jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pankaj Rajak | Aravind Krishnamoorthy | Ankit Mishra | Rajiv K. Kalia | Aiichiro Nakano | Priya Vashishta
Source: npj Computational Materials (article, 2021-07-14)
Field: Materials Science — Materials Chemistry

Autonomous reinforcement learning agent for chemical vapor deposition synthesis of quantum materials Abstract Predictive materials synthesis is the primary bottleneck in realizing functional and quantum materials. Strategies for synthesis of promising materials are currently identified by time-consuming trial and error and there are no known predictive schemes to design synthesis parameters for materials. We use offline reinforcement learning (RL) to predict optimal synthesis schedules, i.e., a time-sequence of reaction conditions like temperatures and concentrations, for the synthesis of semiconducting monolayer MoS 2 using chemical vapor deposition. The RL agent, trained on 10,000 computational synthesis simulations, learned threshold temperatures and chemical potentials for onset of chemical reactions and predicted previously unknown synthesis schedules that produce well-sulfidized crystalline, phase-pure MoS 2 . The model can be extended to multi-task objectives such as predicting profiles for synthesis of complex structures including multi-phase heterostructures and can predict long-time behavior of reacting systems, far beyond the domain of molecular dynamics simulations, making these predictions directly relevant to experimental synthesis. Bottleneck ; Chemical vapor deposition ; Reinforcement learning ; Computer science ; Materials science ; Phase (matter) ; Nanotechnology ; Biological system ; Chemistry ; Artificial intelligence ; Organic chemistry Machine Learning in Materials Science ; 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices
Authors: Perla Sánchez-López | Yulia Kotolevich | Rosario I. Yocupicio-Gaxiola | Joel Antúnez-García | Ramesh Kumar Chowdari | Vitalii Petranovskii | S. Fuentes
Source: Frontiers in Chemistry (review, 2021-08-09)
Field: Materials Science — Materials Chemistry

Recent Advances in Catalysis Based on Transition Metals Supported on Zeolites This article reviews the current state and development of thermal catalytic processes using transition metals (TM) supported on zeolites (TM/Z), as well as the contribution of theoretical studies to understand the details of the catalytic processes. Structural features inherent to zeolites, and their corresponding properties such as ion exchange capacity, stable and very regular microporosity, the ability to create additional mesoporosity, as well as the potential chemical modification of their properties by isomorphic substitution of tetrahedral atoms in the crystal framework, make them unique catalyst carriers. New methods that modify zeolites, including sequential ion exchange, multiple isomorphic substitution, and the creation of hierarchically porous structures both during synthesis and in subsequent stages of post-synthetic processing, continue to be discovered. TM/Z catalysts can be applied to new processes such as CO<sub>2</sub> capture/conversion, methane activation/conversion, selective catalytic NO<sub>x</sub> reduction (SCR-deNO<sub>x</sub>), catalytic depolymerization, biomass conversion and H<sub>2</sub> production/storage. Catalysis ; Depolymerization ; Transition metal ; Chemical engineering ; Materials science ; Ion exchange ; Nanotechnology ; Chemistry ; Inorganic chemistry ; Ion ; Organic chemistry Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Catalysis and Hydrodesulfurization Studies
Authors: Adedibu C. Tella | J. T. Bamgbose | Vincent O. Adimula | Mary F. Omotoso | Sunday E. Elaigwu | Victoria T. Olayemi | O.A. Odunola
Source: SN Applied Sciences (article, 2021-01-01)
Field: Chemistry — Inorganic Chemistry

Synthesis of metal–organic frameworks (MOFs) MIL-100(Fe) functionalized with thioglycolic acid and ethylenediamine for removal of eosin B dye from aqueous solution Abstract The interaction of eosin B dye from aqueous solution with MIL-100(Fe) and functionalized MIL-100(Fe) metal–organic frameworks (MOFs) is reported in this study. MIL-100(Fe) was prepared and functionalized with thioglycolic acid (TH) and ethylenediammine (ED) separately by incorporating the thiol (–SH) and the amine (–NH 2 ) group of the functionalizing agents into the open metal sites of the MIL-100(Fe) to obtain the acidic (TH-MIL-100) and basic (ED-MIL-100) forms of the MOF respectively. Characterization of the MOFs was done by melting point analysis, elemental analysis, spectroscopic techniques, scanning electron microscopy (SEM), and powdered X-ray diffraction (PXRD) analysis. The adsorption experiments were carried out at different conditions such as pH, adsorbent dosage, contact time, temperature, and initial concentration of the dye to estimate the optimum conditions and the maximum adsorption capacities. Adsorption capacities were observed to increase in the order of ED-MIL-100 &lt; MIL-100 &lt; TH-MIL-100, while the TH-MIL-100 was the most effective in the removal process due to acid–base interaction between the acidic thiol group (–SH) and the alkaline medium of eosin B dye solution. The Langmuir Isotherm was seen to fit well to adsorption data obtained for all three adsorbent materials studied, and adsorption processes followed the pseudo-second order kinetics. This study, therefore, indicates the suitability of functionalization of MIL-100(Fe) towards improving its adsorption capacity. Thioglycolic acid ; Adsorption ; Aqueous solution ; Metal-organic framework ; Langmuir adsorption model ; Nuclear chemistry ; Ethylenediamine ; Chemistry ; Eosin ; Eosin Y ; Langmuir ; Surface modification ; Inorganic chemistry ; Materials science ; Organic chemistry ; Catalysis ; Physical chemistry ; Photocatalysis Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography ; Adsorption and biosorption for pollutant removal
Authors: A. Ramanujam | Bertrand J. Neyhouse | Rebecca A. Keogh | Madhivanan Muthuvel | Ronan K. Carroll | Gerardine G. Botte
Source: Chemical Engineering Journal (article, 2021-01-12)
Field: Chemistry — Electrochemistry

Rapid electrochemical detection of Escherichia coli using nickel oxidation reaction on a rotating disk electrode A standalone electrochemical method for detecting the bacterium <i>Escherichia coli</i> in water was developed using a nickel electrode and no biorecognition element. Electric current responses from different <i>E. coli</i> concentrations were recorded based on their interaction with a locally formed electrocatalyst. A rotating disk electrode was used to minimize the mass transport limitations at the interface. Results from experiments with the rotating disk electrode also paved the way for hypothesizing the detection mechanism. The operating conditions were established for sensing the electric current responses in the presence of <i>E. coli</i>. The least-squares linear regression model was fit to the data obtained from currents of some known <i>E. coli</i> concentrations. This probe had a detection limit in the order of 10<sup>4</sup> CFU/ml. The response time to detect the presence/absence of <i>E. coli</i> was less than half a second, while the total assay time, including quantification of its concentration, was 10 min. The electric current response from a solution mixed with <i>E. coli</i> and <i>S. aureus</i> showed current similar to <i>E. coli</i> only solution indicating the specificity of the sensor to respond to signals from <i>E. coli</i>. This electrochemical microbial sensor's uniqueness lies in its ability to rapidly detect <i>E. coli</i> by forming the catalyst locally on demand without the attachment of biorecognition elements. Nickel ; Electrode ; Electrochemistry ; Escherichia coli ; Rotating disk electrode ; Chemistry ; Inorganic chemistry ; Materials science ; Nuclear chemistry ; Metallurgy ; Cyclic voltammetry ; Biochemistry Electrochemical Analysis and Applications ; Biosensors and Analytical Detection ; Electrochemical sensors and biosensors
Authors: Yi Chen | Jun Bin | Congming Zou | Mengjiao Ding
Source: Journal of Analytical Methods in Chemistry (article, 2021-06-07)
Field: Chemistry — Analytical Chemistry

Discrimination of Fresh Tobacco Leaves with Different Maturity Levels by Near-Infrared (NIR) Spectroscopy and Deep Learning The maturity affects the yield, quality, and economic value of tobacco leaves. Leaf maturity level discrimination is an important step in manual harvesting. However, the maturity judgment of fresh tobacco leaves by grower visual evaluation is subjective, which may lead to quality loss and low prices. Therefore, an objective and reliable discriminant technique for tobacco leaf maturity level based on near-infrared (NIR) spectroscopy combined with a deep learning approach of convolutional neural networks (CNNs) is proposed in this study. To assess the performance of the proposed maturity discriminant model, four conventional multiclass classification approaches-K-nearest neighbor (KNN), backpropagation neural network (BPNN), support vector machine (SVM), and extreme learning machine (ELM)-were employed for a comparative analysis of three categories (upper, middle, and lower position) of tobacco leaves. Experimental results showed that the CNN discriminant models were able to precisely classify the maturity level of tobacco leaves for the above three data sets with accuracies of 96.18%, 95.2%, and 97.31%, respectively. Moreover, the CNN models with strong feature extraction and learning ability were superior to the KNN, BPNN, SVM, and ELM models. Thus, NIR spectroscopy combined with CNN is a promising alternative to overcome the limitations of sensory assessment for tobacco leaf maturity level recognition. The development of a maturity-distinguishing model can provide an accurate, reliable, and scientific auxiliary means for tobacco leaf harvesting. Artificial intelligence ; Maturity (psychological) ; Linear discriminant analysis ; Convolutional neural network ; Pattern recognition (psychology) ; Mathematics ; Support vector machine ; Deep learning ; Machine learning ; Artificial neural network ; Computer science Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Smart Agriculture and AI
Authors: Nutth Tuchinda | Christopher A. Schuh
Source: Acta Materialia (article, 2021-12-31)
Field: Materials Science — Materials Chemistry

Grain size dependencies of intergranular solute segregation in nanocrystalline materials Nanocrystalline material ; Materials science ; Grain size ; Grain boundary ; Intergranular corrosion ; Grain boundary strengthening ; Alloy ; Chemical physics ; Microstructure ; Thermodynamics ; Metallurgy ; Nanotechnology ; Physics Microstructure and mechanical properties ; Aluminum Alloy Microstructure Properties ; Ion-surface interactions and analysis
Authors: Parinaz Naseri | Stewart Pearson | Zhengzheng Wang | Sean V. Hum
Source: IEEE Transactions on Antennas and Propagation (article, 2021-12-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Combined Machine-Learning/Optimization-Based Approach for Inverse Design of Nonuniform Bianisotropic Metasurfaces Electromagnetic metasurface (EMMS) design based on far-field (FF) constraints without the complete knowledge of the fields on both sides of the metasurface is typically a time-consuming and iterative process, which relies heavily on heuristics and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ad hoc</i> methods. This article proposes an end-to-end systematic and efficient approach where the designer inputs high-level FF constraints, such as nulls, sidelobe levels, and main beam level(s), and a three-layer nonuniform passive, lossless, and omega-type bianisotropic EMMS design to satisfy them is returned. The surface parameters to realize the FF criteria are found using the alternating direction method of multipliers on a homogenized model derived from the method of moments (MoM). This model incorporates edge effects of the finite surface and intercell mutual coupling in the inhomogeneous impedance sheet. Optimization through the physical unit cell space integrated with machine-learning-based surrogate models is used to realize the desired surface parameters from physical meta-atom (or unit cell) designs. Two passive lossless examples with different feeding systems and FF constraints are shown to demonstrate the effectiveness of this method. Computer science ; Surface (topology) ; Inverse ; Heuristics ; Coupling (piping) ; Electrical impedance ; Algorithm ; Topology (electrical circuits) ; Mathematics ; Mathematical optimization ; Physics ; Geometry ; Engineering ; Mechanical engineering Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Myriam Barrejón | Silvia Marchesan | Núria Alegret | Maurizio Prato
Source: Carbon (article, 2021-08-28)
Field: Materials Science — Biomaterials

Carbon nanotubes for cardiac tissue regeneration: State of the art and perspectives Carbon nanotube ; Nanotechnology ; Materials science ; Tissue engineering ; Regeneration (biology) ; Nanomaterials ; Biomedical engineering ; Cell biology ; Engineering Electrospun Nanofibers in Biomedical Applications ; Tissue Engineering and Regenerative Medicine ; Graphene and Nanomaterials Applications
Authors: Maximilian T. Strauss | Isabell Bludau | Wen‐Feng Zeng | Eugenia Voytik | Constantin Ammar | Julia P. Schessner | Rajesh Ilango | Michelle L. Gill | Florian Meier | Sander Willems | Matthias Mann
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-07-26)
Field: Chemistry — Spectroscopy

AlphaPept, a modern and open framework for MS-based proteomics ABSTRACT In common with other omics technologies, mass spectrometry (MS)-based proteomics produces ever-increasing amounts of raw data, making their efficient analysis a principal challenge. There is a plethora of different computational tools that process the raw MS data and derive peptide and protein identification and quantification. During the last decade, there has been dramatic progress in computer science and software engineering, including collaboration tools that have transformed research and industry. To leverage these advances, we developed AlphaPept, a Python-based open-source framework for efficient processing of large high-resolution MS data sets. Using Numba for just-in-time machine code compilation on CPU and GPU, we achieve hundred-fold speed improvements while maintaining clear syntax and rapid development speed. AlphaPept uses the Python scientific stack of highly optimized packages, reducing the code base to domain-specific tasks while providing access to the latest advances in machine learning. We provide an easy on-ramp for community validation and contributions through the concept of literate programming, implemented in Jupyter Notebooks of the different modules. A framework for continuous integration, testing, and benchmarking enforces solid software engineering principles. Large datasets can rapidly be processed as shown by the analysis of hundreds of cellular proteomes in minutes per file, many-fold faster than the data acquisiton. The AlphaPept framework can be used to build automated processing pipelines using efficient HDF5 based file formats, web-serving functionality and compatibility with downstream analysis tools. Easy access for end-users is provided by one-click installation of the graphical user interface, for advanced users via a modular Python library, and for developers via a fully open GitHub repository. Computer science ; Python (programming language) ; Software engineering ; Software ; Cloud computing ; Source code ; File format ; Database ; Programming language ; Operating system Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Machine Learning in Bioinformatics
Authors: Weihua Liu | Shan Zeng | Wu Guiju | Hao Li | Feifei Chen
Source: Sensors (article, 2021-06-26)
Field: Chemistry — Analytical Chemistry

Rice Seed Purity Identification Technology Using Hyperspectral Image with LASSO Logistic Regression Model Hyperspectral technology is used to obtain spectral and spatial information of samples simultaneously and demonstrates significant potential for use in seed purity identification. However, it has certain limitations, such as high acquisition cost and massive redundant information. This study integrates the advantages of the sparse feature of the least absolute shrinkage and selection operator (LASSO) algorithm and the classification feature of the logistic regression model (LRM). We propose a hyperspectral rice seed purity identification method based on the LASSO logistic regression model (LLRM). The feasibility of using LLRM for the selection of feature wavelength bands and seed purity identification are discussed using four types of rice seeds as research objects. The results of 13 different adulteration cases revealed that the value of the regularisation parameter was different in each case. The recognition accuracy of LLRM and average recognition accuracy were 91.67-100% and 98.47%, respectively. Furthermore, the recognition accuracy of full-band LRM was 71.60-100%. However, the average recognition accuracy was merely 89.63%. These results indicate that LLRM can select the feature wavelength bands stably and improve the recognition accuracy of rice seeds, demonstrating the feasibility of developing a hyperspectral technology with LLRM for seed purity identification. Hyperspectral imaging ; Lasso (programming language) ; Feature selection ; Pattern recognition (psychology) ; Artificial intelligence ; Identification (biology) ; Feature (linguistics) ; Logistic regression ; Computer science ; Regression ; Selection (genetic algorithm) ; Mathematics ; Statistics ; Machine learning ; Biology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Remote-Sensing Image Classification
Authors: Vadim Demichev | Fengchao Yu | Guo Ci Teo | Łukasz Szyrwiel | George Rosenberger | Jens Decker | Stephanie Kaspar‐Schoenefeld | Kathryn S. Lilley | Michael Mülleder | Alexey I. Nesvizhskii | Markus Ralser
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-03-09)
Field: Chemistry — Spectroscopy

High sensitivity dia-PASEF proteomics with DIA-NN and FragPipe Abstract The dia-PASEF technology exploits ion mobility separation for high-sensitivity analysis of complex proteomes. Here, we demonstrate neural network-based processing of the ion mobility data, which we implement in the DIA-NN software suite. Using spectral libraries generated with the MSFragger-based FragPipe computational platform, the DIA-NN analysis of dia-PASEF raw data increases the proteomic depth by up to 69% compared to the originally published dia-PASEF workflow. For example, we quantify over 5200 proteins from 10ng of HeLa peptides separated with a 95-minute nanoflow gradient, and over 5000 proteins from 200ng using a 4.8-minute separation with an Evosep One system. In complex samples, featuring a mix of human and yeast lysates, the workflow detects over 11700 proteins in single runs acquired with a 100-minute nanoflow gradient, while demonstrating quantitative precision. Hence, the combination of FragPipe and DIA-NN provides a simple-to-use software platform for dia-PASEF data analysis, yielding significant gains in high-sensitivity proteomics. Workflow ; Software ; Proteomics ; Computer science ; Proteome ; Sensitivity (control systems) ; Artificial neural network ; Software suite ; Biological system ; Chromatography ; Pattern recognition (psychology) ; Artificial intelligence ; Chemistry ; Bioinformatics ; Biology ; Engineering ; Operating system ; Database ; Biochemistry ; Electronic engineering Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Nikoletta Harsági | György Keglevich
Source: Molecules (review, 2021-05-11)
Field: Chemistry — Organic Chemistry

The Hydrolysis of Phosphinates and Phosphonates: A Review Phosphinic and phosphonic acids are useful intermediates and biologically active compounds which may be prepared from their esters, phosphinates and phosphonates, respectively, by hydrolysis or dealkylation. The hydrolysis may take place both under acidic and basic conditions, but the C-O bond may also be cleaved by trimethylsilyl halides. The hydrolysis of <i>P</i>-esters is a challenging task because, in most cases, the optimized reaction conditions have not yet been explored. Despite the importance of the hydrolysis of <i>P</i>-esters, this field has not yet been fully surveyed. In order to fill this gap, examples of acidic and alkaline hydrolysis, as well as the dealkylation of phosphinates and phosphonates, are summarized in this review. Hydrolysis ; Chemistry ; Alkylation ; Trimethylsilyl ; Organic chemistry ; Halide ; Catalysis Organophosphorus compounds synthesis ; Asymmetric Hydrogenation and Catalysis ; Synthesis and characterization of novel inorganic/organometallic compounds
Authors: Andreia Machado Brito-da-Costa | Diana Dias da Silva | Nelson G. M. Gomes | Ricardo Jorge Dinis‐Oliveira | Áurea Madureira-Carvalho
Source: Pharmaceuticals (review, 2021-02-03)
Field: Chemistry — Analytical Chemistry

Pharmacokinetics and Pharmacodynamics of Salvinorin A and Salvia divinorum: Clinical and Forensic Aspects <i>Salvia divinorum</i> Epling and Játiva is a perennial mint from the Lamiaceae family, endemic to Mexico, predominantly from the state of Oaxaca. Due to its psychoactive properties, <i>S. divinorum</i> had been used for centuries by Mazatecans for divinatory, religious, and medicinal purposes. In recent years, its use for recreational purposes, especially among adolescents and young adults, has progressively increased. The main bioactive compound underlying the hallucinogenic effects, salvinorin A, is a non-nitrogenous diterpenoid with high affinity and selectivity for the k-opioid receptor. The aim of this work is to comprehensively review and discuss the toxicokinetics and toxicodynamics of <i>S. divinorum</i> and salvinorin A, highlighting their psychological, physiological, and toxic effects. Potential therapeutic applications and forensic aspects are also covered in this review. The leaves of <i>S. divinorum</i> can be chewed, drunk as an infusion, smoked, or vaporised. Absorption of salvinorin A occurs through the oral mucosa or the respiratory tract, being rapidly broken down in the gastrointestinal system to its major inactive metabolite, salvinorin B, when swallowed. Salvinorin A is rapidly distributed, with accumulation in the brain, and quickly eliminated. Its pharmacokinetic parameters parallel well with the short-lived psychoactive and physiological effects. No reports on toxicity or serious adverse outcomes were found. A variety of therapeutic applications have been proposed for <i>S. divinorum</i> which includes the treatment of chronic pain, gastrointestinal and mood disorders, neurological diseases, and treatment of drug dependence. Notwithstanding, there is still limited knowledge regarding the pharmacology and toxicology features of <i>S. divinorum</i> and salvinorin A, and this is needed due to its widespread use. Additionally, the clinical acceptance of salvinorin A has been hampered, especially due to the psychotropic side effects and misuse, turning the scientific community to the development of analogues with better pharmacological profiles. Hallucinogen ; Medicine ; Pharmacology ; Traditional medicine ; Drug Chromatography in Natural Products ; Analytical Methods in Pharmaceuticals ; Pain Mechanisms and Treatments