jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hamada A. A. Noreldeen | Kai‐Yuan Huang | Gang-Wei Wu | Qi Zhang | Hua‐Ping Peng | Hao‐Hua Deng | Wei Chen
Source: Analytical Chemistry (article, 2022-12-06)
Field: Materials Science — Materials Chemistry

Feature Selection Assists BLSTM for the Ultrasensitive Detection of Bioflavonoids in Different Biological Matrices Based on the 3D Fluorescence Spectra of Gold Nanoclusters Rapid and on-site qualitative and quantitative analysis of small molecules (including bioflavonoids) in biofluids are of great importance in biomedical applications. Herein, we have developed two deep learning models based on the 3D fluorescence spectra of gold nanoclusters as a single probe for rapid qualitative and quantitative analysis of eight bioflavonoids in serum. The results proved the efficiency and stability of the random forest-bidirectional long short-term memory (RF-BLSTM) model, which was used only with the most important features after deleting the unimportant features that might hinder the performance of the model in identifying the selected bioflavonoids in serum at very low concentrations. The optimized model achieves excellent overall accuracy (98-100%) in the qualitative analysis of the selected bioflavonoids. Next, the optimized model was transferred to quantify the selected bioflavonoids in serum at nanoscale concentrations. The transferred model achieved excellent accuracy, and the overall determination coefficient (R<sup>2</sup>) value range was 99-100%. Furthermore, the optimized model achieved excellent accuracies in other applications, including multiplex detection in serum and model applicability in urine. Also, LOD in serum at nanoscale concentration was considered. Therefore, this approach opens the window for qualitative and quantitative analysis of small molecules in biofluids at nanoscale concentrations, which may help in the rapid inclusion of sensor arrays in biomedical and other applications. Nanoclusters ; Chemistry ; Nanoscopic scale ; Multiplex ; Fluorescence ; Nanotechnology ; Biological system ; Random forest ; Analytical Chemistry (journal) ; Chromatography ; Computer science ; Materials science ; Artificial intelligence ; Bioinformatics ; Physics Advanced Nanomaterials in Catalysis ; Dye analysis and toxicity ; Electrochemical sensors and biosensors
Authors: Rodrigo de Andrade Bustamante | Juan Santos de Oliveira | Brunno Ferreira dos Santos
Source: Environmental Science and Pollution Research (article, 2022-08-23)
Field: Chemistry — Analytical Chemistry

Modeling biosurfactant production from agroindustrial residues by neural networks and polynomial models adjusted by particle swarm optimization Particle swarm optimization ; Biological system ; Bioprocess ; Artificial neural network ; Bioreactor ; Biochemical engineering ; Process (computing) ; Process engineering ; Mathematics ; Computer science ; Engineering ; Chemistry ; Artificial intelligence ; Machine learning ; Chemical engineering Petroleum Processing and Analysis ; Microbial bioremediation and biosurfactants ; Mineral Processing and Grinding
Authors: Ehsan Kianfar
Source: Silicon (article, 2022-07-22)
Field: Chemistry — Inorganic Chemistry

The Effects of SiO2/Al2O3 and H2O/Al2O3 Molar Ratios on SAPO-34 Catalyst in the Methanol to Olefin Process Catalysis ; Methanol ; Crystallinity ; Olefin fiber ; Materials science ; Ethylene ; Selectivity ; Coke ; Nuclear chemistry ; Chemical engineering ; Inorganic chemistry ; Organic chemistry ; Chemistry ; Metallurgy ; Composite material Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions ; Chemical Synthesis and Characterization
Authors: Manuel Moliner | Mercedes Boronat
Source: Microporous and Mesoporous Materials (article, 2022-11-21)
Field: Materials Science — Materials Chemistry

Towards “enzyme-like” zeolite designs to maximize the efficiency of catalysts by molecular recognition: Fine-tuning confinement and active site location In the present Perspective, we highlight relevant recent advances on the design of zeolite-based catalysts, where innovative rationalizations allow guiding towards molecular and atomic control to generate the precise metal active sites and maximize the stabilization of the key intermediates or reaction transition states, in a similar way to how enzymes do in nature and biological systems. Achieving this “enzymatic-like” specificity with zeolite-based catalysts has been one of the main goals of Prof. Corma and his group along his entire career, proposing novel and ground-breaking concepts in the field that have permitted generating excellent fundamental research advances as well as the final application of many of these advances in industrial processes. Zeolite ; Catalysis ; Nanotechnology ; Active site ; Biochemical engineering ; Chemistry ; Materials science ; Computer science ; Combinatorial chemistry ; Engineering ; Organic chemistry Mesoporous Materials and Catalysis ; Zeolite Catalysis and Synthesis ; Catalysis for Biomass Conversion
Authors: Thantip Roongcharoen | Xin Yang | Shuang Han | Luca Sementa | Tejs Vegge | Heine Anton Hansen | Alessandro Fortunelli
Source: Faraday Discussions (article, 2022-10-05)
Field: Materials Science — Materials Chemistry

Oxidation and de-alloying of PtMn particle models: a computational investigation We present a computational study of the energetics and mechanisms of oxidation of Pt–Mn systems. Oxide ; Density functional theory ; Vacancy defect ; Chemical physics ; Materials science ; Transition metal ; Chemistry ; Crystallography ; Computational chemistry ; Metallurgy ; Catalysis Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Machine Learning in Materials Science
Authors: Haidi Wang | Qingqing Feng | Xingxing Li | Jinlong Yang
Source: Research (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

High-Throughput Computational Screening for Bipolar Magnetic Semiconductors Searching ferromagnetic semiconductor materials with electrically controllable spin polarization is a long-term challenge for spintronics. Bipolar magnetic semiconductors (BMS), with valence and conduction band edges fully spin polarized in different spin directions, show great promise in this aspect because the carrier spin polarization direction can be easily tuned by voltage gate. Here, we propose a standard high-throughput computational screening scheme for searching BMS materials. The application of this scheme to the Materials Project database gives 11 intrinsic BMS materials (1 experimental and 10 theoretical) from nearly ~40000 structures. Among them, a room-temperature BMS Li<sub>2</sub>V<sub>3</sub>TeO<sub>8</sub> (mp-771246) is discovered with a Curie temperature of 478 K. Moreover, the BMS feature can be maintained well when cutting the bulk Li<sub>2</sub>V<sub>3</sub>TeO<sub>8</sub> into (001) nanofilms for realistic applications. This work provides a feasible solution for discovering novel intrinsic BMS materials from various crystal structure databases, paving the way for realizing electric-field controlled spintronics devices. Spintronics ; Ferromagnetism ; Materials science ; Magnetic semiconductor ; Semiconductor ; Condensed matter physics ; Spin polarization ; Curie temperature ; Spin (aerodynamics) ; Optoelectronics ; Valence (chemistry) ; Polarization (electrochemistry) ; Physics ; Chemistry ; Quantum mechanics ; Electron Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Electronic and Structural Properties of Oxides
Authors: Koki Hara | Shunji Yamada | Atsushi Kurotani | Eisuke Chikayama | Jun Kikuchi
Source: Scientific Reports (article, 2022-06-22)
Field: Chemistry — Spectroscopy

Materials informatics approach using domain modelling for exploring structure–property relationships of polymers In the development of polymer materials, it is an important issue to explore the complex relationships between domain structure and physical properties. In the domain structure analysis of polymer materials, <sup>1</sup>H-static solid-state NMR (ssNMR) spectra can provide information on mobile, rigid, and intermediate domains. But estimation of domain structure from its analysis is difficult due to the wide overlap of spectra from multiple domains. Therefore, we have developed a materials informatics approach that combines the domain modeling ( http://dmar.riken.jp/matrigica/ ) and the integrated analysis of meta-information (the elements, functional groups, additives, and physical properties) in polymer materials. Firstly, the <sup>1</sup>H-static ssNMR data of 120 polymer materials were subjected to a short-time Fourier transform to obtain frequency, intensity, and T<sub>2</sub> relaxation time for domains with different mobility. The average T<sub>2</sub> relaxation time of each domain is 0.96 ms for Mobile, 0.55 ms for Intermediate (Mobile), 0.32 ms for Intermediate (Rigid), and 0.11 ms for Rigid. Secondly, the estimated domain proportions were integrated with meta-information such as elements, functional group and thermophysical properties and was analyzed using a self-organization map and market basket analysis. This proposed method can contribute to explore structure-property relationships of polymer materials with multiple domains. Domain (mathematical analysis) ; Computer science ; Polymer ; Relaxation (psychology) ; Domain analysis ; Time domain ; Materials science ; Property (philosophy) ; Biological system ; Mathematics ; Composite material Advanced NMR Techniques and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Darwin Kurniawan | Yan-Yi Chen | Neha Sharma | Michael Ryan Rahardja | Wei‐Hung Chiang
Source: Chemosensors (article, 2022-11-23)
Field: Materials Science — Materials Chemistry

Graphene Quantum Dot-Enabled Nanocomposites as Luminescence- and Surface-Enhanced Raman Scattering Biosensors Graphene quantum dots (GQDs) are a zero-dimensional (0D) carbon-based nanomaterial with a unique quantum confinement effect that have captured the eyes of many researchers. In addition to their luminescence properties, a high biocompatibility and large surface area have enabled GQDs to be used for many applications, and even be integrated with either organic or inorganic materials to produce GQD nanocomposites to enhance the application performances and broaden the application scope. In this review, we aim to highlight the exquisite properties and synthesis methods of GQDs, recent advances in the fabrication of GQD nanocomposites with both organic and inorganic materials, and their corresponding luminescence-based and surface enhanced Raman scattering (SERS)-based biosensing applications. Finally, this review article concludes with a summary of current challenges and prospects. Quantum dot ; Graphene ; Nanotechnology ; Materials science ; Nanocomposite ; Raman scattering ; Luminescence ; Nanomaterials ; Biosensor ; Graphene quantum dot ; Biocompatibility ; Surface modification ; Nanoparticle ; Carbon nanotube ; Raman spectroscopy ; Optoelectronics ; Chemistry ; Physics ; Optics Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Giulia Mancardi | Matteo Alberghini | Neus Aguilera-Porta | Mónica Calatayud | Pietro Asinari | Eliodoro Chiavazzo
Source: Nanomaterials (article, 2022-01-10)
Field: Materials Science — Materials Chemistry

Multi-Scale Modelling of Aggregation of TiO2 Nanoparticle Suspensions in Water Titanium dioxide nanoparticles have risen concerns about their possible toxicity and the European Food Safety Authority recently banned the use of TiO<sub>2</sub> nano-additive in food products. Following the intent of relating nanomaterials atomic structure with their toxicity without having to conduct large-scale experiments on living organisms, we investigate the aggregation of titanium dioxide nanoparticles using a multi-scale technique: starting from ab initio Density Functional Theory to get an accurate determination of the energetics and electronic structure, we switch to classical Molecular Dynamics simulations to calculate the Potential of Mean Force for the connection of two identical nanoparticles in water; the fitting of the latter by a set of mathematical equations is the key for the upscale. Lastly, we perform Brownian Dynamics simulations where each nanoparticle is a spherical bead. This coarsening strategy allows studying the aggregation of a few thousand nanoparticles. Applying this novel procedure, we find three new molecular descriptors, namely, the aggregation free energy and two numerical parameters used to correct the observed deviation from the aggregation kinetics described by the Smoluchowski theory. Ultimately, molecular descriptors can be fed into QSAR models to predict the toxicity of a material knowing its physicochemical properties, enabling safe design strategies. Nanoparticle ; Brownian dynamics ; Molecular dynamics ; Quantitative structure–activity relationship ; Titanium dioxide ; Brownian motion ; Density functional theory ; Nanomaterials ; Nanotechnology ; Statistical physics ; Biological system ; Materials science ; Chemical physics ; Chemistry ; Computational chemistry ; Computer science ; Physics ; Machine learning Nanoparticles: synthesis and applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Computational Drug Discovery Methods
Authors: Bijun Tang | Dianyi Hu | Xiaoxu Zhao | Xiaowei Wang | Zheng Liu
Source: ACS Applied Electronic Materials (article, 2022-06-16)
Field: Materials Science — Materials Chemistry

Recent Developments in Chemical Vapor Deposition of 2D Magnetic Transition Metal Chalcogenides In recent years, two-dimensional (2D) magnetic transition metal chalcogenides (TMCs) have attracted tremendous research interests thanks to their intriguing properties that are essential in developing future electronic and spintronic devices in this modernizing era. This review aims to introduce recent developments in the preparation of 2D magnetic TMCs, especially chromium and iron-based chalcogenides, their structures, as well as the related intriguing magnetic phenomena. First, the common crystal structures of magnetic TMCs including both layered and nonlayered structures are introduced. Various chemical vapor deposition strategies for synthesizing 2D magnetic TMCs are then introduced with emphasis on the key synthesis parameters. Moreover, the intriguing physical properties associated with 2D TMCs such as magnetic anisotropy, thickness, and phase-dependent magnetic response as well as stability are summarized. Last but not least, challenges and future research directions are briefly discussed in light of recent advances in the field. Spintronics ; Chemical vapor deposition ; Nanotechnology ; Materials science ; Transition metal ; Engineering physics ; Condensed matter physics ; Chemistry ; Ferromagnetism ; Engineering ; Physics 2D Materials and Applications ; Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films
Authors: Raunaq Bagchi | Mohamed Elshazly | Jeanne N’Diaye | Dian Yu | Jane Y. Howe | Keryn Lian
Source: Chemistry (article, 2022-11-11)
Field: Materials Science — Polymers and Plastics

Effects of Carboxyl Functionalized CNT on Electrochemical Behaviour of Polyluminol-CNT Composites The effect of carboxyl groups on the redox activity of polyluminol-carbon nanotube composites was studied. Carboxyl groups were selected due to their known contributions toward surface wettability and pseudocapacitance while often present on naturally derived low-cost porous carbons. Density functional theory (DFT) predicted energetically favoured bonding and a significantly reduced band gap between the luminol and carboxylated graphene relative to that of bare graphene, suggesting improved charge storage for carboxylated carbon substrates. The prediction was validated using bare carbon nanotubes (CNTs) and carboxylated CNTs (COOH-CNTs) as the substrates for in situ chemical polymerized luminol (CpLum). Surface morphological studies showed a ca. 1.1 nm thick coating of CpLum on CNT (CpLum/CNT) and a ca. 1.3 nm on COOH-CNT (CpLum/COOH-CNT), while surface chemical analysis revealed ca. 10% nitrogen from CpLum on both CpLum/CNT and CpLum/COOH-CNT. However, with merely 4.4% of COOH functionalization, CpLum/COOH-CNT was able to store more charge (137.1 ± 17.1 C cm−3) relative to CpLum/CNT (86.1 ± 14.1 C cm−3) and had increased charge retention over 5000 cycles. The insights from these studies can be used to engineer the surface of carbons such as CNTs and ACs to improve the interfacial properties for redox active materials and composites. Pseudocapacitance ; Carbon nanotube ; Materials science ; Graphene ; Surface modification ; Wetting ; Redox ; Composite material ; Surface charge ; Electrochemistry ; Chemical engineering ; Nanotechnology ; Supercapacitor ; Chemistry ; Electrode Conducting polymers and applications ; Supercapacitor Materials and Fabrication ; Carbon Nanotubes in Composites
Authors: Emily M. Garrity | Cheng‐Wei Lee | Prashun Gorai | M. Brooks Tellekamp | Andriy Zakutayev | Vladan Stevanović
Source: PRX Energy (article, 2022-12-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Computational Identification of Ternary Wide-Band-Gap Oxides for High-Power Electronics Three classes of ternary oxides are identified as semiconductor candidates for high-power electronic devices using a high-throughput computational workflow and a review of doping and crystal growth feasibility. Ternary operation ; Electronics ; Workflow ; Identification (biology) ; Semiconductor ; Materials science ; Power electronics ; Throughput ; Doping ; Power (physics) ; Computer science ; Optoelectronics ; Electrical engineering ; Engineering ; Telecommunications ; Physics Ga2O3 and related materials ; ZnO doping and properties ; Electronic and Structural Properties of Oxides
Authors: Gao Shu-min | Hanwen Kang | Xiaosong An | Yunjiang Cheng | Hong Chen | Yaohui Chen | Shanjun Li
Source: Frontiers in Plant Science (article, 2022-03-29)
Field: Chemistry — Analytical Chemistry

Non-destructive Storage Time Prediction of Newhall Navel Oranges Based on the Characteristics of Rind Oil Glands How to non-destructively and quickly estimate the storage time of citrus fruit is necessary and urgent for freshness control in the fruit market. As a feasibility study, we present a non-destructive method for storage time prediction of Newhall navel oranges by investigating the characteristics of the rind oil glands in this paper. Through the observation using a digital microscope, the oil glands were divided into three types and the change of their proportions could indicate the rind status as well as the storage time. Images of the rind of the oranges were taken in intervals of 10 days for 40 days, and they were used to train and test the proposed prediction models based on K-Nearest Neighbors (KNN) and deep learning algorithms, respectively. The KNN-based model demonstrated explicit features for storage time prediction based on the gland characteristics and reached a high accuracy of 93.0%, and the deep learning-based model attained an even higher accuracy of 96.0% due to its strong adaptability and robustness. The workflow presented can be readily replicated to develop non-destructive methods to predict the storage time of other types of citrus fruit with similar oil gland characteristics in different storage conditions featuring high efficiency and accuracy. Adaptability ; Robustness (evolution) ; Computer science ; Artificial intelligence ; Citrus fruit ; Biological system ; Horticulture ; Chemistry ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Parinaz Naseri | George Goussetis | Nelson J. G. Fonseca | Sean V. Hum
Source: Scientific Reports (article, 2022-10-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Synthesis of multi-band reflective polarizing metasurfaces using a generative adversarial network Electromagnetic linear-to-circular polarization converters with wide- and multi-band capabilities can simplify antenna systems where circular polarization is required. Multi-band solutions are attractive in satellite communication systems, which commonly have the additional requirement that the sense of polarization is reversed between adjacent bands. However, the design of these structures using conventional ad hoc methods relies heavily on empirical methods. Here, we employ a data-driven approach integrated with a generative adversarial network to explore the design space of the polarizer meta-atom thoroughly. Dual-band and triple-band reflective polarizers with stable performance over incident angles up to and including 30°, corresponding to typical reflector antenna system requirements, are synthesized using the proposed method. The feasibility and performance of the designed polarizer is validated through measurements of a fabricated prototype. Adversarial system ; Generative adversarial network ; Generative grammar ; Computer science ; Artificial intelligence ; Deep learning Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: A. N. Shankar | Vishal Jagota | Nashrah Hani Jamadon | R. Raffik | Vikram Lakshmi Suneetha | Issah Abubakari Samori | Lokanayaki Karnan
Source: Advances in Materials Science and Engineering (article, 2022-10-10)
Field: Materials Science — Polymers and Plastics

An AHP-TOPSIS Approach for Optimizing the Mechanical Performance of Natural Fiber-Based Green Composites Natural fibers have emerged as an effective replacement for synthetic fibers in the fabrication of green composites to be used for producing various components in automotive, aerospace, and other applications. In this proposed study, the mechanical properties of banana and coir fiber-based green composites have been optimized by using a hybrid AHP-TOPSIS approach. Corn starch along with glycerol has been used as the matrix material for fabricating the green composites. The mechanical properties such as tensile strength, flexural strength, and impact strength of the developed green composite have been optimized with a focus on the utilization of this composite in automotive and aerospace applications. Three different weight percentages (0%, 5%, and 10%) of banana and coir fibers was considered for the fabrication of green composites. The constituents of the green composite have been taken as the input variables whereas the mechanical properties of the green composite are considered as the output variables for designing the experiment. The design of the experiment consisted of nine different combinations of input and output variables. Results of the study revealed that 5 wt.% of banana fiber, 10 wt.% of coir fiber, and 85 wt.% of corn starch provide the optimum mechanical performance of the developed green composites. Materials science ; Composite material ; Ultimate tensile strength ; Coir ; Fiber ; Composite number ; Natural fiber ; Flexural strength ; Fabrication ; Response surface methodology ; Izod impact strength test ; Computer science Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research ; Nanocomposite Films for Food Packaging