jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Carolin B. Wahl | Muratahan Aykol | Jordan H. Swisher | Joseph H. Montoya | Santosh K. Suram | Chad A. Mirkin
Source: Science Advances (article, 2021-12-22)
Field: Materials Science — Materials Chemistry

Machine learning–accelerated design and synthesis of polyelemental heterostructures In materials discovery efforts, synthetic capabilities far outpace the ability to extract meaningful data from them. To bridge this gap, machine learning methods are necessary to reduce the search space for identifying desired materials. Here, we present a machine learning–driven, closed-loop experimental process to guide the synthesis of polyelemental nanomaterials with targeted structural properties. By leveraging data from an eight-dimensional chemical space (Au-Ag-Cu-Co-Ni-Pd-Sn-Pt) as inputs, a Bayesian optimization algorithm is used to suggest previously unidentified nanoparticle compositions that target specific interfacial motifs for synthesis, results of which are iteratively shared back with the algorithm. This feedback loop resulted in successful syntheses of 18 heterojunction nanomaterials that are too complex to discover by chemical intuition alone, including extremely chemically complex biphasic nanoparticles reported to date. Platforms like the one developed here are poised to transform materials discovery across a wide swath of applications and industries. Chemical space ; Computer science ; Bayesian optimization ; Nanomaterials ; Intuition ; Nanotechnology ; Heterojunction ; Bridge (graph theory) ; Nanoparticle ; Artificial intelligence ; Machine learning ; Materials science ; Biochemical engineering ; Drug discovery ; Bioinformatics ; Engineering ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrocatalysts for Energy Conversion
Authors: Satyen Dhamankar | Michael A. Webb
Source: Journal of Polymer Science (article, 2021-10-02)
Field: Materials Science — Materials Chemistry

Chemically specific coarse‐graining of polymers: Methods and prospects Abstract Coarse‐grained (CG) modeling is an invaluable tool for the study of polymers and other soft matter systems due to the span of spatiotemporal scales that typify their physics and behavior. Given continuing advancements in experimental synthesis and characterization of such systems, there is ever greater need to leverage and expand CG capabilities to simulate diverse soft matter systems with chemical specificity. In this review, we discuss essential modeling techniques, bottom‐up coarse‐graining methodologies, and outstanding challenges for the chemically specific CG modeling of polymer‐based systems. This methodologically oriented discussion is complemented by representative literature examples for polymer simulation; we also offer some advisory practical considerations that should be useful for new researchers. Given its growing importance in the modeling and polymer science community, we further highlight some recent applications of machine learning that enhance CG modeling strategies. Overall, this review provides comprehensive discussion of methods and prospects for the chemically specific coarse‐graining of polymers. Granularity ; Leverage (statistics) ; Computer science ; Soft matter ; Polymer ; Nanotechnology ; Management science ; Biochemical engineering ; Data science ; Systems engineering ; Artificial intelligence ; Materials science ; Chemistry ; Engineering Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization ; Polymer crystallization and properties
Authors: Gaurav Tatrari | Manoj Karakoti | Chetna Tewari | Sandeep Pandey | Bhashkar Singh Bohra | Anirban Dandapat | Nanda Gopal Sahoo
Source: Materials Advances (article, 2021-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Solid waste-derived carbon nanomaterials for supercapacitor applications: a recent overview Universal solid waste management and its hazardous effects on the ecology, ecosystem, and the global human health index are some of the major issues that are currently threatening our future. Hazardous waste ; Human health ; Supercapacitor ; Environmental science ; Waste management ; Solid waste management ; Municipal solid waste ; Ecosystem ; Business ; Nanotechnology ; Ecology ; Engineering ; Materials science ; Medicine ; Environmental health ; Biology ; Chemistry Supercapacitor Materials and Fabrication ; Graphene and Nanomaterials Applications ; Graphene research and applications
Authors: Olesja Bondarenko | Monika Mortimer | Anne Kahru | Neus Feliu | Ibrahim Javed | Aleksandr Käkinen | Sijie Lin | Tian Xia | Yang Song | Thomas P. Davis | Iseult Lynch | Wolfgang J. Parak | David Tai Leong | Pu Chun Ke | Chunying Chen | Yuliang Zhao
Source: Nano Today (article, 2021-05-13)
Field: Materials Science — Materials Chemistry

Nanotoxicology and nanomedicine: The Yin and Yang of nano-bio interactions for the new decade Nanomedicine ; Nanotoxicology ; Nanotechnology ; Engineering ; General partnership ; Business ; Materials science ; Nanoparticle Nanoparticles: synthesis and applications ; Cell Image Analysis Techniques ; Computational Drug Discovery Methods
Authors: Pengcheng Zhang | Sifan Chen | Changjia Zhu | Linxiao Hou | Weipeng Xian | Xiuhui Zuo | Qinghua Zhang | Lin Zhang | Shengqian Ma | Qi Sun
Source: Nature Communications (article, 2021-03-23)
Field: Materials Science — Materials Chemistry

Covalent organic framework nanofluidic membrane as a platform for highly sensitive bionic thermosensation Thermal sensation, which is the conversion of a temperature stimulus into a biological response, is the basis of the fundamental physiological processes that occur ubiquitously in all organisms from bacteria to mammals. Significant efforts have been devoted to fabricating artificial membranes that can mimic the delicate functions of nature; however, the design of a bionic thermometer remains in its infancy. Herein, we report a nanofluidic membrane based on an ionic covalent organic framework (COF) that is capable of intelligently monitoring temperature variations and expressing it in the form of continuous potential differences. The high density of the charged sites present in the sub-nanochannels renders superior permselectivity to the resulting nanofluidic system, leading to a high thermosensation sensitivity of 1.27 mV K<sup>-1</sup>, thereby outperforming any known natural system. The potential applicability of the developed system is illustrated by its excellent tolerance toward a broad range of salt concentrations, wide working temperatures, synchronous response to temperature stimulation, and long-term ultrastability. Therefore, our study pioneers a way to explore COFs for mimicking the sophisticated signaling system observed in the nature. Nanotechnology ; Membrane ; Ionic bonding ; Covalent bond ; Materials science ; Artificial cell ; Biophysics ; Chemistry ; Biology ; Ion ; Biochemistry ; Organic chemistry Covalent Organic Framework Applications ; Nanopore and Nanochannel Transport Studies ; Luminescence and Fluorescent Materials
Authors: Yifei Zhang | Carlos Rı́os | Mikhail Y. Shalaginov | Mo Li | Arka Majumdar | Tian Gu | Juejun Hu
Source: Applied Physics Letters (article, 2021-05-24)
Field: Materials Science — Materials Chemistry

Myths and truths about optical phase change materials: A perspective Uniquely furnishing giant and nonvolatile modulation of optical properties and chalcogenide phase change materials (PCMs) have emerged as a promising material to transform integrated photonics and free-space optics alike. The surge of interest in these materials warrants a thorough understanding of their characteristics specifically in the context of photonic applications. This article seeks to clarify some commonly held misconceptions about PCMs and offer a perspective on new research frontiers in the field. Phase change ; Perspective (graphical) ; Photonics ; Context (archaeology) ; Chalcogenide ; Nanotechnology ; Engineering physics ; Phase (matter) ; Materials science ; Telecommunications ; Computer science ; Optoelectronics ; Engineering ; Physics ; History Phase-change materials and chalcogenides ; Neural Networks and Reservoir Computing ; Perovskite Materials and Applications
Authors: Fuqiang Chang | Carolien M. Vis | Wirawan Ciptonugroho | Pieter C. A. Bruijnincx
Source: Green Chemistry (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Recent developments in catalysis with Pickering Emulsions Pickering emulsions, emulsions stabilized by solid emulsifiers, offer many opportunities for application in catalysis. Recent advances in solid emulsifier design, type of conversions catalyzed and developments in reaction engineering are discussed. Pickering emulsion ; Catalysis ; Nanotechnology ; Materials science ; Polymer science ; Chemical engineering ; Chemistry ; Organic chemistry ; Engineering ; Nanoparticle Pickering emulsions and particle stabilization ; Surfactants and Colloidal Systems ; Advanced Polymer Synthesis and Characterization
Authors: Tim Van Den Bossche | Benoît J. Kunath | Kay Schallert | Stephanie Serena Schäpe | Paul E. Abraham | Jean Armengaud | Magnus Ø. Arntzen | Ariane Bassignani | Dirk Benndorf | Stephan Fuchs | Richard J. Giannone | Timothy J. Griffin | Live H. Hagen | Rashi Halder | Céline Henry | Robert L. Hettich | Robert Heyer | Pratik Jagtap | Nico Jehmlich | Marlene Jensen | Catherine Juste | Manuel Kleiner | Olivier Langella | Theresa Lehmann | Emma Leith | Patrick May | Bart Mesuere | Guylaine Miotello | Samantha L. Peters | Olivier Pible | Pedro T. Queiros | Udo Reichl | Bernhard Y. Renard | Henning Schiebenhoefer | Alexander Sczyrba | Alessandro Tanca | Kathrin Trappe | Jean‐Pierre Trezzi | Sergio Uzzau | Pieter Verschaffelt | Martin von Bergen� | Paul Wilmes | Maximilian Wolf | Lennart Martens | Thilo Muth
Source: Nature Communications (article, 2021-12-15)
Field: Chemistry — Spectroscopy

Critical Assessment of MetaProteome Investigation (CAMPI): a multi-laboratory comparison of established workflows Metaproteomics has matured into a powerful tool to assess functional interactions in microbial communities. While many metaproteomic workflows are available, the impact of method choice on results remains unclear. Here, we carry out a community-driven, multi-laboratory comparison in metaproteomics: the critical assessment of metaproteome investigation study (CAMPI). Based on well-established workflows, we evaluate the effect of sample preparation, mass spectrometry, and bioinformatic analysis using two samples: a simplified, laboratory-assembled human intestinal model and a human fecal sample. We observe that variability at the peptide level is predominantly due to sample processing workflows, with a smaller contribution of bioinformatic pipelines. These peptide-level differences largely disappear at the protein group level. While differences are observed for predicted community composition, similar functional profiles are obtained across workflows. CAMPI demonstrates the robustness of present-day metaproteomics research, serves as a template for multi-laboratory studies in metaproteomics, and provides publicly available data sets for benchmarking future developments. Metaproteomics ; Workflow ; Computer science ; Benchmarking ; Computational biology ; Data mining ; Proteomics ; Biology Advanced Proteomics Techniques and Applications ; Microbial Community Ecology and Physiology ; Metabolomics and Mass Spectrometry Studies
Authors: Yan Jer Ng | Pei En Tham | Kuan Shiong Khoo | Chin Kui Cheng | Kit Wayne Chew | Pau Loke Show
Source: Bioprocess and Biosystems Engineering (review, 2021-05-19)
Field: Chemistry — Inorganic Chemistry

A comprehensive review on the techniques for coconut oil extraction and its application Coconut oil ; Extraction (chemistry) ; Lauric acid ; Supercritical carbon dioxide ; Pulp and paper industry ; Supercritical fluid extraction ; Industrial and production engineering ; Fermentation ; Biotechnology ; Chromatography ; Environmental science ; Waste management ; Chemistry ; Food science ; Engineering ; Biology ; Organic chemistry ; Fatty acid Coconut Research and Applications ; Biodiesel Production and Applications ; Advanced Chemical Sensor Technologies
Authors: Simon Wengert | Gábor Cśanyi | Karsten Reuter | Johannes T. Margraf
Source: Chemical Science (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Data-efficient machine learning for molecular crystal structure prediction The combination of modern machine learning (ML) approaches with high-quality data from quantum mechanical (QM) calculations can yield models with an unrivalled accuracy/cost ratio. However, such methods are ultimately limited by the computational effort required to produce the reference data. In particular, reference calculations for periodic systems with many atoms can become prohibitively expensive for higher levels of theory. This trade-off is critical in the context of organic crystal structure prediction (CSP). Here, a data-efficient ML approach would be highly desirable, since screening a huge space of possible polymorphs in a narrow energy range requires the assessment of a large number of trial structures with high accuracy. In this contribution, we present tailored Δ-ML models that allow screening a wide range of crystal candidates while adequately describing the subtle interplay between intermolecular interactions such as H-bonding and many-body dispersion effects. This is achieved by enhancing a physics-based description of long-range interactions at the density functional tight binding (DFTB) level-for which an efficient implementation is available-with a short-range ML model trained on high-quality first-principles reference data. The presented workflow is broadly applicable to different molecular materials, without the need for a single periodic calculation at the reference level of theory. We show that this even allows the use of wavefunction methods in CSP. Computer science ; Context (archaeology) ; Workflow ; Crystal structure prediction ; Range (aeronautics) ; Reference data ; Wave function ; Quality (philosophy) ; Machine learning ; Artificial intelligence ; Statistical physics ; Data mining ; Chemistry ; Crystal structure ; Physics ; Materials science ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Tarak K. Patra
Source: ACS Polymers Au (review, 2021-12-28)
Field: Materials Science — Materials Chemistry

Data-Driven Methods for Accelerating Polymer Design Optimal design of polymers is a challenging task due to their enormous chemical and configurational space. Recent advances in computations, machine learning, and increasing trends in data and software availability can potentially address this problem and accelerate the molecular-scale design of polymers. Here, the central problem of polymer design is reviewed, and the general ideas of data-driven methods and their working principles in the context of polymer design are discussed. This Review provides a historical perspective and a summary of current trends and outlines future scopes of data-driven methods for polymer research. A few representative case studies on the use of such data-driven methods for discovering new polymers with exceptional properties are presented. Moreover, attempts are made to highlight how data-driven strategies aid in establishing new correlations and advancing the fundamental understanding of polymers. This Review posits that the combination of machine learning, rapid computational characterization of polymers, and availability of large open-sourced homogeneous data will transform polymer research and development over the coming decades. It is hoped that this Review will serve as a useful reference to researchers who wish to develop and deploy data-driven methods for polymer research and education. Context (archaeology) ; Computer science ; Data science ; Open research ; Scale (ratio) ; Perspective (graphical) ; Task (project management) ; Management science ; Systems engineering ; Artificial intelligence ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Viviana Bressi | Angelo Ferlazzo | Daniela Iannazzo | Claudia Espro
Source: Nanomaterials (review, 2021-04-26)
Field: Materials Science — Materials Chemistry

Graphene Quantum Dots by Eco-Friendly Green Synthesis for Electrochemical Sensing: Recent Advances and Future Perspectives The continuous decrease in the availability of fossil resources, along with an evident energy crisis, and the growing environmental impact due to their use, has pushed scientific research towards the development of innovative strategies and green routes for the use of renewable resources, not only in the field of energy production but also for the production of novel advanced materials and platform molecules for the modern chemical industry. A new class of promising carbon nanomaterials, especially graphene quantum dots (GQDs), due to their exceptional chemical-physical features, have been studied in many applications, such as biosensors, solar cells, electrochemical devices, optical sensors, and rechargeable batteries. Therefore, this review focuses on recent results in GQDs synthesis by green, easy, and low-cost synthetic processes from eco-friendly raw materials and biomass-waste. Significant advances in recent years on promising recent applications in the field of electrochemical sensors, have also been discussed. Finally, challenges and future perspectives with possible research directions in the topic are briefly summarized. Nanotechnology ; Renewable energy ; Environmentally friendly ; Graphene ; Computer science ; Materials science ; Engineering ; Electrical engineering Carbon and Quantum Dots Applications ; Quantum Dots Synthesis And Properties ; Nanocluster Synthesis and Applications
Authors: Christos Liosis | Athina Papadopoulou | Evangelos Karvelas | Theodoros E. Karakasidis | Ioannis E. Sarris
Source: Materials (review, 2021-12-07)
Field: Materials Science — Materials Chemistry

Heavy Metal Adsorption Using Magnetic Nanoparticles for Water Purification: A Critical Review Research on contamination of groundwater and drinking water is of major importance. Due to the rapid and significant progress in the last decade in nanotechnology and its potential applications to water purification, such as adsorption of heavy metal ion from contaminated water, a wide number of articles have been published. An evaluating frame of the main findings of recent research on heavy metal removal using magnetic nanoparticles, with emphasis on water quality and method applicability, is presented. A large number of articles have been studied with a focus on the synthesis and characterization procedures for bare and modified magnetic nanoparticles as well as on their adsorption capacity and the corresponding desorption process of the methods are presented. The present review analysis shows that the experimental procedures demonstrate high adsorption capacity for pollutants from aquatic solutions. Moreover, reuse of the employed nanoparticles up to five times leads to an efficiency up to 90%. We must mention also that in some rare occasions, nanoparticles have been reused up to 22 times. Adsorption ; Magnetic nanoparticles ; Reuse ; Nanoparticle ; Groundwater ; Desorption ; Contamination ; Contaminated water ; Metal ions in aqueous solution ; Materials science ; Water quality ; Water treatment ; Pollutant ; Metal ; Environmental science ; Portable water purification ; Nanotechnology ; Environmental chemistry ; Environmental engineering ; Chemistry ; Waste management ; Engineering ; Metallurgy ; Organic chemistry Nanoparticles: synthesis and applications ; Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal
Authors: Mirjam van Bentum | Matthias Selbach
Source: Molecular & Cellular Proteomics (review, 2021-01-01)
Field: Chemistry — Spectroscopy

An Introduction to Advanced Targeted Acquisition Methods Targeted proteomics via selected reaction monitoring (SRM) or parallel reaction monitoring (PRM) enables fast and sensitive detection of a preselected set of target peptides. However, the number of peptides that can be monitored in conventional targeting methods is usually rather small. Recently, a series of methods has been described that employ intelligent acquisition strategies to increase the efficiency of mass spectrometers to detect target peptides. These methods are based on one of two strategies. First, retention time adjustment-based methods enable intelligent scheduling of target peptide retention times. These include Picky, iRT, as well as spike-in free real-time adjustment methods such as MaxQuant.Live. Second, in spike-in triggered acquisition methods such as SureQuant, Pseudo-PRM, TOMAHAQ, and Scout-MRM, targeted scans are initiated by abundant labeled synthetic peptides added to samples before the run. Both strategies enable the mass spectrometer to better focus data acquisition time on target peptides. This either enables more sensitive detection or a higher number of targets per run. Here, we provide an overview of available advanced targeting methods and highlight their intrinsic strengths and weaknesses and compatibility with specific experimental setups. Our goal is to provide a basic introduction to advanced targeting methods for people starting to work in this field. Computer science ; Data acquisition ; Selected reaction monitoring ; Mass spectrometry ; Real-time computing ; Computer hardware ; Chemistry ; Tandem mass spectrometry ; Chromatography ; Operating system Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Samreen Naeem | Aqib Ali | Christophe Chesneau | M. H. Tahir | Farrukh Jamal | Rehan Ahmad Khan Sherwani | Mahmood Ul Hassan
Source: Agronomy (article, 2021-01-30)
Field: Chemistry — Analytical Chemistry

The Classification of Medicinal Plant Leaves Based on Multispectral and Texture Feature Using Machine Learning Approach This study proposes the machine learning based classification of medical plant leaves. The total six varieties of medicinal plant leaves-based dataset are collected from the Department of Agriculture, The Islamia University of Bahawalpur, Pakistan. These plants are commonly named in English as (herbal) Tulsi, Peppermint, Bael, Lemon balm, Catnip, and Stevia and scientifically named in Latin as Ocimum sanctum, Mentha balsamea, Aegle marmelos, Melissa officinalis, Nepeta cataria, and Stevia rebaudiana, respectively. The multispectral and digital image dataset are collected via a computer vision laboratory setup. For the preprocessing step, we crop the region of the leaf and transform it into a gray level format. Secondly, we perform a seed intensity-based edge/line detection utilizing Sobel filter and draw five regions of observations. A total of 65 fused features dataset is extracted, being a combination of texture, run-length matrix, and multi-spectral features. For the feature optimization process, we employ a chi-square feature selection approach and select 14 optimized features. Finally, five machine learning classifiers named as a multi-layer perceptron, logit-boost, bagging, random forest, and simple logistic are deployed on an optimized medicinal plant leaves dataset, and it is observed that the multi-layer perceptron classifier shows a relatively promising accuracy of 99.01% as compared to the competition. The distinct classification accuracy by the multi-layer perceptron classifier on six medicinal plant leaves are 99.10% for Tulsi, 99.80% for Peppermint, 98.40% for Bael, 99.90% for Lemon balm, 98.40% for Catnip, and 99.20% for Stevia. Artificial intelligence ; Pattern recognition (psychology) ; Melissa officinalis ; Computer science ; Feature selection ; Perceptron ; Machine learning ; Mathematics ; Botany ; Artificial neural network ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote-Sensing Image Classification