Intelligent control of nanoparticle synthesis through machine learning

The influence of amphiphilic quaternary ammonium palmitoyl glycol chitosan (GCPQ) polymer composition on oil-loaded nanocapsule architecture

HYPOTHESIS

Predicting the exact nature of the self-assembly of amphiphilic molecules into supramolecular structures is of utmost importance for a variety of applications, but this is a challenge for nanotechnology. The amphiphilic drug delivery polymer-N-palmitoyl-N-monomethyl-N,N-dimethyl-N,N,N-trimethyl-6-O-glycolchitosan (GCPQ) self-assembles in aqueous media to form nanoparticles.

EXPERIMENT

This work aimed to develop a systematic predictive mathematical model on the eventual nature of oil-loaded GCPQ-nanoparticles and to determine the main independent variables that affect their nanoarchitecture following self-assembly. GCPQ polymers were produced with varying degree of palmitoylation (DP, 5.7-23.8 mol%), degree of quaternization (DQ, 7.2-22.7 mol%), and molecular weight (MW, 11.2-44.2 kDa) and their critical hydrophilic-lipophilic balance (cHLB) optimized to produce oil-loaded nanocapsules.

FINDINGS

Non-linear mathematical models (Particle size (nm) = 466.05 - 5.64DP - 6.52DQ + 0.13DQ2 - 0.03 MW2 - 14.48cHLB + 0.48cHLB2) were derived to predict the nanoparticle sizes (R2 = 0.998, R2adj = 0.995). Smaller nanoparticle sizes (148-157 nm) were obtained at high DP, DQ, and cHLB values, in which DP was the main independent variable responsible for nanoparticle size. Single or multiple-oil cores with small particles stabilizing polymer shells could be observed depending on the oil volume. Nanoparticle architectures, especially the nature of the oil-core(s), were driven by the DP, DQ, cHLB, and oil concentration. Here, we have developed a predictive model that may be applied to understand the nanoarchitecture of oil-loaded GCPQ-nanoparticles.

Determining the orderliness of carbon materials with nanoparticle imaging and explainable machine learning

Carbon materials have paramount importance in various fields of materials science, from electronic devices to industrial catalysts. The properties of these materials are strongly related to the distribution of defects-irregularities in electron density on their surfaces. Different materials have various distributions and quantities of these defects, which can be imaged using a procedure that involves depositing palladium nanoparticles. The resulting scanning electron microscopy (SEM) images can be characterized by a key descriptor-the ordering of nanoparticle positions. This work presents a highly interpretable machine learning approach for distinguishing between materials with ordered and disordered arrangements of defects marked by nanoparticle attachment. The influence of the degree of ordering was experimentally evaluated on the example of catalysis via chemical reactions involving carbon-carbon bond formation. This represents an important step toward automated analysis of SEM images in materials science.

Recent advances in the therapeutic applications of selenium nanoparticles

Selenium nanoparticles (SeNPs) are an appealing carrier for the targeted delivery. The selenium nanoparticles are gaining global attention because of the potential therapeutic applications in several diseases e.g., rheumatoid arthritis (RA), inflammatory bowel disease (IBD), asthma, liver, and various autoimmune disorders like psoriasis, cancer, diabetes, and a variety of infectious diseases. Despite the fact still there is no recent literature that summarises the therapeutic applications of SeNPs. There are some challenges that need to be addressed like finding targets for SeNPs in various diseases, and the various functionalization techniques utilized to increase SeNP's stability while facilitating wide drug-loaded SeNP distribution to tumor areas and preventing off-target impacts need to focus on understanding more about the therapeutic aspects for better understanding the science behind it. Keeping that in mind we have focused on this gap and try to summarize all recent key targeted therapies for SeNPs in cancer treatment and the numerous functionalization strategies. We have also focused on recent advancements in SeNP functionalization methodologies and mechanisms for biomedical applications, particularly in anticancer, anti-inflammatory, and anti-infection therapeutics. Based on our observation we found that SeNPs could potentially be useful in suppressing viral epidemics, like the ongoing COVID-19 pandemic, in complement to their antibacterial and antiparasitic uses. SeNPs are significant nanoplatforms with numerous desirable properties for clinical translation.

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating materials design and discovery, and reducing the need for time-consuming and labor-intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarized luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, an analysis of machine learning methods used for studying achiral nanomaterials is provided, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. An overview of chiral nanomaterials within the framework of synthesis-structure-property-application relationships is presented and insights on how to leverage machine learning for the study of these highly complex relationships are provided. Some key recent publications are reviewed and discussed on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field.

A review on toxicity mechanism and risk factors of nanoparticles in respiratory tract

This comprehensive review focuses on various dimensions of nanoparticle toxicity, emphasizing toxicological characteristics, assessment techniques, and examinations of relevant studies on the effects on biological systems. The primary objective is to comprehend the potential risks associated with nanoparticles and to provide efficient strategies for mitigating them by consolidating current research discoveries. For in-depth insights, the discussions extend to crucial aspects such as toxicity associated with different nanoparticles, human exposure, and nanoparticle deposition in the human respiratory tract. The analysis utilizes the multiple-path particle dosimetry (MPPD) modeling for computational simulation. The SiO2 nanoparticles with a volume concentration of 1% and a particle size of 50 nm are used to depict the MPPD modeling of the Left upper (LU), left lower (LL), right upper (RU), right middle (RM), and right lower (RL) lobes in the respiratory tract. The analysis revealed a substantial 67.5% decrease in the deposition fraction as the particle size increased from 10 nm to 100 nm. Graphical representation emphasizes the significant impact of exposure path selection on nanoparticle deposition, with distinct deposition values observed for nasal, oral, oronasal-mouth breather, oronasal - normal augmenter, and endotracheal paths (0.00291 μg, 0.00332 μg, 0.00297 μg, 0.00291 μg, and 0.00383 μg, respectively). Consistent with the focus of the review, the article also addresses crucial mitigation strategies for managing nanoparticle toxicity.

High-throughput microfluidic systems accelerated by artificial intelligence for biomedical applications

High-throughput microfluidic systems are widely used in biomedical fields for tasks like disease detection, drug testing, and material discovery. Despite the great advances in automation and throughput, the large amounts of data generated by the high-throughput microfluidic systems generally outpace the abilities of manual analysis. Recently, the convergence of microfluidic systems and artificial intelligence (AI) has been promising in solving the issue by significantly accelerating the process of data analysis as well as improving the capability of intelligent decision. This review offers a comprehensive introduction on AI methods and outlines the current advances of high-throughput microfluidic systems accelerated by AI, covering biomedical detection, drug screening, and automated system control and design. Furthermore, the challenges and opportunities in this field are critically discussed as well.

MatGPT: A Vane of Materials Informatics from Past, Present, to Future

Combining materials science, artificial intelligence (AI), physical chemistry, and other disciplines, materials informatics is continuously accelerating the vigorous development of new materials. The emergence of "GPT (Generative Pre-trained Transformer) AI" shows that the scientific research field has entered the era of intelligent civilization with "data" as the basic factor and "algorithm + computing power" as the core productivity. The continuous innovation of AI will impact the cognitive laws and scientific methods, and reconstruct the knowledge and wisdom system. This leads to think more about materials informatics. Here, a comprehensive discussion of AI models and materials infrastructures is provided, and the advances in the discovery and design of new materials are reviewed. With the rise of new research paradigms triggered by "AI for Science", the vane of materials informatics: "MatGPT", is proposed and the technical path planning from the aspects of data, descriptors, generative models, pretraining models, directed design models, collaborative training, experimental robots, as well as the efforts and preparations needed to develop a new generation of materials informatics, is carried out. Finally, the challenges and constraints faced by materials informatics are discussed, in order to achieve a more digital, intelligent, and automated construction of materials informatics with the joint efforts of more interdisciplinary scientists.

Autonomous nanomanufacturing of lead-free metal halide perovskite nanocrystals using a self-driving fluidic lab

Lead-based metal halide perovskite (MHP) nanocrystals (NCs) have emerged as a promising class of semiconducting nanomaterials for a wide range of optoelectronic and photoelectronic applications. However, the intrinsic lead toxicity of MHP NCs has significantly hampered their large-scale device applications. Copper-base MHP NCs with composition-tunable optical properties have emerged as a prominent lead-free MHP NC candidate. However, comprehensive synthesis space exploration, development, and synthesis science studies of copper-based MHP NCs have been limited by the manual nature of flask-based synthesis and characterization methods. In this study, we present an autonomous approach for the development of lead-free MHP NCs via seamless integration of a modular microfluidic platform with machine learning-assisted NC synthesis modeling and experiment selection to establish a self-driving fluidic lab for accelerated NC synthesis science studies. For the first time, a successful and reproducible in-flow synthesis of Cs3Cu2I5 NCs is presented. Autonomous experimentation is then employed for rapid in-flow synthesis science studies of Cs3Cu2I5 NCs. The autonomously generated experimental NC synthesis dataset is then utilized for fast-tracked synthetic route optimization of high-performing Cs3Cu2I5 NCs.

Data-Driven Predetermination of Cu Oxidation State in Copper Nanoparticles: Application to the Synthesis by Laser Ablation in Liquid

Copper-based nanocrystals are reference nanomaterials for integration into emerging green technologies, with laser ablation in liquid (LAL) being a remarkable technique for their synthesis. However, the achievement of a specific type of nanocrystal, among the whole library of nanomaterials available using LAL, has been until now an empirical endeavor based on changing synthesis parameters and characterizing the products. Here, we started from the bibliographic analysis of LAL synthesis of Cu-based nanocrystals to identify the relevant physical and chemical features for the predetermination of copper oxidation state. First, single features and their combinations were screened by linear regression analysis, also using a genetic algorithm, to find the best correlation with experimental output and identify the equation giving the best prediction of the LAL results. Then, machine learning (ML) models were exploited to unravel cross-correlations between features that are hidden in the linear regression analysis. Although the LAL-generated Cu nanocrystals may be present in a range of oxidation states, from metallic copper to cuprous oxide (Cu2O) and cupric oxide (CuO), in addition to the formation of other materials such as Cu2S and CuCN, ML was able to guide the experiments toward the maximization of the compounds in the greatest demand for integration in sustainable processes. This approach is of general applicability to other nanomaterials and can help understand the origin of the chemical pathways of nanocrystals generated by LAL, providing a rational guideline for the conscious predetermination of laser-synthesis parameters toward the desired compounds.

A data reusability assessment in the nanosafety domain based on the NSDRA framework followed by an exploratory quantitative structure activity relationships (QSAR) modeling targeting cellular viability

INTRODUCTION

The current effort towards the digital transformation across multiple scientific domains requires data that is Findable, Accessible, Interoperable and Reusable (FAIR). In addition to the FAIR data, what is required for the application of computational tools, such as Quantitative Structure Activity Relationships (QSARs), is a sufficient data volume and the ability to merge sources into homogeneous digital assets. In the nanosafety domain there is a lack of FAIR available metadata.

METHODOLOGY

To address this challenge, we utilized 34 datasets from the nanosafety domain by exploiting the NanoSafety Data Reusability Assessment (NSDRA) framework, which allowed the annotation and assessment of dataset's reusability. From the framework's application results, eight datasets targeting the same endpoint (i.e. numerical cellular viability) were selected, processed and merged to test several hypothesis including universal versus nanogroup-specific QSAR models (metal oxide and nanotubes), and regression versus classification Machine Learning (ML) algorithms.

RESULTS

Universal regression and classification QSARs reached an 0.86 R2 and 0.92 accuracy, respectively, for the test set. Nanogroup-specific regression models reached 0.88 R2 for nanotubes test set followed by metal oxide (0.78). Nanogroup-specific classification models reached 0.99 accuracy for nanotubes test set, followed by metal oxide (0.91). Feature importance revealed different patterns depending on the dataset with common influential features including core size, exposure conditions and toxicological assay. Even in the case where the available experimental knowledge was merged, the models still failed to correctly predict the outputs of an unseen dataset, revealing the cumbersome conundrum of scientific reproducibility in realistic applications of QSAR for nanosafety. To harness the full potential of computational tools and ensure their long-term applications, embracing FAIR data practices is imperative in driving the development of responsible QSAR models.

CONCLUSIONS

This study reveals that the digitalization of nanosafety knowledge in a reproducible manner has a long way towards its successful pragmatic implementation. The workflow carried out in the study shows a promising approach to increase the FAIRness across all the elements of computational studies, from dataset's annotation, selection, merging to FAIR modeling reporting. This has significant implications for future research as it provides an example of how to utilize and report different tools available in the nanosafety knowledge system, while increasing the transparency of the results. One of the main benefits of this workflow is that it promotes data sharing and reuse, which is essential for advancing scientific knowledge by making data and metadata FAIR compliant. In addition, the increased transparency and reproducibility of the results can enhance the trustworthiness of the computational findings.

Silicon nanoparticles: Comprehensive review on biogenic synthesis and applications in agriculture

Recent advancements in nanotechnology have opened new advances in agriculture. Among other nanoparticles, silicon nanoparticles (SiNPs), due to their unique physiological characteristics and structural properties, offer a significant advantage as nanofertilizers, nanopesticides, nanozeolite and targeted delivery systems in agriculture. Silicon nanoparticles are well known to improve plant growth under normal and stressful environments. Nanosilicon has been reported to enhance plant stress tolerance against various environmental stress and is considered a non-toxic and proficient alternative to control plant diseases. However, a few studies depicted the phytotoxic effects of SiNPs on specific plants. Therefore, there is a need for comprehensive research, mainly on the interaction mechanism between NPs and host plants to unravel the hidden facts about silicon nanoparticles in agriculture. The present review illustrates the potential role of silicon nanoparticles in improving plant resistance to combat different environmental (abiotic and biotic) stresses and the underlying mechanisms involved. Furthermore, our review focuses on providing the overview of various methods exploited in the biogenic synthesis of silicon nanoparticles. However, certain limitations exist in synthesizing the well-characterized SiNPs on a laboratory scale. To bridge this gap, in the last section of the review, we discussed the possible use of the machine learning approach in future as an effective, less labour-intensive and time-consuming method for silicon nanoparticle synthesis. The existing research gaps from our perspective and future research directions for utilizing SiNPs in sustainable agriculture development have also been highlighted.

Surfactant Layers on Gold Nanorods

ConspectusGold nanorods (Au NRs) are an exceptionally promising tool in nanotechnology due to three key factors: (i) their strong interaction with electromagnetic radiation, stemming from their plasmonic nature, (ii) the ease with which the resonance frequency of their longitudinal plasmon mode can be tuned from the visible to the near-infrared region of the electromagnetic spectrum based on their aspect ratio, and (iii) their simple and cost-effective preparation through seed-mediated chemical growth. In this synthetic method, surfactants play a critical role in controlling the size, shape, and colloidal stability of Au NRs. For example, surfactants can stabilize specific crystallographic facets during the formation of Au NRs, leading to the formation of NRs with specific morphologies.The process of surfactant adsorption onto the NR surface may result in various assemblies of surfactant molecules, such as spherical micelles, elongated micelles, or bilayers. Again, the assembly mode is critical toward determining the further availability of the Au NR surface to the surrounding medium. Despite its importance and a great deal of research effort, the interaction between Au NPs and surfactants remains insufficiently understood, because the assembly process is influenced by numerous factors, including the chemical nature of the surfactant, the surface morphology of Au NPs, and solution parameters. Therefore, gaining a more comprehensive understanding of these interactions is essential to unlock the full potential of the seed-mediated growth method and the applications of plasmonic NPs. A plethora of characterization techniques have been applied to reach such an understanding, but many open questions remain.In this Account, we review the current knowledge on the interactions between surfactants and Au NRs. We briefly introduce the state-of-the-art methods for synthesizing Au NRs and highlight the crucial role of cationic surfactants during this process. The self-assembly and organization of surfactants on the Au NR surface is then discussed to better understand their role in seed-mediated growth. Subsequently, we provide examples and elucidate how chemical additives can be used to modulate micellar assemblies, in turn allowing for a finer control over the growth of Au NRs, including chiral NRs. Next, we review the main experimental characterization and computational modeling techniques that have been applied to shed light on the arrangement of surfactants on Au NRs and summarize the advantages and disadvantages for each technique. The Account ends with a "Conclusions and Outlook" section, outlining promising future research directions and developments that we consider are still required, mostly related to the application of electron microscopy in liquid and in 3D. Finally, we remark on the potential of exploiting machine learning techniques to predict synthetic routes for NPs with predefined structures and properties.

Data-Driven Multi-Objective Optimization Tactics for Catalytic Asymmetric Reactions Using Bisphosphine Ligands

Optimization of the catalyst structure to simultaneously improve multiple reaction objectives (e.g., yield, enantioselectivity, and regioselectivity) remains a formidable challenge. Herein, we describe a machine learning workflow for the multi-objective optimization of catalytic reactions that employ chiral bisphosphine ligands. This was demonstrated through the optimization of two sequential reactions required in the asymmetric synthesis of an active pharmaceutical ingredient. To accomplish this, a density functional theory-derived database of >550 bisphosphine ligands was constructed, and a designer chemical space mapping technique was established. The protocol used classification methods to identify active catalysts, followed by linear regression to model reaction selectivity. This led to the prediction and validation of significantly improved ligands for all reaction outputs, suggesting a general strategy that can be readily implemented for reaction optimizations where performance is controlled by bisphosphine ligands.

Neural network and decision tree-based machine learning tools to analyse the anion-responsive behaviours of emissive Ru(ii)–terpyridine complexes

Artificial neural network, adaptive neuro-fuzzy inference and decision tree regression are implemented to analyse the anion-responsive behaviours of emissive Ru(ii)–terpyridine complexes.

Biogenic Synthesis of Copper-Based Nanomaterials Using Plant Extracts and Their Applications: Current and Future Directions

Plants have been used for multiple purposes over thousands of years in various applications such as traditional Chinese medicine and Ayurveda. More recently, the special properties of phytochemicals within plant extracts have spurred researchers to pursue interdisciplinary studies uniting nanotechnology and biotechnology. Plant-mediated green synthesis of nanomaterials utilises the phytochemicals in plant extracts to produce nanomaterials. Previous publications have demonstrated that diverse types of nanomaterials can be produced from extracts of numerous plant components. This review aims to cover in detail the use of plant extracts to produce copper (Cu)-based nanomaterials, along with their robust applications. The working principles of plant-mediated Cu-based nanomaterials in biomedical and environmental applications are also addressed. In addition, it discusses potential biotechnological solutions and new applications and research directions concerning plant-mediated Cu-based nanomaterials that are yet to be discovered so as to realise the full potential of the plant-mediated green synthesis of nanomaterials in industrial-scale production and wider applications. This review provides readers with comprehensive information, guidance, and future research directions concerning: (1) plant extraction, (2) plant-mediated synthesis of Cu-based nanomaterials, (3) the applications of plant-mediated Cu-based nanomaterials in biomedical and environmental remediation, and (4) future research directions in this area.