of TechnologyVan der Maasweg 92629HZDelftNetherlands
| | - Klavs F. Jensen
- Department of Chemical EngineeringMassachusetts Institute of Technology77 Massachusetts Avenue02139CambridgeMassachusettsUnited States
| | - Evgeny A. Pidko
- Inorganic Systems EngineeringDepartment of Chemical EngineeringDelft University of TechnologyVan der Maasweg 92629HZDelftNetherlands
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30
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Extrapolating missing antibody-virus measurements across serological studies. Cell Syst 2022; 13:561-573.e5. [PMID: 35798005 DOI: 10.1016/j.cels.2022.06.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 05/03/2022] [Accepted: 06/10/2022] [Indexed: 01/25/2023]
Abstract
The development of new vaccines, as well as our understanding of key processes that shape viral evolution and host antibody repertoires, relies on measuring multiple antibody responses against large panels of viruses. Given the enormous diversity of circulating virus strains and antibody responses, comprehensively testing all antibody-virus interactions is infeasible. Even within individual studies with limited panels, exhaustive testing is not always performed, and there is no common framework for combining information across studies with partially overlapping panels, especially when the assay type or host species differ. Prior studies have demonstrated that antibody-virus interactions can be characterized in a vastly simpler and lower dimensional space, suggesting that relatively few measurements could predict unmeasured antibody-virus interactions. Here, we apply matrix completion to several large-scale influenza and HIV-1 studies. We explore how prediction accuracy evolves as the number of measurements changes and approximates the number of additional measurements necessary in several highly incomplete datasets (suggesting ∼250,000 measurements could be saved). In addition, we show how the method can combine disparate datasets, even when the number of available measurements is below the theoretical limit that guarantees successful prediction. This approach can be readily generalized to other viruses or more broadly to other low-dimensional biological datasets.
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31
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Rohim MAS, Nazmi N, Bahiuddin I, Mazlan SA, Norhaniza R, Yamamoto S, Nordin NA, Abdul Aziz SA. Prediction for magnetostriction magnetorheological foam using machine learning method. J Appl Polym Sci 2022. [DOI: 10.1002/app.52798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Muhamad Amirul Sunni Rohim
- Malaysia‐Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur Malaysia
| | - Nurhazimah Nazmi
- Malaysia‐Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur Malaysia
| | - Irfan Bahiuddin
- Mechanical Engineering Department, Vocational College Universitas Gadjah Mada Yogyakarta Indonesia
| | - Saiful Amri Mazlan
- Malaysia‐Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur Malaysia
| | - Rizuan Norhaniza
- Malaysia‐Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur Malaysia
| | - Shin‐Ichiroh Yamamoto
- Department of Bio‐Science and Engineering Shibaura Institute of Technology Saitama Japan
| | - Nur Azmah Nordin
- Malaysia‐Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur Malaysia
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32
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Kranthiraja K, Saeki A. Machine Learning-Assisted Polymer Design for Improving the Performance of Non-Fullerene Organic Solar Cells. ACS APPLIED MATERIALS & INTERFACES 2022; 14:28936-28944. [PMID: 35696604 DOI: 10.1021/acsami.2c06077] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Despite the progress in machine learning (ML) in terms of prediction of power conversion efficiency (PCE) in organic photovoltaics (OPV), the effectiveness of ML in practical applications is still lacking owing to the complex structure-property relationship. Therefore, verifying the potential of ML through experiments can amplify the use of ML models. Herein, we developed a new series of π-conjugated polymers comprising benzodithiophene and thiazolothiazole with fluorination and alkylthio chains (PBDTTzBO, PFSBDTTzBO, and PFBDTTzBO) for non-fullerene (NF) acceptors based on our random-forest ML model for OPVs. Notably, the order of the ML-predicted PCEs of these polymers with IT-4F (9.93, 11.35, and 11.47%) was in good agreement with their experimental PCEs (5.24, 7.35, and 10.30%). In contrast, an inverse correlation was observed between the predicted (9.20, 12.29, and 12.20%) and experimental (11.98, 1.57, and 6.53%) PCEs with Y6. Both the findings are interpreted in terms of surface morphology, transient photoconductivity, charge carrier mobility, polymer orientation, and miscibility, quantified by the Flory-Huggins parameters. Herein, we present an ML-assisted polymer design for high-performance non-fullerene organic photovoltaics (NFOPVs) and elucidate the importance of the subtle alterations in the morphology of NFOPVs.
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Affiliation(s)
- Kakaraparthi Kranthiraja
- Department of Applied Chemistry, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Akinori Saeki
- Department of Applied Chemistry, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan
- Innovative Catalysis Science Division, Institute for Open and Transdisciplinary Research Initiatives (ICS-OTRI), Osaka University, 1-1 Yamadaoka, Suita, Osaka 565-0871, Japan
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33
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Zhao ZW, Del Cueto M, Troisi A. Limitations of machine learning models when predicting compounds with completely new chemistries: possible improvements applied to the discovery of new non-fullerene acceptors. DIGITAL DISCOVERY 2022; 1:266-276. [PMID: 35769202 PMCID: PMC9189862 DOI: 10.1039/d2dd00004k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 03/23/2022] [Indexed: 11/21/2022]
Abstract
We try to determine if machine learning (ML) methods, applied to the discovery of new materials on the basis of existing data sets, have the power to predict completely new classes of compounds (extrapolating) or perform well only when interpolating between known materials. We introduce the leave-one-group-out cross-validation, in which the ML model is trained to explicitly perform extrapolations of unseen chemical families. This approach can be used across materials science and chemistry problems to improve the added value of ML predictions, instead of using extrapolative ML models that were trained with a regular cross-validation. We consider as a case study the problem of the discovery of non-fullerene acceptors because novel classes of acceptors are naturally classified into distinct chemical families. We show that conventional ML methods are not useful in practice when attempting to predict the efficiency of a completely novel class of materials. The approach proposed in this work increases the accuracy of the predictions to enable at least the categorization of materials with a performance above and below the median value.
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Affiliation(s)
- Zhi-Wen Zhao
- Department of Chemistry, University of Liverpool Liverpool L69 3BX UK
- Institute of Functional Material Chemistry, Faculty of Chemistry, Northeast Normal University Changchun 130024 Jilin P. R. China
| | - Marcos Del Cueto
- Department of Chemistry, University of Liverpool Liverpool L69 3BX UK
| | - Alessandro Troisi
- Department of Chemistry, University of Liverpool Liverpool L69 3BX UK
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34
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Ikemoto K, Akiyoshi M, Mio T, Nishioka K, Sato S, Isobe H. Synthesis of a Negatively Curved Nanocarbon Molecule with an Octagonal Omphalos via Design‐of‐Experiments Optimizations Supplemented by Machine Learning. Angew Chem Int Ed Engl 2022. [DOI: 10.1002/ange.202204035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Koki Ikemoto
- Department of Chemistry The University of Tokyo Hongo 7-3-1, Bunkyo-ku Tokyo 113-0033 Japan
| | - Misato Akiyoshi
- Department of Chemistry The University of Tokyo Hongo 7-3-1, Bunkyo-ku Tokyo 113-0033 Japan
| | - Tatsuru Mio
- Department of Chemistry The University of Tokyo Hongo 7-3-1, Bunkyo-ku Tokyo 113-0033 Japan
| | - Kaito Nishioka
- Department of Chemistry The University of Tokyo Hongo 7-3-1, Bunkyo-ku Tokyo 113-0033 Japan
| | - Sota Sato
- Department of Chemistry The University of Tokyo Hongo 7-3-1, Bunkyo-ku Tokyo 113-0033 Japan
- Present address: Department of Applied Chemistry The University of Tokyo Hongo, Bunkyo-ku, Tokyo 113-8656 Japan
| | - Hiroyuki Isobe
- Department of Chemistry The University of Tokyo Hongo 7-3-1, Bunkyo-ku Tokyo 113-0033 Japan
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35
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Yang JC, Lee S, Ma BS, Kim J, Song M, Kim SY, Kim DW, Kim TS, Park S. Geometrically engineered rigid island array for stretchable electronics capable of withstanding various deformation modes. SCIENCE ADVANCES 2022; 8:eabn3863. [PMID: 35648853 PMCID: PMC9159573 DOI: 10.1126/sciadv.abn3863] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 04/14/2022] [Indexed: 06/12/2023]
Abstract
Integration of rigid components in soft polymer matrix is considered as the most feasible architecture to enable stretchable electronics. However, a method of suppressing cracks at the interface between soft and rigid materials due to excessive and repetitive deformations of various types remains a formidable challenge. Here, we geometrically engineered Ferris wheel-shaped islands (FWIs) capable of effectively suppressing crack propagation at the interface under various deformation modes (stretching, twisting, poking, and crumpling). The optimized FWIs have notable increased strain at failure and fatigue life compared with conventional circle- and square-shaped islands. Stretchable electronics composed of various rigid components (LED and coin cell) were demonstrated using intrinsically stretchable printed electrodes. Furthermore, electronic skin capable of differentiating various tactile stimuli without interference was demonstrated. Our method enables stretchable electronics that can be used under various geometrical forms with notable enhanced durability, enabling stretchable electronics to withstand potentially harsh conditions of everyday usage.
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Affiliation(s)
- Jun Chang Yang
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Seungkyu Lee
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Boo Soo Ma
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Junmo Kim
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Myoung Song
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Su Yeong Kim
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Da Won Kim
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Taek-Soo Kim
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Steve Park
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
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36
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Ting JYC, Barnard AS. Data-driven causal inference of process-structure relationships in nanocatalysis. Curr Opin Chem Eng 2022. [DOI: 10.1016/j.coche.2022.100818] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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37
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MacQueen B, Jayarathna R, Lauterbach J. Knowledge extraction in catalysis utilizing design of experiments and machine learning. Curr Opin Chem Eng 2022. [DOI: 10.1016/j.coche.2021.100781] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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38
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Glazar JT, Shenoy VB. Data-driven design of soft sensors. NAT MACH INTELL 2022. [DOI: 10.1038/s42256-022-00453-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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39
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Development of a fibrin-mediated gene delivery system for the treatment of cystinosis via design of experiment. Sci Rep 2022; 12:3752. [PMID: 35260693 PMCID: PMC8904479 DOI: 10.1038/s41598-022-07750-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 02/11/2022] [Indexed: 11/23/2022] Open
Abstract
Cystinosis is a rare disease, caused by a mutation in the gene cystinosin and characterised by the accumulation of cystine crystals. Advantages of biomaterial-mediated gene delivery include reduced safety concerns and the possibility to cure organs that are difficult to treat using systemic gene transfer methods. This study developed novel fibrin hydrogels for controlled, localised gene delivery, for the treatment of cystinosis. In the first part, fabrication parameters (i.e., DNA, thrombin, and aprotinin concentrations) were optimised, using a Design of Experiment (DOE) methodology. DOE is a statistical engineering approach to process optimisation, which increases experimental efficiency, reduces the number of experiments, takes into consideration interactions between different parameters, and allows the creation of predictive models. This study demonstrated the utility of DOE to the development of gene delivery constructs. In the second part of the study, primary fibroblasts from a patient with cystinosis were seeded on the biomaterials. Seeded cells expressed the recombinant CTNS and showed a decrease in cystine content. Furthermore, conditioned media contained functional copies of the recombinant CTNS. These were taken up by monolayer cultures of non-transfected cells. This study described a methodology to develop gene delivery constructs by using a DOE approach and ultimately provided new insights into the treatment of cystinosis.
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Mayr F, Harth M, Kouroudis I, Rinderle M, Gagliardi A. Machine Learning and Optoelectronic Materials Discovery: A Growing Synergy. J Phys Chem Lett 2022; 13:1940-1951. [PMID: 35188778 DOI: 10.1021/acs.jpclett.1c04223] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Novel optoelectronic materials have the potential to revolutionize the ongoing green transition by both providing more efficient photovoltaic (PV) devices and lowering energy consumption of devices like LEDs and sensors. The lead candidate materials for these applications are both organic semiconductors and more recently perovskites. This Perspective illustrates how novel machine learning techniques can help explore these materials, from speeding up ab initio calculations toward experimental guidance. Furthermore, based on existing work, perspectives around machine-learned molecular dynamics potentials, physically informed neural networks, and generative methods are outlined.
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Affiliation(s)
- Felix Mayr
- Department of Electrical and Computer Engineering, Technical University of Munich, Hans-Piloty-Straße 1, 85748 Garching bei München, Germany
| | - Milan Harth
- Department of Electrical and Computer Engineering, Technical University of Munich, Hans-Piloty-Straße 1, 85748 Garching bei München, Germany
| | - Ioannis Kouroudis
- Department of Electrical and Computer Engineering, Technical University of Munich, Hans-Piloty-Straße 1, 85748 Garching bei München, Germany
| | - Michael Rinderle
- Department of Electrical and Computer Engineering, Technical University of Munich, Hans-Piloty-Straße 1, 85748 Garching bei München, Germany
| | - Alessio Gagliardi
- Department of Electrical and Computer Engineering, Technical University of Munich, Hans-Piloty-Straße 1, 85748 Garching bei München, Germany
- Munich Data Science Institute, Technical University of Munich, Walther-von-Dyck-Straße 10, 85748 Garching bei München, Germany
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Kadulkar S, Sherman ZM, Ganesan V, Truskett TM. Machine Learning-Assisted Design of Material Properties. Annu Rev Chem Biomol Eng 2022; 13:235-254. [PMID: 35300515 DOI: 10.1146/annurev-chembioeng-092220-024340] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Designing functional materials requires a deep search through multidimensional spaces for system parameters that yield desirable material properties. For cases where conventional parameter sweeps or trial-and-error sampling are impractical, inverse methods that frame design as a constrained optimization problem present an attractive alternative. However, even efficient algorithms require time- and resource-intensive characterization of material properties many times during optimization, imposing a design bottleneck. Approaches that incorporate machine learning can help address this limitation and accelerate the discovery of materials with targeted properties. In this article, we review how to leverage machine learning to reduce dimensionality in order to effectively explore design space, accelerate property evaluation, and generate unconventional material structures with optimal properties. We also discuss promising future directions, including integration of machine learning into multiple stages of a design algorithm and interpretation of machine learning models to understand how design parameters relate to material properties. Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering, Volume 13 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Sanket Kadulkar
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas, USA;
| | - Zachary M Sherman
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas, USA;
| | - Venkat Ganesan
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas, USA;
| | - Thomas M Truskett
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas, USA; .,Department of Physics, University of Texas at Austin, Austin, Texas, USA
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Xu X, Aggarwal D, Shankar K. Instantaneous Property Prediction and Inverse Design of Plasmonic Nanostructures Using Machine Learning: Current Applications and Future Directions. NANOMATERIALS (BASEL, SWITZERLAND) 2022; 12:633. [PMID: 35214962 PMCID: PMC8874423 DOI: 10.3390/nano12040633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 02/06/2022] [Accepted: 02/08/2022] [Indexed: 02/06/2023]
Abstract
Advances in plasmonic materials and devices have given rise to a variety of applications in photocatalysis, microscopy, nanophotonics, and metastructures. With the advent of computing power and artificial neural networks, the characterization and design process of plasmonic nanostructures can be significantly accelerated using machine learning as opposed to conventional FDTD simulations. The machine learning (ML) based methods can not only perform with high accuracy and return optical spectra and optimal design parameters, but also maintain a stable high computing efficiency without being affected by the structural complexity. This work reviews the prominent ML methods involved in forward simulation and inverse design of plasmonic nanomaterials, such as Convolutional Neural Networks, Generative Adversarial Networks, Genetic Algorithms and Encoder-Decoder Networks. Moreover, we acknowledge the current limitations of ML methods in the context of plasmonics and provide perspectives on future research directions.
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Affiliation(s)
| | | | - Karthik Shankar
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada; (X.X.); (D.A.)
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Kim SY, Lee JC, Seo G, Woo JH, Lee M, Nam J, Sim JY, Kim HR, Park EC, Park S. Computational Method-Based Optimization of Carbon Nanotube Thin-Film Immunosensor for Rapid Detection of SARS-CoV-2 Virus. SMALL SCIENCE 2022; 2:2100111. [PMID: 34901932 PMCID: PMC8646396 DOI: 10.1002/smsc.202100111] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Indexed: 12/17/2022] Open
Abstract
The recent global spread of COVID-19 stresses the importance of developing diagnostic testing that is rapid and does not require specialized laboratories. In this regard, nanomaterial thin-film-based immunosensors fabricated via solution processing are promising, potentially due to their mass manufacturability, on-site detection, and high sensitivity that enable direct detection of virus without the need for molecular amplification. However, thus far, thin-film-based biosensors have been fabricated without properly analyzing how the thin-film properties are correlated with the biosensor performance, limiting the understanding of property-performance relationships and the optimization process. Herein, the correlations between various thin-film properties and the sensitivity of carbon nanotube thin-film-based immunosensors are systematically analyzed, through which optimal sensitivity is attained. Sensitivities toward SARS-CoV-2 nucleocapsid protein in buffer solution and in the lysed virus are 0.024 [fg/mL]-1 and 0.048 [copies/mL]-1, respectively, which are sufficient for diagnosing patients in the early stages of COVID-19. The technique, therefore, can potentially elucidate complex relationships between properties and performance of biosensors, thereby enabling systematic optimization to further advance the applicability of biosensors for accurate and rapid point-of-care (POC) diagnosis.
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Affiliation(s)
- Su Yeong Kim
- Organic and nano electronics laboratoryKI for Health Science and TechnologyDepartment of Materials Science and EngineeringKorea Advanced Institute of Science and Technology (KAIST)Daejeon34141Republic of Korea
| | - Jeong-Chan Lee
- Organic and nano electronics laboratoryKI for Health Science and TechnologyDepartment of Materials Science and EngineeringKorea Advanced Institute of Science and Technology (KAIST)Daejeon34141Republic of Korea
| | - Giwan Seo
- Research Center for Bioconvergence AnalysisKorea Basic Science InstituteCheongju28119Republic of Korea
- Center for Convergent Research of Emerging Virus InfectionKorea Research Institute of Chemical TechnologyDaejeon34114Republic of Korea
| | - Jun Hee Woo
- Organic and nano electronics laboratoryKI for Health Science and TechnologyDepartment of Materials Science and EngineeringKorea Advanced Institute of Science and Technology (KAIST)Daejeon34141Republic of Korea
| | - Minho Lee
- School of Chemical and Biological Engineering and Institute of Chemical ProcessSeoul National UniversitySeoul08826Republic of Korea
| | - Jaewook Nam
- School of Chemical and Biological Engineering and Institute of Chemical ProcessSeoul National UniversitySeoul08826Republic of Korea
| | - Joo Yong Sim
- Department of Mechanical Systems EngineeringSookmyung Women's UniversitySeoul04310Republic of Korea
| | - Hyung-Ryong Kim
- Department of PharmacologyCollege of DentistryJeonbuk National UniversityJeonju54896Republic of Korea
| | - Edmond Changkyun Park
- Research Center for Bioconvergence AnalysisKorea Basic Science InstituteCheongju28119Republic of Korea
- Center for Convergent Research of Emerging Virus InfectionKorea Research Institute of Chemical TechnologyDaejeon34114Republic of Korea
| | - Steve Park
- Organic and nano electronics laboratoryKI for Health Science and TechnologyDepartment of Materials Science and EngineeringKorea Advanced Institute of Science and Technology (KAIST)Daejeon34141Republic of Korea
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Lee JC, Woo JH, Lee HJ, Lee M, Woo H, Baek S, Nam J, Sim JY, Park S. Microfluidic Screening-Assisted Machine Learning to Investigate Vertical Phase Separation of Small Molecule:Polymer Blend. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2107596. [PMID: 34865268 DOI: 10.1002/adma.202107596] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 11/24/2021] [Indexed: 06/13/2023]
Abstract
Solution-based thin-film processing is a widely utilized technique for the fabrication of various devices. In particular, the tunability of the ink composition and coating condition allows precise control of thin-film properties and device performance. Despite the advantage of having such tunability, the sheer number of possible combinations of experimental parameters render it infeasible to efficiently optimize device performance and analyze the correlation between experimental parameters and device performance. In this work, a microfluidic screening-embedded thin-film processing technique is developed, through which thin-films of varying ratios of small molecule semiconductor:polymer blend are simultaneously generated and screened in a time- and resource-efficient manner. Moreover, utilizing the thin-films of varying combinations of experimental parameters, machine learning models are trained to predict the transistor performance. Gaussian Process Regression (GPR) algorithms tuned by Bayesian optimization shows the best predictive accuracy amongst the trained models, which enables narrowing down of the combinations of experimental parameters and investigation of the degree of vertical phase separation under the predicted parameter space. The technique can serve as a guideline for elucidating the underlying complex parameter-property-performance correlations in solution-based thin-film processing, thereby accelerating the optimization of various thin-film devices in the future.
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Affiliation(s)
- Jeong-Chan Lee
- Organic and nano electronics laboratory, Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Seoul, 34141, Republic of Korea
| | - Jun Hee Woo
- Organic and nano electronics laboratory, Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Seoul, 34141, Republic of Korea
| | - Ho-Jun Lee
- Organic and nano electronics laboratory, Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Seoul, 34141, Republic of Korea
| | - Minho Lee
- School of Chemical and Biological Engineering and Institute of Chemical Process, Seoul National University, Seoul, 08826, Republic of Korea
| | - Heejin Woo
- Organic and nano electronics laboratory, Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Seoul, 34141, Republic of Korea
| | - Seunghyeok Baek
- Organic and nano electronics laboratory, Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Seoul, 34141, Republic of Korea
| | - Jaewook Nam
- School of Chemical and Biological Engineering and Institute of Chemical Process, Seoul National University, Seoul, 08826, Republic of Korea
| | - Joo Yong Sim
- Department of Mechanical Systems Engineering, Sookmyung Women's University, Seoul, 04310, Republic of Korea
| | - Steve Park
- KI for Health Science and Technology, Saudi Aramco-KAIST CO2 Management Center, Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Seoul, 34141, Republic of Korea
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45
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Automatic strain sensor design via active learning and data augmentation for soft machines. NAT MACH INTELL 2022. [DOI: 10.1038/s42256-021-00434-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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Nakamura T, Honda M, Kimura Y, Amezawa K. High-temperature ionic logic gates composed of an ionic rectifying solid–electrolyte interface. RSC Adv 2022; 12:18501-18506. [PMID: 35799931 PMCID: PMC9219041 DOI: 10.1039/d2ra00710j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 06/17/2022] [Indexed: 11/21/2022] Open
Abstract
Direct data collection from extremely high temperature environments is vitally important for the progress of industrial technologies such as combustion-engines, turbines and furnaces for various purposes. However, present semiconductor-based information devices are not suitable for such high-temperature applications due to thermal excitation of electronic carriers. Herein, we demonstrate high-temperature ionic AND and OR logic gates composed of the oxide-ion-conducting yttria stabilized zirconia (YSZ) and the mixed oxide-ion and electron conducting La2NiO4+δ as an ultra-high temperature information device. The ionic AND and OR gates developed in this work exhibited proper and stable electrical responses at 1073 K. The ionic logic gates shown in this work are promising demonstrations for robust information devices in extreme environments. In this work, high-temperature ionic logic gates composed of ion rectifying YSZ/La2NiO4+δ junctions are demonstrated.![]()
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Affiliation(s)
- Takashi Nakamura
- Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, 2-1-1, Katahira, Aoba-Ku, Sendai, 980-8577, Japan
| | - Miri Honda
- Graduate School of Engineering, Tohoku University, 6-6-1 Aramaki-Aoba, Aoba-ku, Sendai, 980-8579, Japan
| | - Yuta Kimura
- Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, 2-1-1, Katahira, Aoba-Ku, Sendai, 980-8577, Japan
| | - Koji Amezawa
- Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, 2-1-1, Katahira, Aoba-Ku, Sendai, 980-8577, Japan
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Wan Y, Zeng Q, Shi P, Yoon YJ, Tay CY, Lee JM. Machine learning-assisted optimization of TBBPA-bis-(2,3-dibromopropyl ether) extraction process from ABS polymer. CHEMOSPHERE 2022; 287:132128. [PMID: 34509015 DOI: 10.1016/j.chemosphere.2021.132128] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 08/30/2021] [Accepted: 08/31/2021] [Indexed: 06/13/2023]
Abstract
The increasing amount of e-waste plastics needs to be disposed of properly, and removing the brominated flame retardants contained in them can effectively reduce their negative impact on the environment. In the present work, TBBPA-bis-(2,3-dibromopropyl ether) (TBBPA-DBP), a novel brominated flame retardant, was extracted by ultrasonic-assisted solvothermal extraction process. Response Surface Methodology (RSM) achieved by machine learning (support vector regression, SVR) was employed to estimate the optimum extraction conditions (extraction time, extraction temperature, liquid to solid ratio) in methanol or ethanol solvent. The predicted optimum conditions of TBBPA-DBP were 96 min, 131 mL g-1, 65 °C, in MeOH, and 120 min, 152 mL g-1, 67 °C in EtOH. And the validity of predicted conditions was verified.
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Affiliation(s)
- Yan Wan
- Energy Research Institute, Nangyang Technological University, 1 Cleantech Loop, 637141, Singapore; School of Chemical and Biomedical Engineering, Nanyang Technological University, 62 Nanyang Drive, 637459, Singapore
| | - Qiang Zeng
- Energy Research Institute, Nangyang Technological University, 1 Cleantech Loop, 637141, Singapore; School of Chemical and Biomedical Engineering, Nanyang Technological University, 62 Nanyang Drive, 637459, Singapore
| | - Pujiang Shi
- Energy Research Institute, Nangyang Technological University, 1 Cleantech Loop, 637141, Singapore
| | - Yong-Jin Yoon
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
| | - Chor Yong Tay
- Energy Research Institute, Nangyang Technological University, 1 Cleantech Loop, 637141, Singapore; School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore; School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore, 637551, Singapore
| | - Jong-Min Lee
- Energy Research Institute, Nangyang Technological University, 1 Cleantech Loop, 637141, Singapore; School of Chemical and Biomedical Engineering, Nanyang Technological University, 62 Nanyang Drive, 637459, Singapore.
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Miyake Y, Saeki A. Machine Learning-Assisted Development of Organic Solar Cell Materials: Issues, Analyses, and Outlooks. J Phys Chem Lett 2021; 12:12391-12401. [PMID: 34939806 DOI: 10.1021/acs.jpclett.1c03526] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Nonfullerene, a small molecular electron acceptor, has substantially improved the power conversion efficiency of organic photovoltaics (OPVs). However, the large structural freedom of π-conjugated polymers and molecules makes it difficult to explore with limited resources. Machine learning, which is based on rapidly growing artificial intelligence technology, is a high-throughput method to accelerate the speed of material design and process optimization; however, it suffers from limitations in terms of prediction accuracy, interpretability, data collection, and available data (particularly, experimental data). This recognition motivates the present Perspective, which focuses on utilizing the experimental data set for ML to efficiently aid OPV research. This Perspective discusses the trends in ML-OPV publications, the NFA category, and the effects of data size and explanatory variables (fingerprints or Mordred descriptors) on the prediction accuracy and explainability, which broadens the scope of ML and would be useful for the development of next-generation solar cell materials.
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Affiliation(s)
- Yuta Miyake
- Department of Applied Chemistry, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Akinori Saeki
- Department of Applied Chemistry, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan
- Innovative Catalysis Science Division, Institute for Open and Transdisciplinary Research Initiatives (ICS-OTRI), Osaka University, 1-1 Yamadaoka, Suita, Osaka 565-0871, Japan
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Sani S, Adnan R, Oh WD, Iqbal A. Comparison of the Surface Properties of Hydrothermally Synthesised Fe 3O 4@C Nanocomposites at Variable Reaction Times. NANOMATERIALS (BASEL, SWITZERLAND) 2021; 11:2742. [PMID: 34685183 PMCID: PMC8537566 DOI: 10.3390/nano11102742] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 10/06/2021] [Accepted: 10/13/2021] [Indexed: 12/17/2022]
Abstract
The influence of variable reaction time (tr) on surface/textural properties (surface area, total pore volume, and pore diameter) of carbon-encapsulated magnetite (Fe3O4@C) nanocomposites fabricated by a hydrothermal process at 190 °C for 3, 4, and 5 h was studied. The properties were calculated using the Brunauer-Emmett-Teller (BET) isotherms data. The nanocomposites were characterised using Fourier transform infrared spectroscopy, X-ray diffraction analysis, thermogravimetry, and scanning and transmission electron microscopies. Analysis of variance shows tr has the largest effect on pore volume (F value = 1117.6, p value < 0.0001), followed by the surface area (F value = 54.8, p value < 0.0001) and pore diameter (F value = 10.4, p value < 0.001) with R2-adjusted values of 99.5%, 88.5% and 63.1%, respectively. Tukey and Fisher tests confirmed tr rise to have caused increased variations in mean particle sizes (11-91 nm), crystallite sizes (5-21 nm), pore diameters (9-16 nm), pore volume (0.017-0.089 cm3 g-1) and surface area (7.6-22.4 m2 g-1) of the nanocomposites with individual and simultaneous confidence limits of 97.9 and 84.4 (p-adj < 0.05). The nanocomposites' retained Fe-O vibrations at octahedral (436 cm-1) and tetrahedral (570 cm-1) cubic ferrite sites, modest thermal stability (37-60 % weight loss), and large volume-specific surface area with potential for catalytic application in advanced oxidation processes.
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Affiliation(s)
- Sadiq Sani
- School of Chemical Sciences, Universiti Sains Malaysia (USM), Penang 11800, Malaysia; (S.S.); (W.-D.O.); (A.I.)
- Department of Applied Chemistry, Federal University Dutsin-Ma, Dutsinma P.M.B. 5001, Nigeria
| | - Rohana Adnan
- School of Chemical Sciences, Universiti Sains Malaysia (USM), Penang 11800, Malaysia; (S.S.); (W.-D.O.); (A.I.)
| | - Wen-Da Oh
- School of Chemical Sciences, Universiti Sains Malaysia (USM), Penang 11800, Malaysia; (S.S.); (W.-D.O.); (A.I.)
| | - Anwar Iqbal
- School of Chemical Sciences, Universiti Sains Malaysia (USM), Penang 11800, Malaysia; (S.S.); (W.-D.O.); (A.I.)
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