jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ruichan Lv | Micah Raab | Yanxing Wang | Jie Tian | Jun Lin | Paras N. Prasad
Source: Coordination Chemistry Reviews (article, 2022-03-09)
Field: Materials Science — Materials Chemistry

Nanochemistry advancing photon conversion in rare-earth nanostructures for theranostics Luminescence ; Nanotechnology ; Nanochemistry ; Photon upconversion ; Materials science ; Nanophotonics ; Optoelectronics Luminescence Properties of Advanced Materials ; Nanoplatforms for cancer theranostics ; Photoacoustic and Ultrasonic Imaging
Authors: Christoph B. Messner | Vadim Demichev | Ziyue Wang | Johannes Hartl | Georg Kustatscher | Michael Mülleder | Markus Ralser
Source: PROTEOMICS (review, 2022-11-09)
Field: Chemistry — Spectroscopy

Mass spectrometry‐based high‐throughput proteomics and its role in biomedical studies and systems biology There are multiple reasons why the next generation of biological and medical studies require increasing numbers of samples. Biological systems are dynamic, and the effect of a perturbation depends on the genetic background and environment. As a consequence, many conditions need to be considered to reach generalizable conclusions. Moreover, human population and clinical studies only reach sufficient statistical power if conducted at scale and with precise measurement methods. Finally, many proteins remain without sufficient functional annotations, because they have not been systematically studied under a broad range of conditions. In this review, we discuss the latest technical developments in mass spectrometry (MS)-based proteomics that facilitate large-scale studies by fast and efficient chromatography, fast scanning mass spectrometers, data-independent acquisition (DIA), and new software. We further highlight recent studies which demonstrate how high-throughput (HT) proteomics can be applied to capture biological diversity, to annotate gene functions or to generate predictive and prognostic models for human diseases. Proteomics ; Computational biology ; Mass spectrometry ; Computer science ; Throughput ; Systems biology ; Biology ; Data science ; Chemistry ; Chromatography ; Gene ; Genetics Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Peihua Yang | Jin‐Lin Yang | Kang Liu | Hong Jin Fan
Source: ACS Nano (review, 2022-09-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hydrogels Enable Future Smart Batteries The growing trend of intelligent devices ranging from wearables and soft robots to artificial intelligence has set a high demand for smart batteries. Hydrogels provide opportunities for smart batteries to self-adjust their functions according to the operation conditions. Despite the progress in hydrogel-based smart batteries, a gap remains between the designable functions of diverse hydrogels and the expected performance of batteries. In this Perspective, we first briefly introduce the fundamentals of hydrogels, including formation, structure, and characteristics of the internal water and ions. Batteries that operate under unusual mechanical and temperature conditions enabled by hydrogels are highlighted. Challenges and opportunities for further development of hydrogels are outlined to propose future research in smart batteries toward all-climate power sources and intelligent wearables. Self-healing hydrogels ; Wearable computer ; Wearable technology ; Computer science ; Smart power ; Nanotechnology ; Materials science ; Embedded system ; Power (physics) Supercapacitor Materials and Fabrication ; Advanced battery technologies research ; Advanced Sensor and Energy Harvesting Materials
Authors: Qaisar Abbas | Pragati A. Shinde | Mohammad Ali Abdelkareem | Abdul Hai Alami | Mojtaba Mirzaeian | Arti Yadav | A.G. Olabi
Source: Materials (review, 2022-11-04)
Field: Materials Science — Materials Chemistry

Graphene Synthesis Techniques and Environmental Applications Graphene is fundamentally a two-dimensional material with extraordinary optical, thermal, mechanical, and electrical characteristics. It has a versatile surface chemistry and large surface area. It is a carbon nanomaterial, which comprises sp<sup>2</sup> hybridized carbon atoms placed in a hexagonal lattice with one-atom thickness, giving it a two-dimensional structure. A large number of synthesis techniques including epitaxial growth, liquid phase exfoliation, electrochemical exfoliation, mechanical exfoliation, and chemical vapor deposition are used for the synthesis of graphene. Graphene prepared using different techniques can have a number of benefits and deficiencies depending on its application. This study provides a summary of graphene preparation techniques and critically assesses the use of graphene, its derivates, and composites in environmental applications. These applications include the use of graphene as membrane material for the detoxication and purification of water, active material for gas sensing, heavy metal ions detection, and CO<sub>2</sub> conversion. Furthermore, a trend analysis of both synthesis techniques and environmental applications of graphene has been performed by extracting and analyzing Scopus data from the past ten years. Finally, conclusions and outlook are provided to address the residual challenges related to the synthesis of the material and its use for environmental applications. Graphene ; Materials science ; Exfoliation joint ; Nanomaterials ; Nanotechnology ; Chemical vapor deposition ; Carbon fibers ; Graphene oxide paper ; Composite material ; Composite number Graphene research and applications ; Graphene and Nanomaterials Applications ; Supercapacitor Materials and Fabrication
Authors: Vishal Dutta | Ritesh Verma | C. Gopalkrishnan | Min-Hao Yuan | Khalid Mujasam Batoo | R. Jayavel | Ankush Chauhan | Kun‐Yi Andrew Lin | Balasubramani Ravindran | Suresh Ghotekar
Source: Inorganics (review, 2022-10-10)
Field: Materials Science — Materials Chemistry

Bio-Inspired Synthesis of Carbon-Based Nanomaterials and Their Potential Environmental Applications: A State-of-the-Art Review Providing safe drinking water and clean water is becoming a more challenging task all around the world. Although some critical issues and limits remain unsolved, implementing ecologically sustainable nanomaterials (NMs) with unique features, e.g., highly efficient and selective, earth-abundance, renewability, low-cost manufacturing procedures, and stability, has become a priority. Carbon nanoparticles (NPs) offer tremendous promise in the sectors of energy and the environment. However, a series of far more ecologically friendly synthesis techniques based on natural, renewable, and less expensive waste resources must be explored. This will reduce greenhouse gas emissions and harmful material extraction and assist the development of green technologies. The progress achieved in the previous 10 years in the fabrication of novel carbon-based NMs utilizing waste materials as well as natural precursors is reviewed in this article. Research on carbon-based NPs and their production using naturally occurring precursors and waste materials focuses on this review research. Water treatment and purification using carbon NMs, notably for industrial and pharmaceutical wastes, has shown significant potential. Research in this area focuses on enhanced carbonaceous NMs, methods, and novel nano-sorbents for wastewater, drinking water, groundwater treatment, as well as ionic metal removal from aqueous environments. Discussed are the latest developments and challenges in environmentally friendly carbon and graphene quantum dot NMs. Environmentally friendly ; Greenhouse gas ; Environmental science ; Renewable energy ; Waste management ; Nanotechnology ; Biochemical engineering ; Materials science ; Engineering Carbon and Quantum Dots Applications ; Graphene and Nanomaterials Applications ; Graphene research and applications
Authors: Aditya Nandy | Gianmarco Terrones | N. Arunachalam | Chenru Duan | David W. Kastner | Heather J. Kulik
Source: Scientific Data (article, 2022-03-11)
Field: Chemistry — Inorganic Chemistry

MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks We report a workflow and the output of a natural language processing (NLP)-based procedure to mine the extant metal-organic framework (MOF) literature describing structurally characterized MOFs and their solvent removal and thermal stabilities. We obtain over 2,000 solvent removal stability measures from text mining and 3,000 thermal decomposition temperatures from thermogravimetric analysis data. We assess the validity of our NLP methods and the accuracy of our extracted data by comparing to a hand-labeled subset. Machine learning (ML, i.e. artificial neural network) models trained on this data using graph- and pore-geometry-based representations enable prediction of stability on new MOFs with quantified uncertainty. Our web interface, MOFSimplify, provides users access to our curated data and enables them to harness that data for predictions on new MOFs. MOFSimplify also encourages community feedback on existing data and on ML model predictions for community-based active learning for improved MOF stability models. Workflow ; Computer science ; Stability (learning theory) ; Artificial neural network ; Artificial intelligence ; Extant taxon ; Thermal stability ; Machine learning ; Data mining ; Deep learning ; Database ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis
Authors: Saichon Sinsomboonthong
Source: International Journal of Mathematics and Mathematical Sciences (article, 2022-04-22)
Field: Chemistry — Analytical Chemistry

Performance Comparison of New Adjusted Min-Max with Decimal Scaling and Statistical Column Normalization Methods for Artificial Neural Network Classification In this research, the normalization performance of the proposed adjusted min-max methods was compared to the normalization performance of statistical column, decimal scaling, adjusted decimal scaling, and min-max methods, in terms of accuracy and mean square error of the final classification outcomes. The evaluation process employed an artificial neural network classification on a large variety of widely used datasets. The best method was min-max normalization, providing 84.0187% average ranking of accuracy and 0.1097 average ranking of mean square error across all six datasets. However, the proposed adjusted-2 min-max normalization achieved a higher accuracy and a lower mean square error than min-max normalization on each of the following datasets: white wine quality, Pima Indians diabetes, vertical column, and Indian liver disease datasets. For example, the proposed adjusted-2 min-max normalization on white wine quality dataset achieved 100% accuracy and 0.00000282 mean square error. To conclude, for some classification applications on one of these specific datasets, the proposed adjusted-2 min-max normalization should be used over the other tested normalization methods because it performed better. Normalization (sociology) ; Mean squared error ; Artificial neural network ; Decimal ; Statistics ; Pattern recognition (psychology) ; Computer science ; Artificial intelligence ; Mathematics ; Arithmetic Spectroscopy and Chemometric Analyses
Authors: Shiwanka V. Wanasinghe | Obed J. Dodo | Dominik Konkolewicz
Source: Angewandte Chemie International Edition (review, 2022-09-28)
Field: Materials Science — Polymers and Plastics

Dynamic Bonds: Adaptable Timescales for Responsive Materials Dynamic bonds introduce unique properties such as self-healing, recyclability, shape memory, and malleability to polymers. Significant efforts have been made to synthesize a variety of dynamic linkers, creating a diverse library of materials. In addition to the development of new dynamic chemistries, fine-tuning of dynamic bonds has emerged as a technique to modulate dynamic properties. This Review highlights approaches for controlling the timescales of dynamic bonds in polymers. Particularly, eight dynamic bonds are considered, including urea/urethanes, boronic esters, Thiol-Michael exchange, Diels-Alder adducts, transesterification, imine bonds, coordination bonds, and hydrogen bonding. This Review emphasizes how structural modifications and external factors have been used as tools to tune the dynamic character of materials. Finally, this Review proposes strategies for tailoring the timescales of dynamic bonds in polymer materials through both kinetic effects and modulating bond thermodynamics. Bond ; Nanotechnology ; Materials science ; Computer science ; Business Polymer composites and self-healing ; Advanced Materials and Mechanics ; Synthetic Organic Chemistry Methods
Authors: Pushkar Ghanekar | Siddharth Deshpande | Jeffrey Greeley
Source: Nature Communications (article, 2022-10-02)
Field: Materials Science — Materials Chemistry

Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis Heterogeneous catalytic reactions are influenced by a subtle interplay of atomic-scale factors, ranging from the catalysts' local morphology to the presence of high adsorbate coverages. Describing such phenomena via computational models requires generation and analysis of a large space of atomic configurations. To address this challenge, we present Adsorbate Chemical Environment-based Graph Convolution Neural Network (ACE-GCN), a screening workflow that accounts for atomistic configurations comprising diverse adsorbates, binding locations, coordination environments, and substrate morphologies. Using this workflow, we develop catalyst surface models for two illustrative systems: (i) NO adsorbed on a Pt<sub>3</sub>Sn(111) alloy surface, of interest for nitrate electroreduction processes, where high adsorbate coverages combined with low symmetry of the alloy substrate produce a large configurational space, and (ii) OH* adsorbed on a stepped Pt(221) facet, of relevance to the Oxygen Reduction Reaction, where configurational complexity results from the presence of irregular crystal surfaces, high adsorbate coverages, and directionally-dependent adsorbate-adsorbate interactions. In both cases, the ACE-GCN model, trained on a fraction (~10%) of the total DFT-relaxed configurations, successfully describes trends in the relative stabilities of unrelaxed atomic configurations sampled from a large configurational space. This approach is expected to accelerate development of rigorous descriptions of catalyst surfaces under in-situ conditions. Catalysis ; Heterogeneous catalysis ; Chemical physics ; Adsorption ; Substrate (aquarium) ; Materials science ; Chemistry ; Molecular dynamics ; Nanotechnology ; Computational chemistry ; Physical chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Photocatalysis Techniques
Authors: Zhiwei Lu | Maoting Chen | Mengjiao Li | Tao Liu | Mengmeng Sun | Wu Chun | Gehong Su | Jiajian Yin | Mingjun Wu | Ping Zou | Lin Li | Xianxiang Wang | Qianming Huang | Huadong Yin | Hanbing Rao | Xinguang Zhou | Jianshan Ye | Yanying Wang
Source: Chemical Engineering Journal (article, 2022-03-15)
Field: Materials Science — Materials Chemistry

Smartphone-integrated multi-color ratiometric fluorescence portable optical device based on deep learning for visual monitoring of Cu2+ and thiram Fluorescence ; Quantum dot ; Detection limit ; Cadmium telluride photovoltaics ; Chemistry ; Thiram ; Luminescence ; Naked eye ; Analytical Chemistry (journal) ; Optoelectronics ; Materials science ; Photochemistry ; Optics ; Chromatography Carbon and Quantum Dots Applications ; Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques
Authors: Robert Freer | Dursun Ekren | Tanmoy Ghosh | Kanishka Biswas | Pengfei Qiu | Shun Wan | Lidong Chen | Shen Han | Chenguang Fu | Tiejun Zhu | A. K. M. Ashiquzzaman Shawon | Alexandra Zevalkink | Kazuki Imasato | G. Jeffrey Snyder | Melis Özen | Kıvanç Sağlık | Umut Aydemir | Raúl Cardoso‐Gil | Eteri Svanidze | Ryoji Funahashi | Anthony V. Powell | Shriparna Mukherjee | Sahil Tippireddy | Paz Vaqueiro | Franck Gascoin | Theodora Kyratsi | Philipp Sauerschnig | Takao Mori
Source: Journal of Physics Energy (article, 2022-01-10)
Field: Materials Science — Materials Chemistry

Key properties of inorganic thermoelectric materials—tables (version 1) Abstract This paper presents tables of key thermoelectric properties, which define thermoelectric conversion efficiency, for a wide range of inorganic materials. The twelve families of materials included in these tables are primarily selected on the basis of well established, internationally-recognized performance and promise for current and future applications: tellurides, skutterudites, half Heuslers, Zintls, Mg–Sb antimonides, clathrates, FeGa 3 -type materials, actinides and lanthanides, oxides, sulfides, selenides, silicides, borides and carbides. As thermoelectric properties vary with temperature, data are presented at room temperature to enable ready comparison, and also at a higher temperature appropriate to peak performance. An individual table of data and commentary are provided for each family of materials plus source references for all the data. Thermoelectric effect ; Thermoelectric materials ; Lanthanide ; Materials science ; Carbide ; Actinide ; Key (lock) ; Engineering physics ; Metallurgy ; Computer science ; Inorganic chemistry ; Thermodynamics ; Chemistry ; Engineering ; Physics Advanced Thermoelectric Materials and Devices ; Heusler alloys: electronic and magnetic properties ; Thermal Expansion and Ionic Conductivity
Authors: Elyse A. Schriber⧓ | Daniel W. Paley⧓ | Robert Bolotovsky | Daniel J. Rosenberg | Raymond G. Sierra | Andrew Aquila | Derek Mendez | Frédéric Poitevin | Johannes Blaschke | Asmit Bhowmick | Ryan P. Kelly | Mark S. Hunter | Brandon Hayes | Derek Popple | Matthew Yeung | Carina Pareja‐Rivera | Stella Lisova | Kensuke Tono | Michihiro Sugahara | Shigeki Owada | Tevye Kuykendall | Kaiyuan Yao | P. James Schuck | Diego Solís-Ibarra | Nicholas K. Sauter | Aaron S. Brewster | J. Nathan Hohman
Source: Nature (article, 2022-01-19)
Field: Materials Science — Materials Chemistry

Chemical crystallography by serial femtosecond X-ray diffraction Inorganic-organic hybrid materials represent a large share of newly reported structures, owing to their simple synthetic routes and customizable properties<sup>1</sup>. This proliferation has led to a characterization bottleneck: many hybrid materials are obligate microcrystals with low symmetry and severe radiation sensitivity, interfering with the standard techniques of single-crystal X-ray diffraction<sup>2,3</sup> and electron microdiffraction<sup>4-11</sup>. Here we demonstrate small-molecule serial femtosecond X-ray crystallography (smSFX) for the determination of material crystal structures from microcrystals. We subjected microcrystalline suspensions to X-ray free-electron laser radiation<sup>12,13</sup> and obtained thousands of randomly oriented diffraction patterns. We determined unit cells by aggregating spot-finding results into high-resolution powder diffractograms. After indexing the sparse serial patterns by a graph theory approach<sup>14</sup>, the resulting datasets can be solved and refined using standard tools for single-crystal diffraction data<sup>15-17</sup>. We describe the ab initio structure solutions of mithrene (AgSePh)<sup>18-20</sup>, thiorene (AgSPh) and tethrene (AgTePh), of which the latter two were previously unknown structures. In thiorene, we identify a geometric change in the silver-silver bonding network that is linked to its divergent optoelectronic properties<sup>20</sup>. We demonstrate that smSFX can be applied as a general technique for structure determination of beam-sensitive microcrystalline materials at near-ambient temperature and pressure. Diffraction ; Materials science ; Femtosecond ; Crystallography ; X-ray crystallography ; Microcrystalline ; Powder diffraction ; Crystal structure ; Single crystal ; Ab initio ; Electron diffraction ; Optics ; Chemistry ; Laser ; Physics ; Organic chemistry X-ray Diffraction in Crystallography ; X-ray Spectroscopy and Fluorescence Analysis ; High-pressure geophysics and materials
Authors: María Emilia Dueñas | Rachel E. Heap | Melanie Leveridge | Roland S. Annan | Frank Büttner | Matthias Trost
Source: EMBO Molecular Medicine (review, 2022-12-14)
Field: Chemistry — Spectroscopy

Advances in high‐throughput mass spectrometry in drug discovery High-throughput (HT) screening drug discovery, during which thousands or millions of compounds are screened, remains the key methodology for identifying active chemical matter in early drug discovery pipelines. Recent technological developments in mass spectrometry (MS) and automation have revolutionized the application of MS for use in HT screens. These methods allow the targeting of unlabelled biomolecules in HT assays, thereby expanding the breadth of targets for which HT assays can be developed compared to traditional approaches. Moreover, these label-free MS assays are often cheaper, faster, and more physiologically relevant than competing assay technologies. In this review, we will describe current MS techniques used in drug discovery and explain their advantages and disadvantages. We will highlight the power of mass spectrometry in label-free in vitro assays, and its application for setting up multiplexed cellular phenotypic assays, providing an exciting new tool for screening compounds in cell lines, and even primary cells. Finally, we will give an outlook on how technological advances will increase the future use and the capabilities of mass spectrometry in drug discovery. Drug discovery ; Mass spectrometry ; Throughput ; Drug ; Computational biology ; Chemistry ; Computer science ; Pharmacology ; Biology ; Chromatography ; Biochemistry Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: Dane Morgan | Ghanshyam Pilania | Adrien Couet | Blas P. Uberuaga | Cheng Sun | Ju Li
Source: Current Opinion in Solid State and Materials Science (article, 2022-01-15)
Field: Materials Science — Materials Chemistry

Machine learning in nuclear materials research Workflow ; Computer science ; Machine learning ; Artificial intelligence ; Bayesian optimization ; Scripting language ; Throughput ; Process (computing) ; Database Nuclear Materials and Properties ; Machine Learning in Materials Science ; Nuclear Physics and Applications