jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Amartya Chakrabarti | Emily Alessandri
Source: Applied Nano (article, 2024-08-26)
Field: Materials Science — Materials Chemistry

Syntheses, Properties, and Applications of ZnS-Based Nanomaterials ZnS is a II-VI semiconductor with a wide bandgap. ZnS-based nanomaterials have been produced in a variety of morphologies with unique properties and characteristic features. An extensive collection of research activities is available on various synthetic methodologies to produce such a wide variety of ZnS-based nanomaterials. In this comprehensive review, we thoroughly covered all the different synthetic techniques employed by researchers across the globe to produce zero-dimensional, one-dimensional, two-dimensional, and three-dimensional ZnS-based nanomaterials. Depending on their morphologies and properties, ZnS-based nanomaterials have found many applications, including optoelectronics, sensors, catalysts, batteries, solar cells, and biomedical fields. The properties and applications of ZnS-based nanostructures are described, and the scope of the future direction is highlighted. Nanomaterials ; Materials science ; Nanotechnology Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films ; Copper-based nanomaterials and applications
Authors: Iván Martínez‐López | J.C. Martínez-Fuentes | J. Bueno-Ferrer | Arantxa Davó‐Quiñonero | Esteban Guillén‐Bas | Esther Bailón‐García | Dolores Lozano‐Castelló | Agustín Bueno‐López
Source: Journal of CO2 Utilization (article, 2024-03-01)
Field: Materials Science — Materials Chemistry

Structural design and particle size examination on NiO-CeO2 catalysts supported on 3D-printed carbon monoliths for CO2 methanation 3D-printed high-surface carbon monoliths have been fabricated and tested as catalyst supports of CO2 methanation active phases (NiO-CeO2, 12 wt% Ni). The carbon carriers show a developed microporosity and good adherence to the catalytic phases of NiO-CeO2, showing great stability and cyclability. Two monolith designs were used: a conventional parallel-channeled structure (honeycomb) and a complex 3D network of non-linear channels built upon interconnected circular sections (circles), where flow turbulences along the reactant gas path are spurred. The effect of the active phases particle size on the catalyst distribution and the overall performance has been assessed by comparing NiO-CeO2 nanoparticles of 7 nm average (Np), with a reference counterpart of uncontrolled structure (Ref). The improved radial gases diffusion in the circles monolith design is confirmed, and nanoparticles show enhanced CO2 methanation activity than the uncontrolled-size active phase at low temperatures (< 300 ºC). On the contrary, the Ref catalysts achieve higher CH4 production at higher temperatures, where the reaction kinetics is controlled by mass transfer limitations (T > 300 ºC). SEM and Hg porosimetry evidence that nanoparticles are deposited at deeper penetration through the narrow micropores of the carbon matrix of the monolithic supports, which tend to accumulate on the channels surface remaining more accessible to the reactant molecules. Altogether, this study examines the impact of the channel tortuosity and the active phase sizing on the CO2 methanation activity, serving as ground knowledge for the further rational and scalable fabrication of carbon monolith for catalytic applications. Methanation ; Monolith ; Materials science ; Catalysis ; Chemical engineering ; Carbon fibers ; Non-blocking I/O ; Nanoparticle ; Nanotechnology ; Composite material ; Chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Catalysts for Methane Reforming
Authors: Shireen Zangana | M. Vereš | Attila Bonyár
Source: Molecules (review, 2024-07-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Surface-Enhanced Raman Spectroscopy (SERS)-Based Sensors for Deoxyribonucleic Acid (DNA) Detection Surface-enhanced Raman spectroscopy (SERS) has emerged as a powerful technique for the detection and analysis of biomolecules due to its high sensitivity and selectivity. In recent years, SERS-based sensors have received significant attention for the detection of deoxyribonucleic acid (DNA) molecules, offering promising applications in fields such as medical diagnostics, forensic analysis, and environmental monitoring. This paper provides a concise overview of the principles, advancements, and potential of SERS-based sensors for DNA detection. First, the fundamental principles of SERS are introduced, highlighting its ability to enhance the Raman scattering signal by several orders of magnitude through the interaction between target molecules with metallic nanostructures. Then, the fabrication technologies of SERS substrates tailored for DNA detection are reviewed. The performances of SERS substrates previously reported for DNA detection are compared and analyzed in terms of the limit of detection (LOD) and enhancement factor (EF) in detail, with respect to the technical parameters of Raman spectroscopy (e.g., laser wavelength and power). Additionally, strategies for functionalizing the sensor surfaces with DNA-specific capture probes or aptamers are outlined. The collected data can be of help in selecting and optimizing the most suitable fabrication technology considering nucleotide sensing applications with Raman spectroscopy. Biomolecule ; Surface-enhanced Raman spectroscopy ; Aptamer ; Raman spectroscopy ; Nanotechnology ; Raman scattering ; Materials science ; Detection limit ; Chemistry ; Optics ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Biosensors and Analytical Detection
Authors: Peter A. Beaucage | Duncan R. Sutherland | Tyler B. Martin
Source: Macromolecules (article, 2024-09-07)
Field: Materials Science — Materials Chemistry

Automation and Machine Learning for Accelerated Polymer Characterization and Development: Past, Potential, and a Path Forward Automation and machine learning techniques are poised to dramatically accelerate the development of new materials while simultaneously increasing our understanding of the physics and chemistry that underlie the formation of such materials. In particular, the convergence of accessible machine learning tools, the availability of high-quality data, and the advent of accessible experimental automation platforms have led to a number of closed-loop autonomous experimentation platforms or “self-driving labs”. Such platforms integrate robotic experimenters with AI-guided experiment planning to autonomously perform large numbers of experiments without human input. After briefly reviewing the state of the field and the broad classes of autonomous efforts, this perspective outlines several high-value focus areas for future ML-guided characterization efforts. Among many advantages, we expect that autonomous approaches will allow the systematic study of rare and nonequilibrium phenomena, provide dramatically greater measurement efficiency through targeting of cutting-edge, resource-intensive characterization, and enable a higher level of thinking and experimental planning for human investigators. Finally, we outline the principal barriers to realization of these advantages, including: (1) a lack of organizational structures and workforce development for the highly interdisciplinary programs needed; (2) funding and publication mechanisms that assign greater value to individual scientific results than foundational infrastructure development; and (3) a dearth of standards for open interchange of hardware, software, and data among the polymer community. We believe that we are in the early days of a once-in-a-generation shift in the way science is planned, executed, and evaluated, and we hope to provide a blueprint for the broader polymer community to take a leading role in this shift. Automation ; Characterization (materials science) ; Path (computing) ; Nanotechnology ; Computer science ; Artificial intelligence ; Engineering ; Systems engineering ; Materials science ; Mechanical engineering Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Force Microscopy Techniques and Applications
Authors: Arpan Biswas | Yongtao Liu | Nicole Creange | Yuchen Liu | Stephen Jesse | Jan‐Chi Yang | Sergei V. Kalinin | Maxim Ziatdinov | Rama K. Vasudevan
Source: npj Computational Materials (article, 2024-02-03)
Field: Materials Science — Materials Chemistry

A dynamic Bayesian optimized active recommender system for curiosity-driven partially Human-in-the-loop automated experiments Abstract Optimization of experimental materials synthesis and characterization through active learning methods has been growing over the last decade, with examples ranging from measurements of diffraction on combinatorial alloys at synchrotrons, to searches through chemical space with automated synthesis robots for perovskites. In virtually all cases, the target property of interest for optimization is defined a priori with the ability to shift the trajectory of the optimization based on human-identified findings during the experiment is lacking. Thus, to highlight the best of both human operators and AI-driven experiments, here we present the development of a human–AI collaborated experimental workflow, via a Bayesian optimized active recommender system (BOARS), to shape targets on the fly with human real-time feedback. Here, the human guidance overpowers AI at early iteration when prior knowledge (uncertainty) is minimal (higher), while the AI overpowers the human during later iterations to accelerate the process with the human-assessed goal. We showcase examples of this framework applied to pre-acquired piezoresponse force spectroscopy of a ferroelectric thin film, and in real-time on an atomic force microscope, with human assessment to find symmetric hysteresis loops. It is found that such features appear more affected by subsurface defects than the local domain structure. This work shows the utility of human–AI approaches for curiosity driven exploration of systems across experimental domains. Bayesian optimization ; Computer science ; Human intelligence ; Human-in-the-loop ; Artificial intelligence ; Workflow ; Recommender system ; Ranging ; Machine learning ; A priori and a posteriori ; Hyperparameter optimization ; Curiosity ; Domain (mathematical analysis) ; Process (computing) ; Mathematics ; Database Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Machine Learning and Data Classification
Authors: John C. Thomas | Wei Chen | Yihuang Xiong | Bradford A. Barker | Junze Zhou | Weiru Chen | Antonio Rossi | Nolan Kelly | Zhuohang Yu | Da Zhou | Shalini Kumari | Edward S. Barnard | Joshua A. Robinson | Mauricio Terrones | Adam Schwartzberg | D. Frank Ogletree | Eli Rotenberg | Marcus M. Noack | Sinéad M. Griffin | Archana Raja | David A. Strubbe | Gian‐Marco Rignanese | Alexander Weber‐Bargioni | Geoffroy Hautier
Source: Nature Communications (article, 2024-04-26)
Field: Materials Science — Materials Chemistry

A substitutional quantum defect in WS2 discovered by high-throughput computational screening and fabricated by site-selective STM manipulation Point defects in two-dimensional materials are of key interest for quantum information science. However, the parameter space of possible defects is immense, making the identification of high-performance quantum defects very challenging. Here, we perform high-throughput (HT) first-principles computational screening to search for promising quantum defects within WS<sub>2</sub>, which present localized levels in the band gap that can lead to bright optical transitions in the visible or telecom regime. Our computed database spans more than 700 charged defects formed through substitution on the tungsten or sulfur site. We found that sulfur substitutions enable the most promising quantum defects. We computationally identify the neutral cobalt substitution to sulfur (Co <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msubsup><mml:mrow></mml:mrow> <mml:mrow><mml:mi>S</mml:mi></mml:mrow> <mml:mrow><mml:mn>0</mml:mn></mml:mrow> </mml:msubsup> </mml:math> ) and fabricate it with scanning tunneling microscopy (STM). The Co <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msubsup><mml:mrow></mml:mrow> <mml:mrow><mml:mi>S</mml:mi></mml:mrow> <mml:mrow><mml:mn>0</mml:mn></mml:mrow> </mml:msubsup> </mml:math> electronic structure measured by STM agrees with first principles and showcases an attractive quantum defect. Our work shows how HT computational screening and nanoscale synthesis routes can be combined to design promising quantum defects. Throughput ; Quantum ; Computer science ; High-throughput screening ; Nanotechnology ; Materials science ; Bioinformatics ; Physics ; Biology ; Telecommunications ; Quantum mechanics ; Wireless 2D Materials and Applications ; Electronic and Structural Properties of Oxides ; MXene and MAX Phase Materials
Authors: Weiliang Luo | Gengmo Zhou | Zhengdan Zhu | Yannan Yuan | Guolin Ke | Zhewei Wei | Zhifeng Gao | Hang Zheng
Source: JACS Au (article, 2024-07-17)
Field: Materials Science — Materials Chemistry

Bridging Machine Learning and Thermodynamics for Accurate p <i>K</i> <sub>a</sub> Prediction Integrating scientific principles into machine learning models to enhance their predictive performance and generalizability is a central challenge in the development of AI for Science. Herein, we introduce Uni-p<i>K</i> <sub>a</sub>, a novel framework that successfully incorporates thermodynamic principles into machine learning modeling, achieving high-precision predictions of acid dissociation constants (p<i>K</i> <sub>a</sub>), a crucial task in the rational design of drugs and catalysts, as well as a modeling challenge in computational physical chemistry for small organic molecules. Uni-p<i>K</i> <sub>a</sub> utilizes a comprehensive free energy model to represent molecular protonation equilibria accurately. It features a structure enumerator that reconstructs molecular configurations from p<i>K</i> <sub>a</sub> data, coupled with a neural network that functions as a free energy predictor, ensuring high-throughput, data-driven prediction while preserving thermodynamic consistency. Employing a pretraining-finetuning strategy with both predicted and experimental p<i>K</i> <sub>a</sub> data, Uni-p<i>K</i> <sub>a</sub> not only achieves state-of-the-art accuracy in chemoinformatics but also shows comparable precision to quantum mechanics-based methods. Generalizability theory ; Computer science ; Artificial neural network ; Quantitative structure–activity relationship ; Protonation ; Machine learning ; Bridging (networking) ; Artificial intelligence ; Chemistry ; Quantum chemical ; Molecule ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Free Radicals and Antioxidants
Authors: Pavlo O. Dral
Source: Chemical Communications (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

AI in computational chemistry through the lens of a decade-long journey This article gives a perspective on the progress of AI tools in computational chemistry through the lens of the author's decade-long contributions put in the wider context of the trends in this rapidly expanding field. This progress over the last decade is tremendous: while a decade ago we had a glimpse of what was to come through many proof-of-concept studies, now we witness the emergence of many AI-based computational chemistry tools that are mature enough to make faster and more accurate simulations increasingly routine. Such simulations in turn allow us to validate and even revise experimental results, deepen our understanding of the physicochemical processes in nature, and design better materials, devices, and drugs. The rapid introduction of powerful AI tools gives rise to unique challenges and opportunities that are discussed in this article too. Lens (geology) ; Chemistry ; Optics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Salah Abdalla | Mohamed Kheireddine Aroua | Lai Ti Gew
Source: ACS Omega (review, 2024-10-25)
Field: Chemistry — Inorganic Chemistry

A Comprehensive Review of Plant-Based Cosmetic Oils (Virgin Coconut Oil, Olive Oil, Argan Oil, and Jojoba Oil): Chemical and Biological Properties and Their Cosmeceutical Applications Plant-based oils, such as coconut, olive, argan, and jojoba, are abundant in natural emollients and vital fatty acids that hydrate and moisturize the skin. They shield the surface, stop moisture loss, and maintain suppleness of, the skin. They are rich in vitamins, nutrients, and antioxidants that nourish the skin. Virgin coconut oil (VCO) is used as a functional food due to its tremendous health benefits, and olive oil is well-known for its cosmetic and culinary applications. Argan oil contains many antioxidants, vital fatty acids, and vitamin E, while jojoba oil is an excellent moisturizer and conditioner. Plant-based oils can be extracted using various techniques including conventional and chemical extraction methods, and each will affect the yield and quality. Traditional methods like mechanical pressing are less efficient, whereas extraction methods such as pressurized liquid and supercritical fluid extraction may give higher yields and better quality. The chemical composition of olive oil primarily consists of saturated fatty acids (SFAs), polyunsaturated fatty acids (PUFAs), and monounsaturated fatty acids (MUFAs). Argan oil is rich in tocopherols, containing between 60 and 90 mg per 100 g, with only 19 g/100 g of argan oil's fatty acids saturated. Jojoba oil is liquid wax comprising over 98% triglyceride esters, pure waxes, vitamins, and sterols. This review focuses on the chemical and biological properties, production processes, and applications of natural cosmetic oils (virgin coconut oil, olive oil, argan oil, and jojoba oil), emphasizing their usage in skin care and cosmeceutical products. Cosmeceutical ; Coconut oil ; Olive oil ; Edible oil ; Palm oil ; Pulp and paper industry ; Food science ; Chemistry ; Biotechnology ; Biology ; Engineering Coconut Research and Applications ; Essential Oils and Antimicrobial Activity ; Edible Oils Quality and Analysis
Authors: Fu‐Qiang Gong | Yun‐Pei Liu | Ye Wang | E Weinan | Zhong‐Qun Tian | Jun Cheng
Source: Angewandte Chemie International Edition (article, 2024-04-19)
Field: Materials Science — Materials Chemistry

Machine Learning Molecular Dynamics Shows Anomalous Entropic Effect on Catalysis through Surface Pre‐melting of Nanoclusters Due to the superior catalytic activity and efficient utilization of noble metals, nanocatalysts are extensively used in the modern industrial production of chemicals. The surface structures of these materials are significantly influenced by reactive adsorbates, leading to dynamic behavior under experimental conditions. The dynamic nature poses significant challenges in studying the structure-activity relations of catalysts. Herein, we unveil an anomalous entropic effect on catalysis via surface pre-melting of nanoclusters through machine learning accelerated molecular dynamics and free energy calculation. We find that due to the pre-melting of shell atoms, there exists a non-linear variation in the catalytic activity of the nanoclusters with temperature. Consequently, two notable changes in catalyst activity occur at the respective temperatures of melting for the shell and core atoms. We further study the nanoclusters with surface point defects, i.e. vacancy and ad-atom, and observe significant decrease in the surface melting temperatures of the nanoclusters, enabling the reaction to take place under more favorable and milder conditions. These findings not only provide novel insights into dynamic catalysis of nanoclusters but also offer new understanding of the role of point defects in catalytic processes. Nanoclusters ; Catalysis ; Nanomaterial-based catalyst ; Vacancy defect ; Molecular dynamics ; Chemical physics ; Melting point ; Atom (system on chip) ; Materials science ; Nanoparticle ; Nanotechnology ; Chemistry ; Chemical engineering ; Computational chemistry ; Crystallography ; Organic chemistry Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Theoretical and Computational Physics
Authors: Soohaeng Yoo Willow | Amir Hajibabaei | Miran Ha | D. ChangMo Yang | Chang Woo Myung | Seung Kyu Min | Geunsik Lee | Kwang S. Kim
Source: Chemical Physics Reviews (article, 2024-11-27)
Field: Materials Science — Materials Chemistry

Sparse Gaussian process based machine learning first principles potentials for materials simulations: Application to batteries, solar cells, catalysts, and macromolecular systems To design new materials and understand their novel phenomena, it is imperative to predict the structure and properties of materials that often rely on first-principles theory. However, such methods are computationally demanding and limited to small systems. This topical review investigates machine learning (ML) approaches, specifically non-parametric sparse Gaussian process regression (SGPR), to model the potential energy surface (PES) of materials, while starting from the basics of ML methods for a comprehensive review. SGPR can efficiently represent PES with minimal ab initio data, significantly reducing the computational costs by bypassing the need for inverting massive covariance matrices. SGPR rank reduction accelerates density functional theory calculations by orders of magnitude, enabling accelerated simulations. An optimal adaptive sampling algorithm is utilized for on-the-fly regression with molecular dynamics, extending to interatomic potentials through scalable SGPR formalism. Through merging quantum mechanics with ML methods, the universal first-principles SGPR-based ML potential can create a digital-twin capable of predicting phenomena arising from static and dynamic changes as well as inherent and collective characteristics of materials. These techniques have been applied successfully to materials such as solid electrolytes, lithium-ion batteries, electrocatalysts, solar cells, and macromolecular systems, reproducing their structures, energetics, dynamics, properties, phase-changes, materials performance, and device efficiency. This review discusses the built-in library universal first-principles SGPR-based ML potential, showcasing its applications and successes, offering insights into the development of future ML potentials and their applications in advanced materials, catering to both educational and expert readers. Process (computing) ; Gaussian process ; Computer science ; Catalysis ; Artificial intelligence ; Process engineering ; Gaussian ; Materials science ; Nanotechnology ; Biochemical engineering ; Machine learning ; Biological system ; Chemistry ; Engineering ; Computational chemistry ; Organic chemistry ; Biology Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Computational Drug Discovery Methods