jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Guangzhao Qin | Yi Wei | Linfeng Yu | Jinyuan Xu | Joshua Ojih | Alejandro Rodriguez | Huimin Wang | Zhenzhen Qin | Ming Hu
Source: Journal of Materials Chemistry A (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Predicting lattice thermal conductivity from fundamental material properties using machine learning techniques The well-trained machine learning models successfully capture the inherent correlation between fundamental properties and thermal conductivity for different types of materials, providing powerful tool for advanced thermal materials screening. Thermal conductivity ; Materials science ; Thermal ; Lattice (music) ; Computer science ; Artificial intelligence ; Machine learning ; Composite material ; Physics ; Thermodynamics ; Acoustics Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Thermoelectric Materials and Devices
Authors: Jiyu Cui | Fang Wu | Wen Zhang | Lifeng Yang | Jianbo Hu | Fang Yin | Peng Ye | Qiang Zhang | Xian Suo | Yiming Mo | Xili Cui | Huajun Chen | Huabin Xing
Source: Nature Communications (article, 2023-11-03)
Field: Chemistry — Inorganic Chemistry

Direct prediction of gas adsorption via spatial atom interaction learning Physisorption relying on crystalline porous materials offers prospective avenues for sustainable separation processes, greenhouse gas capture, and energy storage. However, the lack of end-to-end deep learning model for adsorption prediction confines the rapid and precise screen of crystalline porous materials. Here, we present DeepSorption, a spatial atom interaction learning network that realizes accurate, fast, and direct structure-adsorption prediction with only information of atomic coordinate and chemical element types. The breakthrough in prediction is attributed to the awareness of global structure and local spatial atom interactions endowed by the developed Matformer, which provides the intuitive visualization of atomic-level thinking and executing trajectory in crystalline porous materials prediction. Complete adsorption curves prediction could be performed using DeepSorption with a higher accuracy than Grand canonical Monte Carlo simulation and other machine learning models, a 20-35% decline in the mean absolute error compared to graph neural network CGCNN and machine learning models based on descriptors. Since the established direct associations between raw structure and target functions are based on the understanding of the fundamental chemistry of interatomic interactions, the deep learning network is rationally universal in predicting the different physicochemical properties of various crystalline materials. Physisorption ; Adsorption ; Atom (system on chip) ; Computer science ; Artificial neural network ; Machine learning ; Artificial intelligence ; Materials science ; Biological system ; Chemistry ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Covalent Organic Framework Applications
Authors: Kai Zhu | Xin Xu | Bing Yan
Source: Advanced Healthcare Materials (article, 2023-02-11)
Field: Materials Science — Materials Chemistry

Ratio Fluorescent Detecting of Tryptophan and Its Metabolite 5‐Hydroxyindole‐3‐acetic Acid Relevant with Depression via Tb(III) Modified HOFs Hybrids: Further Designing Recyclable Molecular Logic Gate Connected by Back Propagation Neural Network Exploring intelligent fluorescent materials with high reliability and precision to diagnose diseases is significant but remains a great challenge. Herein, based on coordination post-synthetic modification, a Tb<sup>3+</sup> functionalized ME-PA (Tb@1) is prepared, which can emit brilliant green fluorescence through ligand-to-mental charge transfer-assisted energy transfer (LMCT-ET) process from ME-PA to Tb<sup>3+</sup> ions. Tb@1 can simultaneously distinguish Tryptophan (Try) and its metabolite 5-hydroxyindole-3-acetic acid (5-HIAA), two effective indicators for depression, in ratio and colorimetric mode. And this sensor behaves the advantages of high efficiency and sensitivity, as well as excellent reusability and anti-interference. The PET process from ME to Try and 5-HIAA, and the competitive absorption between analytes and Tb@1 may be relevant to sensing mechanism. In realistic serum or urine environment, the detection limits of Tb@1 for Try and 5-HIAA are 0.0183 and 0.0149 mg L<sup>-1</sup> respectively. Moreover, in conjunction with back propagation neural network (BPNN), two dual-output molecular logic gates that can be calculated circularly are further designed, which realizes intelligent control of the electronic component to identify the existence of two biomarkers and judge their concentrations from fluorescence images. This work offers a novel approach to modulate logic circuits based on ML-assisted HOF fluorescent sensor, with promising application for a precise and pictorial depression diagnosis. Fluorescence ; Analyte ; Metabolite ; Tryptophan ; Absorption (acoustics) ; Combinatorial chemistry ; Materials science ; Graphene ; Computer science ; Chemistry ; Nanotechnology ; Chromatography ; Biochemistry ; Amino acid ; Physics Luminescence and Fluorescent Materials ; Molecular Sensors and Ion Detection ; Analytical Chemistry and Sensors
Authors: Thijs Stuyver | Kjell Jorner | Connor W. Coley
Source: Scientific Data (article, 2023-02-01)
Field: Chemistry — Organic Chemistry

Reaction profiles for quantum chemistry-computed [3 + 2] cycloaddition reactions Bio-orthogonal click chemistry based on [3 + 2] dipolar cycloadditions has had a profound impact on the field of biochemistry and significant effort has been devoted to identify promising new candidate reactions for this purpose. To gauge whether a prospective reaction could be a suitable bio-orthogonal click reaction, information about both on- and off-target activation and reaction energies is highly valuable. Here, we use an automated workflow, based on the autodE program, to compute over 5000 reaction profiles for [3 + 2] cycloadditions involving both synthetic dipolarophiles and a set of biologically-inspired structural motifs. Based on a succinct benchmarking study, the B3LYP-D3(BJ)/def2-TZVP//B3LYP-D3(BJ)/def2-SVP level of theory was selected for the DFT calculations, and standard conditions and an (aqueous) SMD model were imposed to mimic physiological conditions. We believe that this data, as well as the presented workflow for high-throughput reaction profile computation, will be useful to screen for new bio-orthogonal reactions, as well as for the development of novel machine learning models for the prediction of chemical reactivity more broadly. Cycloaddition ; Workflow ; Click chemistry ; Reactivity (psychology) ; Computer science ; Chemistry ; Computational chemistry ; Quantum chemistry ; Benchmarking ; Reaction mechanism ; Combinatorial chemistry ; Computational science ; Biochemical engineering ; Organic chemistry ; Database ; Catalysis Click Chemistry and Applications ; Chemical Synthesis and Analysis ; Synthesis and Characterization of Pyrroles
Authors: S. Saravanakumar | S. Sathiyamurthy | P. Pathmanaban | P Devi
Source: Composite Interfaces (article, 2023-09-20)
Field: Materials Science — Polymers and Plastics

Integrating machine learning and response surface methodology for analyzing anisotropic mechanical properties of biocomposites ABSTRACTThis study enhances the anisotropic mechanical properties of banana fiber-epoxy composites by optimizing fiber loading, orientation, and treatment using Response Surface Methodology (RSM) and Artificial Neural Network (ANN). RSM suggests optimal values: fiber loading at 33 wt%, NaOH treatment at 6.8 wt%, and fiber orientation at 15 degrees. This material has exceptional mechanical characteristics, including a maximum tensile strength (TLS) of 31.72 MPa, a maximum flexural strength (FLS) of 42.86 MPa, and a maximum impact strength (IPS) of 38.56 kJm-2. ANN effectively predicts strengths with high R2 scores of 0.969, 0.984, and 0.954 for tensile, flexural, and impact strengths. Incorporating batch normalization and dropout layers enhances robustness. The study concludes that NaOH treatment and fiber orientation significantly impact the composite's anisotropy.KEYWORDS: ANNbiocompositesfiber orientationalkali treatmentanisotropic behaviormechanical propertiesresponse surface methodology (RSM) AcknowledgementsThe authors would like to acknowledge the scheme of Innovation, Technology Development, and Deployment (1819) of the Department of Science and Technology (DST) - Delhi.Disclosure statementNo potential conflict of interest was reported by the author(s).Author contributionAuthor 1: Corresponding AuthorAuthor 2: Research GuideAuthor 3: Machine Learning Prediction model developed. Response surface methodology ; Materials science ; Ultimate tensile strength ; Anisotropy ; Flexural strength ; Composite material ; Artificial neural network ; Izod impact strength test ; Composite number ; Robustness (evolution) ; Surface modification ; Epoxy ; Machine learning ; Computer science ; Mechanical engineering Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research ; Bamboo properties and applications
Authors: Xiao Wang | Genki Terashi | Daisuke Kihara
Source: Nature Methods (article, 2023-10-02)
Field: Materials Science — Materials Chemistry

CryoREAD: de novo structure modeling for nucleic acids in cryo-EM maps using deep learning Nucleic acid ; Computational biology ; Cryo-electron microscopy ; Deep learning ; Biology ; Computer science ; Artificial intelligence ; Biophysics ; Biochemistry Enzyme Structure and Function ; Advanced Electron Microscopy Techniques and Applications ; RNA and protein synthesis mechanisms
Authors: Tariq Aziz | Arshad Farid | Fazal Haq | Mehwish Kiran | Naveed Ullah | Shah Faisal | Amjad Ali | Farman Ullah Khan | Siming You | Awais Bokhari | Muhammad Mubashir | Lai Fatt Chuah | Pau Loke Show
Source: Environmental Research (article, 2023-01-23)
Field: Materials Science — Biomaterials

Role of silica-based porous cellulose nanocrystals in improving water absorption and mechanical properties Epoxy ; Materials science ; Thermogravimetric analysis ; Nanocomposite ; Absorption of water ; Composite material ; Fourier transform infrared spectroscopy ; Thermosetting polymer ; Polymer ; Adhesive ; Composite number ; Chemical engineering Advanced Cellulose Research Studies ; Aerogels and thermal insulation ; Natural Fiber Reinforced Composites
Authors: Zexi Zhang | Kaixuan Chen | Bruno Améduri | Mao Chen
Source: Chemical Reviews (review, 2023-10-31)
Field: Chemistry — Organic Chemistry

Fluoropolymer Nanoparticles Synthesized via Reversible-Deactivation Radical Polymerizations and Their Applications Fluorinated polymeric nanoparticles (FPNPs) combine unique properties of fluorocarbon and polymeric nanoparticles, which has stimulated massive interest for decades. However, fluoropolymers are not readily available from nature, resulting in synthetic developments to obtain FPNPs via free radical polymerizations. Recently, while increasing cutting-edge directions demand tailored FPNPs, such materials have been difficult to access via conventional approaches. Reversible-deactivation radical polymerizations (RDRPs) are powerful methods to afford well-defined polymers. Researchers have applied RDRPs to the fabrication of FPNPs, enabling the construction of particles with improved complexity in terms of structure, composition, morphology, and functionality. Related examples can be classified into three categories. First, well-defined fluoropolymers synthesized via RDRPs have been utilized as precursors to form FPNPs through self-folding and solution self-assembly. Second, thermally and photoinitiated RDRPs have been explored to realize in situ preparations of FPNPs with varied morphologies via polymerization-induced self-assembly and cross-linking copolymerization. Third, grafting from inorganic nanoparticles has been investigated based on RDRPs. Importantly, those advancements have promoted studies toward promising applications, including magnetic resonance imaging, biomedical delivery, energy storage, adsorption of perfluorinated alkyl substances, photosensitizers, and so on. This Review should present useful knowledge to researchers in polymer science and nanomaterials and inspire innovative ideas for the synthesis and applications of FPNPs. Chemistry ; Nanoparticle ; Nanomaterials ; Nanotechnology ; Polymer ; Fluoropolymer ; Polymerization ; Supramolecular chemistry ; Copolymer ; Self-assembly ; Combinatorial chemistry ; Organic chemistry ; Molecule ; Materials science Advanced Polymer Synthesis and Characterization ; Surface Modification and Superhydrophobicity ; Polymer Nanocomposite Synthesis and Irradiation
Authors: Ji Yang | Peng Wang | Helfried Neumann | Ralf Jackstell | Matthias Beller
Source: Industrial Chemistry and Materials (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Industrially applied and relevant transformations of 1,3-butadiene using homogeneous catalysts In this review, the use of 1,3-butadiene as a comparably cheap and abundant raw material to harvest fine and bulk chemicals is summarized in detail. Homogeneous ; Raw material ; 1,3-Butadiene ; Catalysis ; Process engineering ; Materials science ; Environmental science ; Waste management ; Chemical engineering ; Biochemical engineering ; Chemistry ; Organic chemistry ; Engineering ; Physics ; Thermodynamics Asymmetric Hydrogenation and Catalysis ; Carbon dioxide utilization in catalysis ; Catalysis for Biomass Conversion
Authors: Xiangyu Huang | Pejman Hadi | Ritika Joshi | Abdulrahman G. Alhamzani | Benjamin S. Hsiao
Source: ACS Omega (article, 2023-02-23)
Field: Materials Science — Biomaterials

A Comparative Study of Mechanism and Performance of Anionic and Cationic Dialdehyde Nanocelluloses for Dye Adsorption and Separation In this study, anionic dialdehyde cellulose (DAC) and cationic dialdehyde cellulose (c-DAC) nanofibrous adsorbents were prepared via a two-step reaction from bamboo pulp, using sodium periodate and Girard's reagent T as oxidizing and cationizing agents, respectively. The performance of DAC and c-DAC for selective dye adsorption and separation was evaluated by six different organic dyes (with varying charge properties) and certain binary mixtures. Both adsorbents could remove the dyes but with different capability, where DAC exhibited high adsorption efficiency against cationic dyes (e.g., the maximum adsorption capacity for Bismarck brown Y was 552.1 mg/g) and c-DAC exhibited high adsorption efficiency against anionic dyes (e.g., the maximum adsorption capacity for Congo red was 540.3 mg/g). To investigate the adsorption mechanism for these adsorbents, effects of contact time, initial pH value, initial dye concentration, and ionic strength on the adsorption activity against Congo red were investigated. The adsorption equilibrium data of DAC were found to fit best with the Langmuir isotherm model, whereas that of c-DAC were found to fit best with the Freundlich model. Both DAC and c-DAC adsorption kinetic data could be described by the pseudo-second-order kinetic model, and these adsorbents possessed stable adsorption efficiency in the pH range of 4-10. Furthermore, their dye adsorption capabilities were found to increase with increasing ionic strength (salt concentration). The distinctive complementary features of DAC and c-DAC will allow them to remove a wide range of organic dyes from industrial wastewater. Cationic polymerization ; Adsorption ; Mechanism (biology) ; Chemistry ; Separation (statistics) ; Chemical engineering ; Polymer chemistry ; Materials science ; Organic chemistry ; Computer science ; Engineering ; Philosophy Advanced Cellulose Research Studies ; Aerogels and thermal insulation ; Dyeing and Modifying Textile Fibers
Authors: K. A. Esther Jebakumari | N. K. Murugasenapathi | Tamilarasan Palanisamy
Source: Biosensors (review, 2023-01-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Engineered Two-Dimensional Nanostructures as SERS Substrates for Biomolecule Sensing: A Review Two-dimensional nanostructures (2DNS) attract tremendous interest and have emerged as potential materials for a variety of applications, including biomolecule sensing, due to their high surface-to-volume ratio, tuneable optical and electronic properties. Advancements in the engineering of 2DNS and associated technologies have opened up new opportunities. Surface-enhanced Raman scattering (SERS) is a rapid, highly sensitive, non-destructive analytical technique with exceptional signal amplification potential. Several structurally and chemically engineered 2DNS with added advantages (e.g., π-π* interaction), over plasmonic SERS substrates, have been developed specifically towards biomolecule sensing in a complex matrix, such as biological fluids. This review focuses on the recent developments of 2DNS-SERS substrates for biomolecule sensor applications. The recent advancements in engineered 2DNS, particularly for SERS substrates, have been systematically surveyed. In SERS substrates, 2DNS are used as either a standalone signal enhancer or as support for the dispersion of plasmonic nanostructures. The current challenges and future opportunities in this synergetic combination have also been discussed. Given the prospects in the design and preparation of newer 2DNS, this review can give a critical view on the current status, challenges and opportunities to extrapolate their applications in biomolecule detection. Biomolecule ; Nanotechnology ; Materials science ; Plasmon ; Nanostructure ; Raman scattering ; Raman spectroscopy ; Optoelectronics ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Extracellular vesicles in disease
Authors: Syed Imdadul Hossain | Ekaterina A. Kukushkina | Margherita Izzi | Maria Chiara Sportelli | Rosaria Anna Picca | Nicoletta Ditaranto | Nicola Cioffi
Source: Nanomaterials (review, 2023-02-24)
Field: Materials Science — Materials Chemistry

A Review on Montmorillonite-Based Nanoantimicrobials: State of the Art One of the crucial challenges of our time is to effectively use metal and metal oxide nanoparticles (NPs) as an alternative way to combat drug-resistant infections. Metal and metal oxide NPs such as Ag, Ag<sub>2</sub>O, Cu, Cu<sub>2</sub>O, CuO, and ZnO have found their way against antimicrobial resistance. However, they also suffer from several limitations ranging from toxicity issues to resistance mechanisms by complex structures of bacterial communities, so-called biofilms. In this regard, scientists are urgently looking for convenient approaches to develop heterostructure synergistic nanocomposites which could overcome toxicity issues, enhance antimicrobial activity, improve thermal and mechanical stability, and increase shelf life. These nanocomposites provide a controlled release of bioactive substances into the surrounding medium, are cost effective, reproducible, and scalable for real life applications such as food additives, nanoantimicrobial coating in food technology, food preservation, optical limiters, the bio medical field, and wastewater treatment application. Naturally abundant and non-toxic Montmorillonite (MMT) is a novel support to accommodate NPs, due to its negative surface charge and control release of NPs and ions. At the time of this review, around 250 articles have been published focusing on the incorporation of Ag-, Cu-, and ZnO-based NPs into MMT support and thus furthering their introduction into polymer matrix composites dominantly used for antimicrobial application. Therefore, it is highly relevant to report a comprehensive review of Ag-, Cu-, and ZnO-modified MMT. This review provides a comprehensive overview of MMT-based nanoantimicrobials, particularly dealing with preparation methods, materials characterization, and mechanisms of action, antimicrobial activity on different bacterial strains, real life applications, and environmental and toxicity issues. Nanocomposite ; Materials science ; Nanotechnology ; Montmorillonite ; Antimicrobial ; Oxide ; Coating ; Chemistry ; Composite material ; Metallurgy ; Organic chemistry Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Nanotechnology research and applications
Authors: Luca M. Ghiringhelli | Carsten Baldauf | Tristan Bereau | Sándor Brockhauser | Christian Carbogno | Javad Chamanara | Stefano Cozzini | Stefano Curtarolo | Claudia Draxl | Shyam Dwaraknath | Ádám Fekete | James R. Kermode | Christoph T. Koch | Markus Kühbach | Alvin Noe Ladines | Patrick Lambrix | Maja-Olivia Himmer | Sergey V. Levchenko | Micael J. T. Oliveira | Adam A. L. Michalchuk∞ | Ronald E. Miller | Berk Onat | P. Pavone | Giovanni Pizzi | Benjamin Regler | Gian‐Marco Rignanese | Jörg Schaarschmidt | Markus Scheidgen | A. Schneidewind | Tatyana Sheveleva | Chuanxun Su | Denis Usvyat | Ómar Valsson | Christof Wöll | Matthias Scheffler
Source: Scientific Data (article, 2023-09-14)
Field: Materials Science — Materials Chemistry

Shared metadata for data-centric materials science The expansive production of data in materials science, their widespread sharing and repurposing requires educated support and stewardship. In order to ensure that this need helps rather than hinders scientific work, the implementation of the FAIR-data principles (Findable, Accessible, Interoperable, and Reusable) must not be too narrow. Besides, the wider materials-science community ought to agree on the strategies to tackle the challenges that are specific to its data, both from computations and experiments. In this paper, we present the result of the discussions held at the workshop on “Shared Metadata and Data Formats for Big-Data Driven Materials Science”. We start from an operative definition of metadata, and the features that a FAIR-compliant metadata schema should have. We will mainly focus on computational materials-science data and propose a constructive approach for the FAIRification of the (meta)data related to ground-state and excited-states calculations, potential-energy sampling, and generalized workflows. Finally, challenges with the FAIRification of experimental (meta)data and materials-science ontologies are presented together with an outlook of how to meet them. Metadata ; Computer science ; Interoperability ; Workflow ; Data sharing ; Data science ; Data curation ; Big data ; World Wide Web ; Database ; Data mining Machine Learning in Materials Science ; Research Data Management Practices ; Scientific Computing and Data Management