Data flow modeling, data mining and QSAR in high-throughput discovery of functional nanomaterials

A 30-Year Review on Nanocomposites: Comprehensive Bibliometric Insights into Microstructural, Electrical, and Mechanical Properties Assisted by Artificial Intelligence

From 1990 to 2024, this study presents a groundbreaking bibliometric and sentiment analysis of nanocomposite literature, distinguishing itself from existing reviews through its unique computational methodology. Developed by our research group, this novel approach systematically investigates the evolution of nanocomposites, focusing on microstructural characterization, electrical properties, and mechanical behaviors. By deploying advanced Boolean search strategies within the Scopus database, we achieve a meticulous extraction and in-depth exploration of thematic content, a methodological advancement in the field. Our analysis uniquely identifies critical trends and insights concerning nanocomposite microstructure, electrical attributes, and mechanical performance. The paper goes beyond traditional textual analytics and bibliometric evaluation, offering new interpretations of data and highlighting significant collaborative efforts and influential studies within the nanocomposite domain. Our findings uncover the evolution of research language, thematic shifts, and global contributions, providing a distinct and comprehensive view of the dynamic evolution of nanocomposite research. A critical component of this study is the "State-of-the-Art and Gaps Extracted from Results and Discussions" section, which delves into the latest advancements in nanocomposite research. This section details various nanocomposite types and their properties and introduces novel interpretations of their applications, especially in nanocomposite films. By tracing historical progress and identifying emerging trends, this analysis emphasizes the significance of collaboration and influential studies in molding the field. Moreover, the "Literature Review Guided by Artificial Intelligence" section showcases an innovative AI-guided approach to nanocomposite research, a first in this domain. Focusing on articles from 2023, selected based on citation frequency, this method offers a new perspective on the interplay between nanocomposites and their electrical properties. It highlights the composition, structure, and functionality of various systems, integrating recent findings for a comprehensive overview of current knowledge. The sentiment analysis, with an average score of 0.638771, reflects a positive trend in academic discourse and an increasing recognition of the potential of nanocomposites. Our bibliometric analysis, another methodological novelty, maps the intellectual domain, emphasizing pivotal research themes and the influence of crosslinking time on nanocomposite attributes. While acknowledging its limitations, this study exemplifies the indispensable role of our innovative computational tools in synthesizing and understanding the extensive body of nanocomposite literature. This work not only elucidates prevailing trends but also contributes a unique perspective and novel insights, enhancing our understanding of the nanocomposite research field.

High-throughput synthesis of AlPO and SAPO zeolites by ink jet printing

Ink jet printing is for the first time introduced into the synthesis of aluminophosphate (AlPO) and silicoaluminophosphate (SAPO) zeolite. As a high-throughput technique, 256 zeolite precursors with multiple formulations could be obtained within 2 h, while the product phase was regulated relative to the variant compositions.

Recent advances and prospects of inkjet printing in heterogeneous catalysis

This review provides an insight into inkjet printing technology in the context of heterogeneous catalysis.

Chemistry in supercritical fluids for the synthesis of metal nanomaterials

The supercritical flow synthesis of metal nanomaterials is sustainable and scalable for the efficient production of materials.

Continuous Hydrothermal Synthesis of Inorganic Nanoparticles: Applications and Future Directions

Nanomaterials are at the leading edge of the emerging field of nanotechnology. Their unique and tunable size-dependent properties (in the range 1-100 nm) make these materials indispensable in many modern technological applications. In this Review, we summarize the state-of-art in the manufacture and applications of inorganic nanoparticles made using continuous hydrothermal flow synthesis (CHFS) processes. First, we introduce ideal requirements of any flow process for nanoceramics production, outline different approaches to CHFS, and introduce the pertinent properties of supercritical water and issues around mixing in flow, to generate nanoparticles. This Review then gives comprehensive coverage of the current application space for CHFS-made nanomaterials including optical, healthcare, electronics (including sensors, information, and communication technologies), catalysis, devices (including energy harvesting/conversion/fuels), and energy storage applications. Thereafter, topics of precursor chemistry and products, as well as materials or structures, are discussed (surface-functionalized hybrids, nanocomposites, nanograined coatings and monoliths, and metal-organic frameworks). Later, this Review focuses on some of the key apparatus innovations in the field, such as in situ flow/rapid heating systems (to investigate kinetics and mechanisms), approaches to high throughput flow syntheses (for nanomaterials discovery), as well as recent developments in scale-up of hydrothermal flow processes. Finally, this Review covers environmental considerations, future directions and capabilities, along with the conclusions and outlook.

A quasi-QSPR modelling for the photocatalytic decolourization rate constants and cellular viability (CV%) of nanoparticles by CORAL

Most quantitative structure-property/activity relationships (QSPRs/QSARs) predict various endpoints related to organic compounds. Gradually, the variety of organic compounds has been extended to inorganic, organometallic compounds and polymers. However, the so-called molecular descriptors cannot be defined for super-complex substances such as different nanomaterials and peptides, since there is no simple and clear representation of their molecular structure. Some possible ways to define approaches for a predictive model in the case of super-complex substances are discussed. The basic idea of the approach is to change the traditionally used paradigm 'the endpoint is a mathematical function of the molecular structure' with another paradigm 'the endpoint is a mathematical function of available eclectic information'. The eclectic data can be (i) conditions of a synthesis, (ii) technological attributes, (iii) size of nanoparticles, (iv) concentration, (v) attributes related to cell membranes, and so on. Two examples of quasi-QSPR/QSAR analyses are presented and discussed. These are (i) photocatalytic decolourization rate constants (DRC) (10(-5)/s) of different nanopowders; and (ii) the cellular viability under the effect of nano-SiO(2).

High-throughput continuous hydrothermal synthesis of nanomaterials (part II): unveiling the as-prepared CexZryYzO2-δ phase diagram

High-throughput continuous hydrothermal flow synthesis was used to manufacture 66 unique nanostructured oxide samples in the Ce-Zr-Y-O system. This synthesis approach resulted in a significant increase in throughput compared to that of conventional batch or continuous hydrothermal synthesis methods. The as-prepared library samples were placed into a wellplate for both automated high-throughput powder X-ray diffraction and Raman spectroscopy data collection, which allowed comprehensive structural characterization and phase mapping. The data suggested that a continuous cubic-like phase field connects all three Ce-Zr-O, Ce-Y-O, and Y-Zr-O binary systems together with a smooth and steady transition between the structures of neighboring compositions. The continuous hydrothermal process led to as-prepared crystallite sizes in the range of 2-7 nm (as determined by using the Scherrer equation).

Development of structure-activity relationship for metal oxide nanoparticles

Nanomaterial structure-activity relationships (nano-SARs) for metal oxide nanoparticles (NPs) toxicity were investigated using metrics based on dose-response analysis and consensus self-organizing map clustering. The NP cellular toxicity dataset included toxicity profiles consisting of seven different assays for human bronchial epithelial (BEAS-2B) and murine myeloid (RAW 264.7) cells, over a concentration range of 0.39-100 mg L(-1) and exposure time up to 24 h, for twenty-four different metal oxide NPs. Various nano-SAR building models were evaluated, based on an initial pool of thirty NP descriptors. The conduction band energy and ionic index (often correlated with the hydration enthalpy) were identified as suitable NP descriptors that are consistent with suggested toxicity mechanisms for metal oxide NPs and metal ions. The best performing nano-SAR with the above two descriptors, built with support vector machine (SVM) model and of validated robustness, had a balanced classification accuracy of ~94%. An applicability domain for the present data was established with a reasonable confidence level of 80%. Given the potential role of nano-SARs in decision making, regarding the environmental impact of NPs, the class probabilities provided by the SVM nano-SAR enabled the construction of decision boundaries with respect to toxicity classification under different acceptance levels of false negative relative to false positive predictions.

Nano-SAR development for bioactivity of nanoparticles with considerations of decision boundaries

The development of classification nano-structure-activity Relationships (nano-SARs) of nanoparticle (NP) bioactivity is presented with the aim of demonstrating the integration of multiparametric toxicity/bioactivity assays to arrive at statistically meaningful class definitions (i.e., bioactivity/inactivity endpoints), as well as the implications of nano-SAR applicability domains and decision boundaries. Nano-SARs are constructed based on a dataset of 44 iron oxide core nanoparticles (NPs), used in molecular imaging and nano-sensing, containing bioactivity profiles for four cell types and four different assays. Class definitions are developed on the basis of 'hit' (i.e., significant bioactivity) identification analysis and self-organizing map based consensus clustering; these class definitions enable construction of nano-SARs of a high classification accuracy (>78%) with different NP descriptor combinations that include primary size, spin-lattice and spin-spin relaxivities, and zeta potentials. Analysis of the nano-SAR performance for different class definitions suggests that H4 (i.e., class with at least four hits) is a reasonable endpoint (from a 'regulatory' viewpoint) for keeping the level of false negatives (i.e., incorrect labeling of bioactive NPs as inactive) low. The establishment of a quantitative nano-SAR applicability domain is demonstrated, making use of a probability density with the H4 class definition and naive Bayesian classifier (NBC) model (with spin-lattice relaxivity and zeta potential as descriptors). Decision boundaries are determined for the above H4/NBC nano-SAR for different acceptance levels of false negative to false positive predictions, illustrating a practical approach that may assist in regulatory decision making with a consideration of reducing the likelihood of identifying bioactive NPs as being inactive.

Applying quantitative structure-activity relationship approaches to nanotoxicology: current status and future potential

The potential (eco)toxicological hazard posed by engineered nanoparticles is a major scientific and societal concern since several industrial sectors (e.g. electronics, biomedicine, and cosmetics) are exploiting the innovative properties of nanostructures resulting in their large-scale production. Many consumer products contain nanomaterials and, given their complex life-cycle, it is essential to anticipate their (eco)toxicological properties in a fast and inexpensive way in order to mitigate adverse effects on human health and the environment. In this context, the application of the structure-toxicity paradigm to nanomaterials represents a promising approach. Indeed, according to this paradigm, it is possible to predict toxicological effects induced by chemicals on the basis of their structural similarity with chemicals for which toxicological endpoints have been previously measured. These structure-toxicity relationships can be quantitative or qualitative in nature and they can predict toxicological effects directly from the physicochemical properties of the entities (e.g. nanoparticles) of interest. Therefore, this approach can aid in prioritizing resources in toxicological investigations while reducing the ethical and monetary costs that are related to animal testing. The purpose of this review is to provide a summary of recent key advances in the field of QSAR modelling of nanomaterial toxicity, to identify the major gaps in research required to accelerate the use of quantitative structure-activity relationship (QSAR) methods, and to provide a roadmap for future research needed to achieve QSAR models useful for regulatory purposes.

Inkjet printing assisted synthesis of multicomponent mesoporous metal oxides for ultrafast catalyst exploration

We describe an inkjet printing assisted cooperative-assembly method for high-throughput generation of catalyst libraries (multicomponent mesoporous metal oxides) at a rate of 1,000,000-formulations/hour with up to eight-component compositions. The compositions and mesostructures of the libraries can be well-controlled and continuously varied. Fast identification of an inexpensive and efficient quaternary catalyst for photocatalytic hydrogen evolution is achieved via a multidimensional group testing strategy to reduce the number of performance validation experiments (25,000-fold reduction over an exhaustive one-by-one search).