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Yahaya N, Mohamed AH, Sajid M, Zain NNM, Liao PC, Chew KW. Deep eutectic solvents as sustainable extraction media for extraction of polysaccharides from natural sources: Status, challenges and prospects. Carbohydr Polym 2024; 338:122199. [PMID: 38763725 DOI: 10.1016/j.carbpol.2024.122199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 04/18/2024] [Accepted: 04/21/2024] [Indexed: 05/21/2024]
Abstract
Deep eutectic solvents (DES) emerge as promising alternatives to conventional solvents, offering outstanding extraction capabilities, low toxicity, eco-friendliness, straightforward synthesis procedures, broad applicability, and impressive recyclability. DES are synthesized by combining two or more components through various synthesis procedures, such as heat-assisted mixing/stirring, grinding, freeze drying, and evaporation. Polysaccharides, as abundant natural materials, are highly valued for their biocompatibility, biodegradability, and sustainability. These versatile biopolymers can be derived from various natural sources such as plants, algae, animals, or microorganisms using diverse extraction techniques. This review explores the synthesis procedures of DES, their physicochemical properties, characterization analysis, and their application in polysaccharide extraction. The extraction optimization strategies, parameters affecting DES-based polysaccharide extraction, and separation mechanisms are comprehensively discussed. Additionally, this review provides insights into recently developed molecular guides for DES screening and the utilization of artificial neural networks for optimizing DES-based extraction processes. DES serve as excellent extraction media for polysaccharides from different sources, preserving their functional features. They are utilized both as extraction solvents and as supporting media to enhance the extraction abilities of other solvents. Continued research aims to improve DES-based extraction methods and achieve selective, energy-efficient processes to meet the demands of this expanding field.
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Affiliation(s)
- Noorfatimah Yahaya
- Department of Toxicology, Advanced Medical and Dental Institute (AMDI), Universiti Sains Malaysia, 13200, Bertam Kepala Batas, Penang, Malaysia.
| | - Ahmad Husaini Mohamed
- School of Chemistry and Environment, Faculty of Applied Sciences, Universiti Teknologi MARA, Cawangan Negeri Sembilan, Kampus Kuala Pilah, 72000, Kuala Pilah, Negeri Sembilan, Malaysia.
| | - Muhammad Sajid
- Applied Research Center for Environment and Marine Studies, Research Institute, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
| | - Nur Nadhirah Mohamad Zain
- Department of Toxicology, Advanced Medical and Dental Institute (AMDI), Universiti Sains Malaysia, 13200, Bertam Kepala Batas, Penang, Malaysia
| | - Pao-Chi Liao
- Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan, 704, Taiwan
| | - Kit Wayne Chew
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, 637459, Singapore
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Ayres LB, Gomez FJV, Silva MF, Linton JR, Garcia CD. Predicting the formation of NADES using a transformer-based model. Sci Rep 2024; 14:2715. [PMID: 38388549 PMCID: PMC10883925 DOI: 10.1038/s41598-022-27106-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 12/26/2022] [Indexed: 02/24/2024] Open
Abstract
The application of natural deep eutectic solvents (NADES) in the pharmaceutical, agricultural, and food industries represents one of the fastest growing fields of green chemistry, as these mixtures can potentially replace traditional organic solvents. These advances are, however, limited by the development of new NADES which is today, almost exclusively empirically driven and often derivative from known mixtures. To overcome this limitation, we propose the use of a transformer-based machine learning approach. Here, the transformer-based neural network model was first pre-trained to recognize chemical patterns from SMILES representations (unlabeled general chemical data) and then fine-tuned to recognize the patterns in strings that lead to the formation of either stable NADES or simple mixtures of compounds not leading to the formation of stable NADES (binary classification). Because this strategy was adapted from language learning, it allows the use of relatively small datasets and relatively low computational resources. The resulting algorithm is capable of predicting the formation of multiple new stable eutectic mixtures (n = 337) from a general database of natural compounds. More importantly, the system is also able to predict the components and molar ratios needed to render NADES with new molecules (not present in the training database), an aspect that was validated using previously reported NADES as well as by developing multiple novel solvents containing ibuprofen. We believe this strategy has the potential to transform the screening process for NADES as well as the pharmaceutical industry, streamlining the use of bioactive compounds as functional components of liquid formulations, rather than simple solutes.
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Affiliation(s)
- Lucas B Ayres
- Department of Chemistry, Clemson University, 211 S. Palmetto Blvd, Clemson, SC, 29634, USA
| | - Federico J V Gomez
- Facultad de Ciencias Agrarias, Instituto de Biología Agrícola de Mendoza (IBAM-CONICET), Universidad Nacional de Cuyo, Mendoza, Argentina
| | - Maria Fernanda Silva
- Facultad de Ciencias Agrarias, Instituto de Biología Agrícola de Mendoza (IBAM-CONICET), Universidad Nacional de Cuyo, Mendoza, Argentina
| | - Jeb R Linton
- Department of Chemistry, Clemson University, 211 S. Palmetto Blvd, Clemson, SC, 29634, USA
- IBM Cloud, Armonk, NY, 10504, USA
| | - Carlos D Garcia
- Department of Chemistry, Clemson University, 211 S. Palmetto Blvd, Clemson, SC, 29634, USA.
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Fajar ATN, Hanada T, Hartono AD, Goto M. Estimating the phase diagrams of deep eutectic solvents within an extensive chemical space. Commun Chem 2024; 7:27. [PMID: 38347186 PMCID: PMC10861527 DOI: 10.1038/s42004-024-01116-3] [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: 09/26/2023] [Accepted: 01/30/2024] [Indexed: 02/15/2024] Open
Abstract
Assessing the formation of a deep eutectic solvent (DES) necessitates a solid-liquid equilibrium phase diagram. Yet, many studies focusing on DES applications do not include this diagram because of challenges in measurement, leading to misidentified eutectic points. The present study provides a practical approach for estimating the phase diagram of any binary mixture from the structural information, utilizing machine learning and quantum chemical techniques. The selected machine learning model provides reasonably high accuracy in predicting melting point (R2 = 0.84; RMSE = 40.53 K) and fusion enthalpy (R2 = 0.84; RMSE = 4.96 kJ mol-1) of pure compounds upon evaluation by test data. By pinpointing the eutectic point coordinates within an extensive chemical space, we highlighted the impact of the mole fractions and melting properties on the eutectic temperatures. Molecular dynamics simulations of selected mixtures at the eutectic points emphasized the pivotal role of hydrogen bonds in dictating mixture behavior.
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Affiliation(s)
- Adroit T N Fajar
- Department of Applied Chemistry, Graduate School of Engineering, Kyushu University, 744 Motooka, Fukuoka, 819-0395, Japan
- Center for Energy Systems Design (CESD), International Institute for Carbon-Neutral Energy Research (WPI-I2CNER), Kyushu University, 744 Motooka, Fukuoka, 819-0395, Japan
| | - Takafumi Hanada
- Department of Applied Chemistry, Graduate School of Technology, Industrial and Social Science, Tokushima University, 2-1 Minamijosanjima, Tokushima, 770-8506, Japan
| | - Aditya D Hartono
- Mathematical Modeling Laboratory, Department of Agro-environmental Sciences, Faculty of Agriculture, Kyushu University, 744 Motooka, Fukuoka, 819-0395, Japan
| | - Masahiro Goto
- Department of Applied Chemistry, Graduate School of Engineering, Kyushu University, 744 Motooka, Fukuoka, 819-0395, Japan.
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Dowlatabadi M, Azizi S, Dehbashi M, Sadeqi H. A novel neural-evolutionary framework for predicting weight on the bit in drilling operations. Sci Rep 2023; 13:18539. [PMID: 37898632 PMCID: PMC10613297 DOI: 10.1038/s41598-023-45760-6] [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: 07/14/2023] [Accepted: 10/23/2023] [Indexed: 10/30/2023] Open
Abstract
This study compares the performance of artificial neural networks (ANN) trained by grey wolf optimization (GWO), biogeography-based optimization (BBO), and Levenberg-Marquardt (LM) to estimate the weight on bit (WOB). To this end, a dataset consisting of drilling depth, drill string rotational speed, rate of penetration, and volumetric flow rate as input variables and the WOB as a response is used to develop and validate the intelligent tools. The relevance test is applied to sort the strength of WOB dependency on the considered features. It was observed that the WOB has the highest linear correlation with the drilling depth and drill string rotational speed. After dividing the databank into the training and testing (4:1) parts, the proposed LM-ANN, GWO-ANN, and BBO-ANN ensembles are constructed. A sensitivity analysis is then carried out to find the most powerful structure of the models. Each model performs to reveal the relationship between the WOB and the mentioned independent factors. The performance of the models is finally evaluated by mean square error (MSE) and mean absolute error criteria. The results showed that both GWO and BBO algorithms effectively help the ANN to achieve a more accurate prediction of the WOB. Accordingly, the training MSEs decreased by 14.62% and 24.90%, respectively, by applying the GWO and BBO evolutionary algorithms. Meanwhile, these values were obtained as around 9.86% and 9.41% for the prediction error of the ANN in the testing phase. It was also deduced that the BBO performs more efficiently than the other technique. The effect of input variables dimension on the accuracy and training time of the BBO-ANN clarified that the most accurate WOB predictions are achieved when the model constructs with all four input variables instead of utilizing either three or two of them with the highest linear correlation. It was also observed that the training stage of the BBO-ANN model with four input variables needs a little more computational time than its training with either two or three variables. Finally, the accuracy of the BBO-ANN model for the WOB prediction has been compared with the multiple linear regression, support vector regression, adaptive neuro-fuzzy inference systems, and group method of data handling. The statistical accuracy analysis confirmed that the BBO-ANN is more accurate than the other checked techniques.
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Affiliation(s)
- Masrour Dowlatabadi
- Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
| | - Saeed Azizi
- Chemical Engineering, Oklahoma State University, Stillwater, OK, USA
| | - Mohsen Dehbashi
- Institute of Physics, Center for Science and Education, Silesian University of Technology, Konarskiego 22B, 44-100, Gliwice, Poland.
| | - Hamed Sadeqi
- Information Technology Department, Informatic University of Applied Sciences, Tehran, Iran
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Wysokowski M, Luu RK, Arevalo S, Khare E, Stachowiak W, Niemczak M, Jesionowski T, Buehler MJ. Untapped Potential of Deep Eutectic Solvents for the Synthesis of Bioinspired Inorganic-Organic Materials. CHEMISTRY OF MATERIALS : A PUBLICATION OF THE AMERICAN CHEMICAL SOCIETY 2023; 35:7878-7903. [PMID: 37840775 PMCID: PMC10568971 DOI: 10.1021/acs.chemmater.3c00847] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 08/02/2023] [Indexed: 10/17/2023]
Abstract
Since the discovery of deep eutectic solvents (DESs) in 2003, significant progress has been made in the field, specifically advancing aspects of their preparation and physicochemical characterization. Their low-cost and unique tailored properties are reasons for their growing importance as a sustainable medium for the resource-efficient processing and synthesis of advanced materials. In this paper, the significance of these designer solvents and their beneficial features, in particular with respect to biomimetic materials chemistry, is discussed. Finally, this article explores the unrealized potential and advantageous aspects of DESs, focusing on the development of biomineralization-inspired hybrid materials. It is anticipated that this article can stimulate new concepts and advances providing a reference for breaking down the multidisciplinary borders in the field of bioinspired materials chemistry, especially at the nexus of computation and experiment, and to develop a rigorous materials-by-design paradigm.
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Affiliation(s)
- Marcin Wysokowski
- Institute
of Chemical Technology, Faculty of Chemical Technology, Poznan University of Technology, Berdychowo 4, 60965 Poznan, Poland
- Laboratory
for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139, United States
| | - Rachel K. Luu
- Laboratory
for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139, United States
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139, United States
| | - Sofia Arevalo
- Laboratory
for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139, United States
| | - Eesha Khare
- Laboratory
for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139, United States
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139, United States
| | - Witold Stachowiak
- Institute
of Chemical Technology, Faculty of Chemical Technology, Poznan University of Technology, Berdychowo 4, 60965 Poznan, Poland
| | - Michał Niemczak
- Institute
of Chemical Technology, Faculty of Chemical Technology, Poznan University of Technology, Berdychowo 4, 60965 Poznan, Poland
| | - Teofil Jesionowski
- Institute
of Chemical Technology, Faculty of Chemical Technology, Poznan University of Technology, Berdychowo 4, 60965 Poznan, Poland
| | - Markus J. Buehler
- Laboratory
for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139, United States
- Center
for Computational Science and Engineering, Schwarzman College of Computing, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139, United States
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Abdollahi SA, Ranjbar SF, Razeghi Jahromi D. Applying feature selection and machine learning techniques to estimate the biomass higher heating value. Sci Rep 2023; 13:16093. [PMID: 37752284 PMCID: PMC10522575 DOI: 10.1038/s41598-023-43496-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 09/25/2023] [Indexed: 09/28/2023] Open
Abstract
The biomass higher heating value (HHV) is an important thermal property that determines the amount of recoverable energy from agriculture byproducts. Precise laboratory measurement or accurate prediction of the HHV is essential for designing biomass conversion equipment. The current study combines feature selection scenarios and machine learning tools to establish a general model for estimating biomass HHV. Multiple linear regression and Pearson's correlation coefficients justified that volatile matter, nitrogen, and oxygen content of biomass samples have a slight effect on the HHV and it is better to ignore them during the HHV modeling. Then, the prediction performance of random forest, multilayer and cascade feedforward neural networks, group method of data handling, and least-squares support vector regressor are compared to determine the intelligent estimator with the highest accuracy toward biomass HHV prediction. The ranking test shows that the multilayer perceptron neural network better predicts the HHV of 532 biomass samples than the other intelligent models. This model presents the outstanding absolute average relative error of 2.75% and 3.12% and regression coefficients of 0.9500 and 0.9418 in the learning and testing stages. The model performance is also superior to a recurrent neural network which was recently developed in the literature using the same databank.
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Abdollahi SA, Ranjbar SF. Modeling the CO 2 separation capability of poly(4-methyl-1-pentane) membrane modified with different nanoparticles by artificial neural networks. Sci Rep 2023; 13:8812. [PMID: 37258709 PMCID: PMC10232494 DOI: 10.1038/s41598-023-36071-x] [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: 03/22/2023] [Accepted: 05/29/2023] [Indexed: 06/02/2023] Open
Abstract
Membranes are a potential technology to reduce energy consumption as well as environmental challenges considering the separation processes. A new class of this technology, namely mixed matrix membrane (MMM) can be fabricated by dispersing solid substances in a polymeric medium. In this way, the poly(4-methyl-1-pentene)-based MMMs have attracted great attention to capturing carbon dioxide (CO2), which is an environmental pollutant with a greenhouse effect. The CO2 permeability in different MMMs constituted of poly(4-methyl-1-pentene) (PMP) and nanoparticles was comprehensively analyzed from the experimental point of view. In addition, a straightforward mathematical model is necessary to compute the CO2 permeability before constructing the related PMP-based separation process. Hence, the current study employs multilayer perceptron artificial neural networks (MLP-ANN) to relate the CO2 permeability in PMP/nanoparticle MMMs to the membrane composition (additive type and dose) and pressure. Accordingly, the effect of these independent variables on CO2 permeability in PMP-based membranes is explored using multiple linear regression analysis. It was figured out that the CO2 permeability has a direct relationship with all independent variables, while the nanoparticle dose is the strongest one. The MLP-ANN structural features have efficiently demonstrated an appealing potential to achieve the highest accurate prediction for CO2 permeability. A two-layer MLP-ANN with the 3-8-1 topology trained by the Bayesian regulation algorithm is identified as the best model for the considered problem. This model simulates 112 experimentally measured CO2 permeability in PMP/ZnO, PMP/Al2O3, PMP/TiO2, and PMP/TiO2-NT with an excellent absolute average relative deviation (AARD) of lower than 5.5%, mean absolute error (MAE) of 6.87 and correlation coefficient (R) of higher than 0.99470. It was found that the mixed matrix membrane constituted of PMP and TiO2-NT (functionalized nanotube with titanium dioxide) is the best medium for CO2 separation.
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