jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sichao Li | Jonathan Y. C. Ting | Amanda S. Barnard
Source: Journal of Computational Science (article, 2022-10-30)
Field: Materials Science — Materials Chemistry

The impact of domain-driven and data-driven feature selection on the inverse design of nanoparticle catalysts Feature selection ; Computer science ; Nanomaterial-based catalyst ; Feature (linguistics) ; Domain (mathematical analysis) ; Data-driven ; Machine learning ; Process (computing) ; Artificial intelligence ; Inverse ; Selection (genetic algorithm) ; Data mining ; Nanoparticle ; Nanotechnology ; Materials science ; Mathematics Machine Learning in Materials Science ; Neural Networks and Applications ; Gene expression and cancer classification
Authors: Anantha Venkataraman Nagarajan | Dennis Johan Loevlie | Michael Cowan | Giannis Mpourmpakis
Source: Current Opinion in Chemical Engineering (article, 2022-01-10)
Field: Materials Science — Materials Chemistry

Resolving electrocatalytic imprecision in atomically precise metal nanoclusters Nanoclusters ; Metal ; Nanotechnology ; Materials science ; Metallurgy Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Raja Ghosh | Francesco Paesani
Source: Chemical Science (review, 2022-11-16)
Field: Chemistry — Inorganic Chemistry

Connecting the dots for fundamental understanding of structure–photophysics–property relationships of COFs, MOFs, and perovskites using a Multiparticle Holstein Formalism Photoactive organic and hybrid organic-inorganic materials such as conjugated polymers, covalent organic frameworks (COFs), metal-organic frameworks (MOFs), and layered perovskites, display intriguing photophysical signatures upon interaction with light. Elucidating structure-photophysics-property relationships across a broad range of functional materials is nontrivial and requires our fundamental understanding of the intricate interplay among excitons (electron-hole pair), polarons (charges), bipolarons, phonons (vibrations), inter-layer stacking interactions, and different forms of structural and conformational defects. In parallel with electronic structure modeling and data-driven science that are actively pursued to successfully accelerate materials discovery, an accurate, computationally inexpensive, and physically-motivated theoretical model, which consistently makes quantitative connections with conceptually complicated experimental observations, is equally important. Within this context, the first part of this perspective highlights a unified theoretical framework in which the electronic coupling as well as the local coupling between the electronic and nuclear degrees of freedom can be efficiently described for a broad range of quasiparticles with similarly structured Holstein-style vibronic Hamiltonians. The second part of this perspective discusses excitonic and polaronic photophysical signatures in polymers, COFs, MOFs, and perovskites, and attempts to bridge the gap between different research fields using a common theoretical construct - the Multiparticle Holstein Formalism. We envision that the synergistic integration of state-of-the-art computational approaches with the Multiparticle Holstein Formalism will help identify and establish new, transformative design strategies that will guide the synthesis and characterization of next-generation energy materials optimized for a broad range of optoelectronic, spintronic, and photonic applications. Formalism (music) ; Property (philosophy) ; Nanotechnology ; Quantum dot ; Materials science ; Physics ; Epistemology ; Philosophy Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques ; Perovskite Materials and Applications
Authors: Alfredo Pereira | Camilo Albornoz | Oleksandra S. Trofymchuk
Source: Organometallics (article, 2022-04-29)
Field: Materials Science — Materials Chemistry

Data-Driven Analysis of Reactions Catalyzed by [CoCp*(CO)I<sub>2</sub>] The combination of the [Cp*Co(CO)I2] catalyst with various additives and solvents at different temperatures for different durations has led to a considerable increase in the amount of research regarding the catalysis of C–H functionalization via high-valent Co, owing to the unique reactivity of the catalyst. Our study presents, for the first time, a data-driven approach toward reported reactions catalyzed using [Cp*Co(CO)I2]. We analyze distinct solvents, additives, reaction times, temperatures, and directing groups with their associated mean yields, which accelerates the screening process in the laboratory to yield favorable combinations. We demonstrate the effects of reaction conditions and directing groups on the reaction performance by integrating data from the reaction. Additionally, we examine the reactions based on their yields using principal component analysis and the t-stochastic neighbor embedding method. These exploratory investigations may be very useful for synthetic chemists as a starting point in catalytic studies. Catalysis ; Chemistry ; Yield (engineering) ; Reactivity (psychology) ; Reaction conditions ; Organic chemistry ; Thermodynamics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalytic C–H Functionalization Methods
Authors: Junpeng Yuan | Chang Niu | Min Li | Hui Wang
Source: Microporous and Mesoporous Materials (article, 2022-12-07)
Field: Chemistry — Inorganic Chemistry

High-throughput computational screening of adsorbents and membrane materials for acetylene capture Acetylene ; Adsorption ; Membrane ; Materials science ; Monte Carlo method ; Diffusion ; Chemical physics ; Topology (electrical circuits) ; Chemistry ; Physics ; Physical chemistry ; Thermodynamics ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Membrane Separation and Gas Transport
Authors: Yafeng Xing | Yachao Dong | Christos Goergakis | Yu Zhuang | Lei Zhang | Jian Du | Qingwei Meng
Source: AIChE Journal (article, 2022-04-05)
Field: Materials Science — Materials Chemistry

Automatic data‐driven stoichiometry identification and kinetic modeling framework for homogeneous organic reactions Abstract Data‐driven and knowledge‐driven methods are two approaches used in studying reaction kinetics. This article proposes a hybrid‐modeling framework for homogeneous synthesis reactions, which combines the advantages of high level of automation in the data‐driven approach and improved accuracy in the knowledge‐driven approach. A constrained enumeration method is proposed to generate possible candidate stoichiometries, and dynamic response surface methodology, target factor analysis, and mass balance are used together for identifying stoichiometries one‐by‐one, without the necessity of an expert‐generated candidate list. Then, the previously screened stoichiometries are formed into different groups that represent candidate reaction systems, and the group (or groups) with the greatest likelihood will be identified, based on kinetic fitting and reaction dynamic criteria. This framework has been demonstrated by several examples of different reaction systems. The true reaction stoichiometries are all correctly identified, and the accurate kinetic models are obtained, showing satisfactory performance of the proposed method. Stoichiometry ; Homogeneous ; Kinetic energy ; Automation ; Computer science ; Biological system ; Identification (biology) ; Chemistry ; Data mining ; Thermodynamics ; Physical chemistry ; Physics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Cecilia Engel Thomas | Leo Dahl | Sanna Byström | Yan Chen | Mathias Uhlén | Anders Mälarstig | Kamila Czene | Per Hall | Jochen M. Schwenk | Marike Gabrielson
Source: Translational Oncology (article, 2022-01-13)
Field: Chemistry — Spectroscopy

Circulating proteins reveal prior use of menopausal hormonal therapy and increased risk of breast cancer Accessible risk predictors are crucial for improving the early detection and prognosis of breast cancer. Blood samples are widely available and contain proteins that provide important information about human health and disease, however, little is still known about the contribution of circulating proteins to breast cancer risk prediction. We profiled EDTA plasma samples collected before diagnosis from the Swedish KARMA breast cancer cohort to evaluate circulating proteins as molecular predictors. A data-driven analysis strategy was applied to the molecular phenotypes built on 700 circulating proteins to identify and annotate clusters of women. The unsupervised analysis of 183 future breast cancer cases and 366 age-matched controls revealed five stable clusters with distinct proteomic plasma profiles. Among these women, those in the most stable cluster (N = 19; mean Jaccard index: 0.70 ± 0.29) were significantly more likely to have used menopausal hormonal therapy (MHT), get a breast cancer diagnosis, and were older compared to the remaining clusters. The circulating proteins associated with this cluster (FDR < 0.001) represented physiological processes related to cell junctions (F11R, CLDN15, ITGAL), DNA repair (RBBP8), cell replication (TJP3), and included proteins found in female reproductive tissue (PTCH1, ZP4). Using a data-driven approach on plasma proteomics data revealed the potential long-lasting molecular effects of menopausal hormonal therapy (MHT) on the circulating proteome, even after women had ended their treatment. This provides valuable insights concerning proteomics efforts to identify molecular markers for breast cancer risk prediction. Breast cancer ; Proteomics ; Cancer ; Proteome ; Medicine ; Bioinformatics ; Hormone ; Oncology ; Blood proteins ; Hormonal therapy ; Internal medicine ; Biology ; Genetics ; Gene Advanced Proteomics Techniques and Applications ; Breast Cancer Treatment Studies ; Gene expression and cancer classification
Authors: Valeriy V. Ginzburg
Source: Industrial & Engineering Chemistry Research (article, 2022-06-29)
Field: Chemistry — Organic Chemistry

Mesoscale Modeling of Micellization and Adsorption of Surfactants and Surfactant-Like Polymers in Solution: Challenges and Opportunities Surfactants and surfactant-like amphiphilic polymers play important roles in many industrial processes (e.g., home and personal care, laundry, paints and coatings, and biopharmaceuticals). In the past two decades, significant progress has been achieved in understanding and modeling crucial surfactant properties (e.g., critical micellization concentration [CMC], cloud points, adsorption, and stabilization of colloidal particles against flocculation), but many challenges still remain. In this review, several popular data-driven, atomistic, and coarse-grained modeling approaches are described that are used to study surfactant properties. Then, specific examples of how these approaches are used for specific applications are provided. Finally, opportunities and challenges for surfactant theory and modeling are discussed. Pulmonary surfactant ; Adsorption ; Flocculation ; Polymer ; Laundry ; Amphiphile ; Colloid ; Materials science ; Chemical engineering ; Critical micelle concentration ; Polymer science ; Nanotechnology ; Chemistry ; Organic chemistry ; Micelle ; Copolymer ; Aqueous solution ; Engineering ; Waste management Surfactants and Colloidal Systems ; Advanced Polymer Synthesis and Characterization ; Electrostatics and Colloid Interactions
Authors: Yuan An | Jane Greenberg | Xiaohua Hu | Alex Kalinowski | Xiao Fang | Xintong Zhao | Scott McClellan | Fernando J. Uribe‐Romo | Kyle Langlois | Jacob Furst | Diego A. Gómez‐Gualdrón | Fernando Fajardo-Rojas | Katherine Ardila | Semion K. Saikin | Corey A. Harper | Ron Daniel
Source: 2022 IEEE International Conference on Big Data (Big Data) (article, 2022-12-17)
Field: Materials Science — Materials Chemistry

Exploring Pre-Trained Language Models to Build Knowledge Graph for Metal-Organic Frameworks (MOFs) Building a knowledge graph is a time-consuming and costly process which often applies complex natural language processing (NLP) methods for extracting knowledge graph triples from text corpora. Pre-trained large Language Models (PLM) have emerged as a crucial type of approach that provides readily available knowledge for a range of AI applications. However, it is unclear whether it is feasible to construct domain-specific knowledge graphs from PLMs. Motivated by the capacity of knowledge graphs to accelerate data-driven materials discovery, we explored a set of state-of-the-art pre-trained general-purpose and domain-specific language models to extract knowledge triples for metal-organic frameworks (MOFs). We created a knowledge graph benchmark with 7 relations for 1248 published MOF synonyms. Our experimental results showed that domain-specific PLMs consistently outperformed the general-purpose PLMs for predicting MOF related triples. The overall benchmarking results, however, show that using the present PLMs to create domain-specific knowledge graphs is still far from being practical, motivating the need to develop more capable and knowledgeable pre-trained language models for particular applications in materials science. Computer science ; Benchmarking ; Knowledge graph ; Domain knowledge ; Artificial intelligence ; Graph ; Benchmark (surveying) ; Natural language processing ; Domain (mathematical analysis) ; Natural language understanding ; Knowledge extraction ; Set (abstract data type) ; Process (computing) ; Data science ; Machine learning ; Natural language ; Theoretical computer science ; Programming language Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Yuting Qiao | Lijuan Qiao | Peize Zhao | Peng Zhang | Fanbin Wu | Jiahui Zhang | Li Gao | Bingxin Liu | Lei Zhang
Source: Biosensors (article, 2022-05-24)
Field: Materials Science — Materials Chemistry

Phosphoprotein Detection in Sweat Realized by Intercalation Structure 2D@3D g-C3N4@Fe3O4 Wearable Sensitive Motif Abnormal protein phosphorylation in sweat metabolites is closely related to cancer, cardiovascular disease, and other diseases. The real-time monitoring of phosphoproteins in sweat is significant for early monitoring of disease biomarkers. Here, a high-efficiency electrochemical sensor for phosphoprotein in sweat was realized by 2D@3D g-C<sub>3</sub>N<sub>4</sub>@Fe<sub>3</sub>O<sub>4</sub> with intercalation structure. Common phosphoprotein β-Casein was selected to demonstrate the platform's functionalities. The detection limit of g-C<sub>3</sub>N<sub>4</sub>@Fe<sub>3</sub>O<sub>4</sub> could be as low as 9.7 μM, and the detection range was from 0.01 mg/mL to 1 mg/mL. In addition, the sensing platform showed good selectivity, reproducibility, and stability. We also investigated the effects of interface structure on adsorption properties and electronic properties of the g-C<sub>3</sub>N<sub>4</sub> and Fe<sub>3</sub>O<sub>4</sub> heterostructure using DFT. More electrons from Fe<sub>3</sub>O<sub>4</sub> were transferred to g-C<sub>3</sub>N<sub>4</sub>, which increased the electrons in the energy band of N atoms and promoted the formation of stable N-H bonds with H atoms in phosphoproteins. We demonstrated phosphoprotein sensor functionality by measuring the phosphoprotein in human sweat during exercising. This work realizes a sensing platform for noninvasive and continuous detection of sweat phosphoproteins in wearable devices. Phosphoprotein ; Intercalation (chemistry) ; Wearable computer ; Motif (music) ; SWEAT ; Chemistry ; Computer science ; Medicine ; Biochemistry ; Physics ; Phosphorylation ; Internal medicine ; Inorganic chemistry ; Embedded system ; Acoustics Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques ; Biosensors and Analytical Detection
Authors: Dongyang Sun | Bernard Lip Huat Saw | Amaka J. Onyianta | Bowen Wang | Callum Wilson | Dominic O’Rourke | Chan Hwang See | Carmen‐Mihaela Popescu | Mark Dorris | Islam Shyha | Zhilun Lu
Source: Journal of Advanced Dielectrics (article, 2022-11-09)
Field: Materials Science — Biomaterials

Preparation of elastomeric nanocomposites using nanocellulose and recycled alum sludge for flexible dielectric materials Flexible dielectric materials with environmental-friendly, low-cost and high-energy density characteristics are in increasing demand as the world steps into the new Industrial 4.0 era. In this work, an elastomeric nanocomposite was developed by incorporating two components: cellulose nanofibrils (CNFs) and recycled alum sludge, as the reinforcement phase and to improve the dielectric properties, in a bio-elastomer matrix. CNF and alum sludge were produced by processing waste materials that would otherwise be disposed to landfills. A biodegradable elastomer polydimethylsiloxane was used as the matrix and the nanocomposites were processed by casting the materials in Petri dishes. Nanocellulose extraction and heat treatment of alum sludge were conducted and characterized using various techniques including scanning electron microscopy (SEM), thermogravimetric analysis/derivative thermogravimetric (TGA/DTG) and X-ray diffraction (XRD) analysis. When preparing the nanocomposite samples, various amount of alum sludge was added to examine their impact on the mechanical, thermal and electrical properties. Results have shown that it could be a sustainable practice of reusing such wastes in preparing flexible, lightweight and miniature dielectric materials that can be used for energy storage applications. Thermogravimetric analysis ; Materials science ; Nanocomposite ; Nanocellulose ; Alum ; Elastomer ; Composite material ; Cellulose ; Chemical engineering ; Metallurgy Advanced Cellulose Research Studies ; Dielectric materials and actuators ; biodegradable polymer synthesis and properties
Authors: Michael Breuckmann | Georg Wacker | Stephanie Hanning | Matthias Otto | Martin Kreyenschmidt
Source: Journal of Analytical Atomic Spectrometry (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Quantification of C, H, N and O in polymers using WDXRF scattering spectra and PLS regression depending on the spectral resolution The C, H, N and O composition of polymers, sometimes called “dark matrix”, is determined by wavelength dispersive X-ray fluorescence (WDXRF). The recovery was best for the lowest spectral resolution and highest recorded intensity. Resolution (logic) ; Analytical Chemistry (journal) ; Spectral resolution ; Polymer ; Wavelength ; Spectral line ; Scattering ; Matrix (chemical analysis) ; Chemistry ; High resolution ; Fluorescence ; Materials science ; Optics ; Physics ; Chromatography ; Geology ; Organic chemistry ; Remote sensing Spectroscopy and Chemometric Analyses ; X-ray Spectroscopy and Fluorescence Analysis ; Nuclear Physics and Applications
Authors: Frida Book | Michael Persson | Eric Carmona | Thomas Backhaus | Tobias Lammel
Source: Environmental Science Nano (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Colloidal silica nanomaterials reduce the toxicity of pesticides to algae, depending on charge and surface area Colloidal silica nanomaterials as adsorbents for aquatic pollutants. Nanomaterials ; Adsorption ; Colloid ; Pollutant ; Environmental chemistry ; Pesticide ; Surface charge ; Materials science ; Nanotechnology ; Chemical engineering ; Chemistry ; Organic chemistry ; Ecology ; Biology Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Laser-Ablation Synthesis of Nanoparticles
Authors: MERVE NENNİ | Ayşegül Doğan | MUSTAFA ÇELEBİER | Murat Soyseven | Mustafa Sinan Kaynak | HASSAN Y. ABOUL-ENEIN | GÖKSEL ARLİ
Source: TURKISH JOURNAL OF CHEMISTRY (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Ultrafiltration-based sample preparation and HPLC-UV determination of diclofenac in human plasma samples The sample preparation step is the initial step in pharmaceutical analysis. While ultrafiltration is a well-known technique used in the food and pharmaceutical industries, it has rarely been used to measure the plasma concentration of active pharmaceutical ingredients. This study aimed to analyze diclofenac sodium (DS) in human plasma samples using ultrafiltration-based sample preparation before high-performance liquid chromatography (HPLC) analysis. The advantages and limitations of ultrafiltration-based sample preparation in bioanalysis were evaluated by comparing the results with conventional methods. The precipitating agent was used before ultrafiltration. The analysis was carried on an HPLC-UV system with a C18 column (250 ×4.6 mm, 5 μm) and acetonitrile : phosphate buffer (pH 3.0, 10 mM) (70 : 30 v/v) was used as the mobile phase. The bioanalytical method was validated according to FDA guidelines and applied to spiked samples of DS in commercial human plasma samples. The LOD and LOQ values were 0.006 μg mL<sup>-1</sup> and 0.020 μg mL<sup>-1</sup>, respectively. The method was linear in the range of 0.025-0.50 μgmL<sup>-1</sup> with excellent determination coefficients (R<sup>2</sup> > 0.9991). The findings of this analysis with low LOD and LOQ values and high recovery values with high trueness and precision proved the matrix minimizing the effect of the presented sample preparation technique. Chromatography ; Chemistry ; Bioanalysis ; Ultrafiltration (renal) ; High-performance liquid chromatography ; Sample preparation ; Human plasma ; Quantitative analysis (chemistry) Analytical Methods in Pharmaceuticals ; Analytical chemistry methods development ; Analytical Chemistry and Chromatography
Authors: Jack D. Sundberg | Siona S. Benjamin | Lauren M. McRae | Scott C. Warren
Source: The Journal of Open Source Software (article, 2022-07-19)
Field: Materials Science — Materials Chemistry

Simmate: a framework for materials science Each of these projects facilitate the creation and distribution of materials science data to the broader researcher community through databases, workflow libraries, and web interfaces. However, each software ecosystem (i.e. the collection of software used by a specific project) still possesses several painpoints for users attempting to implement new standards for computational research. Proper setup involves learning how (i) workflows are defined/orchestrated, (ii) how databases are built/accessed, and (iii) how website interfaces/APIs make results accessible to the community, where each component requires learning a new package and, more importantly, learning how that package integrates with others. As a result, it can be difficult to integrate several smaller packages when building a production-ready server and database for materials science research. To address this, we developed the Simulated Materials Ecosystem (Simmate). Engineering ethics ; Engineering Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Materials Characterization Techniques