jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Viswambari R Devi | Michael Keidar
Source: Preprints.org (preprint, 2023-08-22)
Field: Materials Science — Materials Chemistry

Personalized Plasma Medicine for Cancer: Transforming Treatment Strategies with Mathematical Modeling and AI Algorithms Plasma technology shows tremendous potential for revolutionizing oncology research and treatment. Reactive oxygen and nitrogen species, electromagnetic emissions generated through gas plasma jets, have attracted significant attention due to their selective cytotoxicity towards cancer cells. To leverage the full potential of plasma medicine, researchers have explored the use of mathematical models and various subsets of machine learning, such as reinforcement learning, and deep learning. This review emphasizes the significant application of AI algorithms in the adaptive plasma system, paving the way for precision and dynamic cancer treatment. Realizing the full potential of AI in plasma medicine, requires research efforts, data sharing and interdisciplinary collaborations. Unravelling the complex mechanisms, developing real-time diagnostics, and optimizing AI models will be crucial to harness the true power of plasma technology in oncology. The integration of personalized and dynamic plasma therapies, alongside AI and diagnostic sensors, presents a transformative approach to cancer treatment with the potential to improve outcomes globally. Leverage (statistics) ; Transformative learning ; Plasma medicine ; Personalized medicine ; Precision medicine ; Computer science ; Cancer therapy ; Artificial intelligence ; Machine learning ; Cancer treatment ; Reinforcement learning ; Cancer ; Plasma ; Bioinformatics ; Medicine ; Atmospheric-pressure plasma ; Psychology ; Internal medicine ; Physics ; Biology Catalytic Processes in Materials Science ; Plasma Applications and Diagnostics ; Air Quality Monitoring and Forecasting
Authors: Dmitriy Yu. Vandyshev | Olga V. Sleptsova | Vladislav Yu. Gazin | Stanislav A. Malyutin | Naum R. Malkin | Х. С. Шихалиев
Source: Конденсированные среды и межфазные границы (article, 2023-09-01)
Field: Chemistry — Spectroscopy

Sedimentation of bentonite suspensions under the influence of low molecular weight polymers based on amino ester salts Among the available technologies for treating natural and wastewater from clay materials, coagulation/flocculation is the most common method due to its high efficiency, simplicity, and cost-effectiveness. Inorganic coagulants such as aluminum sulfate and ferric chloride, widely used as destabilizing agents for colloidal particles, have several significant drawbacks: low efficiency and toxicity. Organic reagents of both natural and synthetic origin are a good alternative. This work is devoted to the evaluation of the flocculation action of new reagents, which are low molecular weight polymers based on amino ester salts on clay suspensions, as well as the selection of their optimal concentration, providing the maximum sedimentation rate. Studies have shown that amino ester salts can be effectively used for the treatment of water-clay suspensions. An important factor is the nature of the anion used, which has a significant influence on the oagulation ability of esters. Thus, 40–50 % (wt.) aqueous solutions of amino ester chlorides added to clay suspensions in an amount not exceeding 0.1% (vol.) can be used to thicken clay suspensions. At the same time, aqueous solutions of amino ester bromides regardless of the concentration, introduced into bentonite suspensions of 0.1–0.4 % (vol.), contribute to improved sedimentation, reducing viscosity, and increasing filtration capacity. These results allow us to recommend the use of amino ester chlorides as athickener in the preparation of drilling muds for strengthening the walls of wells during drilling, and bromides – for flocculation of bentonite suspensions in oil production Bentonite ; Sedimentation ; Polymer ; Chemical engineering ; Chemistry ; Polymer chemistry ; Materials science ; Organic chemistry ; Geology ; Sediment ; Engineering Adsorption, diffusion, and thermodynamic properties of materials ; Mining and Gasification Technologies ; Coal Combustion and Slurry Processing
Authors: Seyed Iman Saedi | Mehdi Rezaei
Source: Preprints.org (preprint, 2023-11-14)
Field: Chemistry — Analytical Chemistry

Xception Deep Learning Model for Automatic Sorting of Olives Based on Ripening Stages Olive fruits at different ripening stages give rise to various table olive products and oil qualities. Therefore, developing an efficient method for recognizing and sorting olive fruits based on their ripening stages can greatly facilitate postharvest processing. This study introduces an automatic computer vision system that utilizes deep learning technology to classify the `Roghani` Iranian olive cultivar into five ripening stages using color images. The developed model employs convolutional neural networks (CNN) and transfer learning based on the Xception architecture and ImageNet weights as the base network. The model was fine-tuned by testing multiple configurations of well-known CNN layers. To minimize overfitting and enhance model generality, data augmentation techniques were employed. By considering different optimizers and two image sizes, four final candidate models were generated. These models were then compared in terms of loss and accuracy on the test dataset, classification performance (classification report and confusion matrix), and generality. All four candidates exhibited high accuracies ranging from 86.93% to 93.46% and comparable classification performance. In all models, at least one class was recognized with 100% accuracy. However, by taking into account the risk of overfitting, two models were discarded. Finally, a model with an image size of 224 × 224 and an SGD optimizer, which had a loss of 1.23 and an accuracy of 86.93%, was selected as the preferred option. The results of this study offer robust tools for automatic olive sorting systems, simplifying the differentiation of olives at various ripening levels for different post-harvest products. Overfitting ; Convolutional neural network ; Computer science ; Ripening ; Artificial intelligence ; Sorting ; Contextual image classification ; Deep learning ; Pattern recognition (psychology) ; Confusion matrix ; Transfer of learning ; Machine learning ; Artificial neural network ; Image (mathematics) ; Horticulture Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Date Palm Research Studies
Authors: Karrar Majeed Obaid | Ahmed S. Abbas | Yahya Al‐Khafaji
Source: Indonesian Journal of Chemistry (article, 2023-10-16)
Field: Materials Science — Polymers and Plastics

Removal of Methyl Orange Using Nanocomposites Based on Polyaniline/Nb&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;5&lt;/sub&gt;/MnO&lt;sub&gt;2&lt;/sub&gt; and Polyaniline/Nb&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;5&lt;/sub&gt;/Cr&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;3&lt;/sub&gt; as New Adsorbents Dyes are the most widely employed materials for coloring applications, especially for industrial purposes; thus, dyes are applied in the textile, cosmetics and foodstuffs. Dyes are very important owing to their applications in all aspects of human life. Accordingly, the production volume of dyes around the world is increasing. In this study, a new type of multifunctional material: polyaniline/Nb2O5/MnO2 and polyaniline/Nb2O5/Cr2O3 nanocomposites, was prepared by chemical polymerization from aniline monomer in the presence of metal oxides (Nb2O5, Cr2O3, and MnO2) and an oxidant (ammonium persulfate) in acidic aqueous solution for the elimination of dye from water. Herein, the nanocomposite was found to be a favorable adsorbent for wastewater treatment due to its high adsorption and efficiency, self-regeneration quality, low cost and easy synthesis. Fourier transform infrared spectroscopy, X-ray diffraction, scanning electron microscopy and UV-visible spectroscopy were used to assess the synthesized nanocomposites' characteristics. From the results, we discovered that polyaniline nanocomposites doped with Nb2O5 and MnO2 nanoparticles had a higher adsorption efficiency (~97.37%) than those found in polyaniline with Cr2O3 and MnO2 (~94.3%). We looked at the adsorption conditions, including the medium's pH, the initial dye concentration, the dosage of the adsorbent, and the adsorption time. Polyaniline ; Ammonium persulfate ; Chemistry ; Nanocomposite ; Adsorption ; Methyl orange ; Polymerization ; Fourier transform infrared spectroscopy ; Nuclear chemistry ; Aqueous solution ; Aniline ; Chemical engineering ; Organic chemistry ; Polymer ; Photocatalysis Conducting polymers and applications ; Water Quality Monitoring and Analysis
Authors: Santiago Rigamonti | Maria Troppenz | Martin Kubáň | Axel Hübner | Claudia Draxl
Source: arXiv (Cornell University) (preprint, 2023-10-27)
Field: Materials Science — Materials Chemistry

CELL: a Python package for cluster expansion with a focus on complex alloys We present the Python package CELL, which provides a modular approach to the cluster expansion (CE) method. CELL can treat a wide variety of substitutional systems, including one-, two-, and three-dimensional alloys, in a general multi-component and multi-sublattice framework. It is capable of dealing with complex materials comprising several atoms in their parent lattice. CELL uses state-of-the-art techniques for the construction of training data sets, model selection, and finite-temperature simulations. The user interface consists of well-documented Python classes and modules (http://sol.physik.hu-berlin.de/cell/). CELL also provides visualization utilities and can be interfaced with virtually any ab initio package, total-energy codes based on interatomic potentials, and more. The usage and capabilities of CELL are illustrated by a number of examples, comprising a Cu-Pt surface alloy with oxygen adsorption, featuring two coupled binary sublattices, and the thermodynamic analysis of its order-disorder transition; the demixing transition and lattice-constant bowing of the Si-Ge alloy; and an iterative CE approach for a complex clathrate compound with a parent lattice consisting of 54 atoms. Python (programming language) ; Computational science ; Alloy ; Lattice (music) ; Cluster expansion ; Ab initio ; Computer science ; Materials science ; Lattice constant ; Thermodynamics ; Crystallography ; Chemistry ; Physics ; Programming language ; Quantum mechanics Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis
Authors: Haiteng Zhao | Shengchao Liu | Chang Ma | Hannan Xu | Jie Fu | Zhi‐Hong Deng | Lingpeng Kong | Qi Liu
Source: arXiv (Cornell University) (preprint, 2023-05-28)
Field: Materials Science — Materials Chemistry

GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning Molecule property prediction has gained significant attention in recent years. The main bottleneck is the label insufficiency caused by expensive lab experiments. In order to alleviate this issue and to better leverage textual knowledge for tasks, this study investigates the feasibility of employing natural language instructions to accomplish molecule-related tasks in a zero-shot setting. We discover that existing molecule-text models perform poorly in this setting due to inadequate treatment of instructions and limited capacity for graphs. To overcome these issues, we propose GIMLET, which unifies language models for both graph and text data. By adopting generalized position embedding, our model is extended to encode both graph structures and instruction text without additional graph encoding modules. GIMLET also decouples encoding of the graph from tasks instructions in the attention mechanism, enhancing the generalization of graph features across novel tasks. We construct a dataset consisting of more than two thousand molecule tasks with corresponding instructions derived from task descriptions. We pretrain GIMLET on the molecule tasks along with instructions, enabling the model to transfer effectively to a broad range of tasks. Experimental results demonstrate that GIMLET significantly outperforms molecule-text baselines in instruction-based zero-shot learning, even achieving closed results to supervised GNN models on tasks such as toxcast and muv. Computer science ; Leverage (statistics) ; Graph ; Bottleneck ; Embedding ; Zero (linguistics) ; Theoretical computer science ; Generalization ; Artificial intelligence ; Natural language processing ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Tengfei Luo | Jiahang Zhou | Ruiyang Li
Source: Research Square (preprint, 2023-04-24)
Field: Materials Science — Materials Chemistry

Physics-Informed Neural Networks for Solving Time-Dependent Mode-Resolved Phonon Boltzmann Transport Equation Phonon ; Boltzmann equation ; Physics ; Mode (computer interface) ; Boltzmann constant ; Artificial neural network ; Statistical physics ; Quantum mechanics ; Computer science ; Artificial intelligence Thermal properties of materials ; Model Reduction and Neural Networks ; Heat Transfer and Optimization
Authors: Edi Topić | Mirta Rubčić
Source: Materials (article, 2023-03-10)
Field: Chemistry — Spectroscopy

Structural Insights into Layered Tetrahalocuprates(II) Based on Small Unsaturated and Cyclic Primary Ammonium Cations Layered hybrid halometallates represent a promising class of multifunctional materials, yet with many open challenges regarding the interaction between building blocks. In this work, we present a synthetic and analytical methodology for the efficient synthesis and structural analysis of a series of novel tetrahalocuprate(II) hybrids based on small alkylammonium cations. Observed robustness in geometrical motifs provided a platform for crystal structure determination, even from the complex laboratory powder diffraction data. The slight differences in inorganic layer geometry and severe differences in organic bilayer packing are quantified using well-established descriptors for these materials, and dependences of geometric parameters on anion and cation choice are accounted for. Temperature dependence of structural parameters for one of the tetrachlorocuprate hybrids that was chosen as a model unveils a possible geometrical origin of thermochromism in these materials. Thermochromism ; Materials science ; Diffraction ; Robustness (evolution) ; Bilayer ; Crystal structure ; Ion ; Hybrid material ; Powder diffraction ; Crystallography ; Chemistry ; Nanotechnology ; Organic chemistry ; Optics ; Physics Molecular Sensors and Ion Detection ; Crystallography and molecular interactions ; Luminescence and Fluorescent Materials
Authors: Maria H. Rasmussen | Julius Seumer | Jan H. Jensen
Source: ChemRxiv (preprint, 2023-07-03)
Field: Materials Science — Materials Chemistry

De Novo Catalyst Discovery: Fast Identification of New Catalyst Candidates Very recently our group has demonstrated how a genetic algorithm (GA) starting from random tertiary amines can be used to discover a new and efficient catalyst for the alcohol-mediated Morita- Baylis-Hillman (MBH) reaction. In particular, the discovered catalyst was shown experimentally to be eight times more active than 1,4-diazabicyclo[2.2.2]octane (DABCO), which is commonly used to catalyze the MBH reaction. This represents a breakthrough in using generative models for catalyst optimization. However, the GA-procedure, and hence discovery, relied on two important pieces of information; 1) the knowledge that tertiary amines catalyze the reaction and 2) the mechanism and reaction profile for the catalyzed reaction, in particular the transition state structure of the rate determining step. Thus truly de novo catalyst discovery must also include these steps. Here we present such a method for discovering catalyst candidates for a specific reaction while simultaneously proposing a mechanism for the catalyzed reaction. We use the method to show that tertiary amines and phosphines are potential catalysts for the MBH reaction by screening a selection of 11 molecular templates representing common functional groups. The presented method relies on an automated reaction discovery workflow using meta-dynamics calculations. Combining this method for catalyst candidate discovery with our GA-based catalyst optimization method results in an algorithm for truly de novo catalyst discovery Catalysis ; DABCO ; Chemistry ; Combinatorial chemistry ; Octane ; Drug discovery ; Identification (biology) ; Iminium ; Workflow ; Computer science ; Organic chemistry ; Database ; Biology Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Computational Drug Discovery Methods
Authors: Longhui Yu | Hugh Nakamura
Source: JACS Au (article, 2023-11-09)
Field: Chemistry — Organic Chemistry

Short, Scalable Access to Pyrrovobasine A concise gram-scale synthesis of pyrrovobasine (<b>1</b>) is reported. Key transformations include a three-step decagram-scale synthesis of the tetracyclic compound, Mn-mediated direct radical cyclization, and the introduction of a naturally rare pyrraline structure. The synthesis is designed to be applicable to gram-scale synthesis using inexpensive and readily available reagents. Gram ; Combinatorial chemistry ; Scalability ; Scale (ratio) ; Computer science ; Key (lock) ; Chemistry ; Nanotechnology ; Materials science ; Physics ; Database ; Operating system ; Biology Radical Photochemical Reactions ; Catalytic C–H Functionalization Methods ; Oxidative Organic Chemistry Reactions
Authors: Peter Warren | Nandhini Raju | Abhilash Prasad | Shajahan Hossain | Ramesh Subramanian | Jayanta Kapat | Navin Jose Manjooran | Ranajay Ghosh
Source: arXiv (Cornell University) (preprint, 2023-07-12)
Field: Materials Science — Metals and Alloys

Grain and Grain Boundary Segmentation using Machine Learning with Real and Generated Datasets We report significantly improved accuracy of grain boundary segmentation using Convolutional Neural Networks (CNN) trained on a combination of real and generated data. Manual segmentation is accurate but time-consuming, and existing computational methods are faster but often inaccurate. To combat this dilemma, machine learning models can be used to achieve the accuracy of manual segmentation and have the efficiency of a computational method. An extensive dataset of from 316L stainless steel samples is additively manufactured, prepared, polished, etched, and then microstructure grain images were systematically collected. Grain segmentation via existing computational methods and manual (by-hand) were conducted, to create "real" training data. A Voronoi tessellation pattern combined with random synthetic noise and simulated defects, is developed to create a novel artificial grain image fabrication method. This provided training data supplementation for data-intensive machine learning methods. The accuracy of the grain measurements from microstructure images segmented via computational methods and machine learning methods proposed in this work are calculated and compared to provide much benchmarks in grain segmentation. Over 400 images of the microstructure of stainless steel samples were manually segmented for machine learning training applications. This data and the artificial data is available on Kaggle. Artificial intelligence ; Computer science ; Segmentation ; Machine learning ; Convolutional neural network ; Pattern recognition (psychology) ; Image segmentation ; Noise (video) ; Computer vision ; Image (mathematics) Hydrogen embrittlement and corrosion behaviors in metals ; Welding Techniques and Residual Stresses ; Metal and Thin Film Mechanics
Authors: Anton Kovyakh | Soham Banerjee | Chia-Hao Liu | Christopher J. Wright | Yuguang Li | Thomas E. Mallouk | R. Feidenhans’l | Simon J. L. Billinge
Source: Journal of Applied Crystallography (article, 2023-07-17)
Field: Materials Science — Materials Chemistry

Towards scanning nanostructure X-ray microscopy This article demonstrates spatial mapping of the local and nanoscale structure of thin film objects using spatially resolved pair distribution function (PDF) analysis of synchrotron X-ray diffraction data. This is exemplified in a lab-on-chip combinatorial array of sample spots containing catalytically interesting nanoparticles deposited from liquid precursors using an ink-jet liquid-handling system. A software implementation is presented of the whole protocol, including an approach for automated data acquisition and analysis using the atomic PDF method. The protocol software can handle semi-automated data reduction, normalization and modeling, with user-defined recipes generating a comprehensive collection of metadata and analysis results. By slicing the collection using included functions, it is possible to build images of different contrast features chosen by the user, giving insights into different aspects of the local structure. Slicing ; Computer science ; Software ; Synchrotron ; Data reduction ; Nanostructure ; Metadata ; Nanoscopic scale ; Profiling (computer programming) ; Normalization (sociology) ; Rapid prototyping ; Materials science ; Nanotechnology ; Computer graphics (images) ; Data mining ; Optics ; Physics ; Programming language X-ray Diffraction in Crystallography ; Iron oxide chemistry and applications ; Machine Learning in Materials Science
Authors: Baptiste Py | Adeleke Maradesa | Francesco Ciucci
Source: ChemRxiv (preprint, 2023-11-16)
Field: Chemistry — Electrochemistry

Theoretical and Experimental Study of the Distribution of Capacitive Times The distribution of relaxation times (DRT) method is a non-parametric approach for analyzing electrochemical impedance spectroscopy (EIS) data. However, we must be careful when using the DRT method on electrochemical systems with blocking electrodes, such as those encountered in batteries and supercapacitors. This is because at low frequencies the asymptotic behavior of the DRT model cannot capture unbounded impedances. To address this issue, we explore the distribution of capacitive times (DCT), a method that, despite being developed decades ago, is still not widely used. In this work, we detail the theoretical underpinnings of the DCT, deriving DCT-specific analytical formulae based on several standard impedance models. We also draw parallels between DCT and DRT and show how these two methods differ in capturing timescales and peaks, elucidating the scenarios where DCT can serve as a viable alternative should the DRT not be applicable. This article seeks to expand the scope of nonparametric approaches for EIS data analysis, particularly to systems characterized by blocking electrodes. Capacitive sensing ; Electrical impedance ; Parametric statistics ; Computer science ; Discrete cosine transform ; Parallels ; Blocking (statistics) ; Point (geometry) ; Relaxation (psychology) ; Dielectric spectroscopy ; Parametric model ; Electronic engineering ; Electrode ; Mathematics ; Electrochemistry ; Electrical engineering ; Artificial intelligence ; Physics ; Engineering ; Mechanical engineering ; Geometry ; Statistics Electrochemical Analysis and Applications ; Advanced Battery Technologies Research ; Conducting polymers and applications