jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nerdy Nerdy | Linda Margata | Nilsya Febrika Zebua | Puji Lestari | Tedy Kurniawan Bakri | Faisal Yusuf | Vonna Aulianshah
Source: Science & Technology Indonesia (article, 2023-07-06)
Field: Chemistry — Analytical Chemistry

Development and Validation of Fast and Simple Fourier Transform Infrared Spectrophotometric Method for Analysis of Thiamphenicol in Capsule Dosage Form The development of a method for identification and determination of thiamphenicol by Fourier Transform Infrared will provide convenience to developers because it is fast and easy for analysis. The research was carried out by utilizing the solubility of thiamphenicol in methanol with three stages, namely method development, sample analysis, and method validation. The method development stage showed that the specific peak of thiamphenicol was at a peak with a wavenumber of 1694.1 cm−1; this specific peak of thiamphenicol was used for qualitative analysis and quantitative analysis of thiamphenicol in the capsule dosage form. The sample analysis showed that all analyzed thiamphenicol in capsule dosage form showed good results both qualitatively and quantitatively. Qualitatively all the samples analyzed showed a specific peak at specific positions and specific wavenumbers. These results meet the requirements for containing thiamphenicol in the dosage form. Quantitatively all the samples analyzed ranged from 97.97% to 102.24% by peak height and peak area. These results meet the requirements for active substance levels in general preparations within 90.0% to 110.0%. The method validation for peak height and peak area showed that the accuracy parameter had a recovery percentage of 100.28% and 100.41% (between 98.0% to 102.0%), the precision parameter with a relative standard deviation of 0.31% and 0.37% (not more than 2.0%), and the linearity parameter with a correlation coefficient of 0.9999 and 0.9997 (not less than 0.99). The limit of detection value was 0.2971 mg/mL and 0.5338 mg/mL, the limit of quantitation value was 0.9004 mg/mL and 1.6176 mg/mL, the range for both was 80% to 120%, and the specificity for both met the requirement. The Fourier Transform Infrared method has been successfully developed, applied, and validated for qualitative analysis and quantitative analysis of thiamphenicol in capsule dosage form. Thiamphenicol ; Dosage form ; Chromatography ; Wavenumber ; Quantitative analysis (chemistry) ; Analytical Chemistry (journal) ; Fourier transform ; Chemistry ; Mathematics ; Materials science ; Physics ; Chloramphenicol ; Optics ; Mathematical analysis Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; thermodynamics and calorimetric analyses
Authors: Sonone Priti D. | Khan Subur W.
Source: Journal of Innovations in Pharmaceutical and Biological Sciences (review, 2023-01-01)
Field: Chemistry — Analytical Chemistry

A comprehensive review on standardization of Khus powder by analytical method Vetiver (Chrysopogon zizanioides) is a plant that has been used in a variety of disciplines, including medicine, aromatherapy, trade, environmental protection, and agriculture, due to its historic importance and versatility. Vetiver has a rich history in traditional medical practises; nevertheless, just a few research studies have confirmed its usefulness in illness therapy. The goal of this study was to gather current data on phytochemistry and pharmacology. It was discovered that several traditional vetivers use were validated when examined using various disease-based pharmacological models with extracts of roots, leaves, and root oil as test materials. Plants are valued in the pharmaceutical business for their structural diversity and vast variety of pharmacological activity. Phytochemicals refer to physiologically active substances found in plants. These phytochemicals serve as direct medicinal agents. This research focuses on the collecting of Vetiver plants, the qualitative and quantitative analysis of the phytochemicals, and the technique of detection Standardization ; Materials science ; Computer science ; Process engineering ; Engineering Heavy Metals in Plants ; Essential Oils and Antimicrobial Activity ; Psidium guajava Extracts and Applications
Authors: Rochus Schmid | Larissa Schaper
Source: Research Square (preprint, 2023-04-19)
Field: Chemistry — Inorganic Chemistry

Simulating the Breathing Phase Transitions of Metal-organic Frameworks : Volume Control of Nanocrystallites Abstract Flexible metal-organic frameworks (MOFs) can undergo structural transitions with significant pore volume changes upon guest adsorption or other external triggers while maintaining their porosity. In computational studies of this breathing behavior, molecular dynamics (MD) simulations within periodic boundary conditions (PBCs) are commonly performed. However, to account for the finite size and surface effects affecting the phase transition mechanism, the simulation of non-periodic nanocrystallite (NC) models without the constraint of PBCs is an important alternative. In this study, we present an approach allowing the analysis and control of the volume of finite-size structures during MD simulations by a tetrahedral tessellation of the (deformed) NC's volume. The new method allows for defining the current NC's volume during the simulation and manipulating it regarding a particular reference volume to compute free energies for the phase transformation via umbrella sampling. The application on differently sized DMOF‑1 and DUT‑128 NCs reveals flexible pore closing mechanisms without significant biasing of the transition pathway. The concept provides the theoretical foundation for further research on flexible materials regarding targeted initialization of the breathing phase behavior to elucidate the underlying mechanism, which can be used to improve the applications of flexible materials by targeted controlling of the phase transition. Phase (matter) ; Volume (thermodynamics) ; Metal-organic framework ; Breathing ; Materials science ; Control (management) ; Phase transition ; Nanotechnology ; Condensed matter physics ; Chemistry ; Thermodynamics ; Computer science ; Physics ; Physical chemistry ; Psychology ; Organic chemistry ; Adsorption ; Artificial intelligence Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Advanced NMR Techniques and Applications
Authors: Wenlin Chen | Julien Horwood | Juyeon Heo | José Miguel Hernández-Lobato
Source: arXiv (Cornell University) (preprint, 2023-06-26)
Field: Materials Science — Materials Chemistry

Leveraging Task Structures for Improved Identifiability in Neural Network Representations This work extends the theory of identifiability in supervised learning by considering the consequences of having access to a distribution of tasks. In such cases, we show that linear identifiability is achievable in the general multi-task regression setting. Furthermore, we show that the existence of a task distribution which defines a conditional prior over latent factors reduces the equivalence class for identifiability to permutations and scaling of the true latent factors, a stronger and more useful result than linear identifiability. Crucially, when we further assume a causal structure over these tasks, our approach enables simple maximum marginal likelihood optimization, and suggests potential downstream applications to causal representation learning. Empirically, we find that this straightforward optimization procedure enables our model to outperform more general unsupervised models in recovering canonical representations for both synthetic data and real-world molecular data. Identifiability ; Computer science ; Machine learning ; Artificial intelligence ; Feature learning ; Representation (politics) ; Task (project management) ; Equivalence (formal languages) ; Conditional probability distribution ; Simple (philosophy) ; Theoretical computer science ; Mathematics ; Econometrics Machine Learning in Materials Science ; Neural Networks and Applications ; Computational Drug Discovery Methods
Authors: Penghua Ying | Zheyong Fan
Source: arXiv (Cornell University) (preprint, 2023-10-08)
Field: Materials Science — Materials Chemistry

Combining the D3 dispersion correction with the neuroevolution machine-learned potential Machine-learned potentials (MLPs) have become a popular approach of modelling interatomic interactions in atomistic simulations, but to keep the computational cost under control, a relatively short cutoff must be imposed, which put serious restrictions on the capability of the MLPs for modelling relatively long-ranged dispersion interactions. In this paper, we propose to combine the neuroevolution potential (NEP) with the popular D3 correction to achieve a unified NEP-D3 model that can simultaneously model relatively short-ranged bonded interactions and relatively long-ranged dispersion interactions. We show the improved descriptions of the binding and sliding energies in bilayer graphene can be obtained by the NEP-D3 approach compared to the pure NEP approach. We implement the D3 part into the GPUMD package such that it can be used out of the box for many exchange-correlation functionals. As a realistic application, we show that dispersion interactions result in approximately a 10% reduction in thermal conductivity for three typical metal-organic frameworks. Dispersion (optics) ; Computer science ; Reduction (mathematics) ; Artificial intelligence ; Statistical physics ; Mathematical optimization ; Physics ; Mathematics Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Memory and Neural Computing
Authors: Arash Ramian | Rani Elhajjar
Source:  (preprint, 2023-11-26)
Field: Materials Science — General Materials Science

Machine Learning Algorithms for Identification of Cohesive Zone Parameters for Mixed-Mode Fracture in Composite Sandwich Structure We investigate three machine-learning algorithms to identify the cohesive zone parameters used in the fracture analysis of a honeycomb/carbon-epoxy sandwich structure.Determining the cohesive parameters from experimental results can be complicated because multiple parameters can fit the experimental results equally well, resulting in non-unique solutions that do not capture the correct values.Numerically, determining the cohesive zone parameters can be time-consuming due to modeling challenges, such as fine meshes needed near the crack tip.In this study, we apply three machine learning algorithms, namely, Support Vector Regression (SVR), Random Forest (RF), and Artificial Neural Network (ANN), to identify the interfacial cohesive parameters between the honeycomb core and carbon-epoxy facesheets.Our study represents the interfacial fracture using a cohesive zone model in a finite element (FE) simulation of the Asymmetric Double Cantilever Beam (ADCB) specimen configuration.The input variables include four cohesive zone parameters: the maximum normal contact stress, critical fracture energy for normal separation, maximum equivalent tangential contact stress, and critical fracture energy for tangential slip.FE simulations with ranges of cohesive model parameters produce a database of load-displacement responses that serve as training data for the machine learning algorithms.The results show that the suggested methods demonstrate remarkable accuracy for situations where the interfacial characteristics lie within the training dataset or beyond and reveal the importance of specific parameters.Additionally, we perform hyperparameter tuning and optimization techniques to enhance the model's performance. Composite number ; Mode (computer interface) ; Identification (biology) ; Fracture (geology) ; Mixed mode ; Materials science ; Algorithm ; Composite material ; Computer science Material Properties and Applications ; Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability
Source: Chemical Communications (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Contents list Permissions Request permissions Contents list Chem. Commun., 2023, 59, 5635 DOI: 10.1039/D3CC90155F This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given. Read more about how to correctly acknowledge RSC content. Acknowledgement ; Chromatin structure remodeling (RSC) complex ; Computer science ; Attribution ; World Wide Web ; Library science ; Chemistry ; Computer security ; Psychology Radioactive element chemistry and processing
Authors: T. A. Gomktsyan | Angelina Khachatryan | Diana Avakyan | Lernik Hunanyan | Roza S. Shainova | A. V. Karapetyan | Emma A. Ghazaryan | A. S. Vorskanyan | Margarita Dovlatyan | Yana Gharibyan | Aleksandr P. Yengoyan | V. A. Pivazyan
Source: Green Processing and Synthesis (article, 2023-01-01)
Field: Chemistry — Organic Chemistry

Ultrasound-assisted green synthesis and <i>in silico</i> study of 6-(4-(butylamino)-6-(diethylamino)-1,3,5-triazin-2-yl)oxypyridazine derivatives Abstract Eco-friendly ultrasound-assisted synthesis of a series of 3- N -substituted 6-((4-(butylamino)-6-(diethylamino)-1,3,5-triazin-2-yl)oxy)pyridazin-3(2 H )-one derivatives and in silico study to predict their biological activities were carried out. Physicochemical and pharmacokinetic properties were obtained. Absorption, distribution, metabolism, excretion and toxicity properties and bioavailability index were calculated. A comparative analysis of structural similarity based on the Tanimoto coefficient was carried out. In silico ; Bioavailability ; Chemistry ; Pharmacokinetics ; Ultrasound ; Absorption (acoustics) ; Organic chemistry ; Pharmacology ; Biochemistry ; Materials science Synthesis and Characterization of Heterocyclic Compounds ; Synthesis and biological activity ; Phenothiazines and Benzothiazines Synthesis and Activities
Authors: Ke Li
Source: theses.fr (ABES) (article, 2023-04-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Plasmonique accordable à base de nanostructures hybrides métal-diélectrique Dans cette thèse, nous utilisons des nanostructures hybrides métal-diélectriques (nanoouvertures) intégrées dans des cristaux liquides pour démontrer des couleurs plasmoniques accordables. Les nanoouvertures présentent un pic de transmission unique, ce qui permet d'obtenir des couleurs additives couvrant toute la gamme visible en modifiant les dimensions des structures. Des nanoapertures asymétriques peuvent en outre conférer des couleurs sensibles à la polarisation linéaire. Sur cette base, nous avons intégré des cristaux liquides nématiques sur les nanoouvertures convenablement arrangées, démontrant la commutation entre une image aux couleurs vives et une image aux couleurs délavées, contrôlée par le champ électrique appliqué et la polarisation incidente. En outre, l’utilisation d’un codage numérique binaire permet de crypter des motifs multicanaux dans des codes QR. Une couche de cristaux liquides dispersés dans un polymère a été introduite comme commutateur entre un état transparent et un état diffusif grâce à l’application d’un champ électrique. De plus, une approche simple de fabrication pour réaliser des nanostructures présentant un dichroïsme circulaire accordable a été étudiée. Nous démontrons une structure chirale composée de deux réseaux empilés avec un angle de rotation, présentant une valeur élevée de dichroïsme circulaire provenant du couplage entre la résonance plasmonique des première et deuxième couches après dépôt d’une couche d'or. Agrégation ; Semiconductor materials ; Nanostructure ; Chirality (physics) Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Plasmonic and Surface Plasmon Research
Authors: Fubao Yang | Zeren Zhang
Source: arXiv (Cornell University) (preprint, 2023-09-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Diffusion metamaterials for plasma transport Plasma technology has found widespread applications in numerous domains, yet the techniques to manipulate plasma transport predominantly rely on magnetic control. In this review, we present a streamlined diffusion-migration method to characterize plasma transport. Based on this framework, the viability of the transformation theory for plasma transport is demonstrated. Highlighted within are three model devices designed to cloak, concentrate, and rotate plasmas without significantly altering the density profile of background plasmas. Additionally, insights regarding potential implications for novel physics are discussed. This review aims to contribute to advancements in plasma technology, especially in sectors like medicine and chemistry. Plasma ; Diffusion ; Physics ; Nanotechnology ; Statistical physics ; Materials science ; Nuclear physics ; Thermodynamics Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Energy Harvesting in Wireless Networks
Authors: Jérémie Zumer | Sébastien Lemieux
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-11-02)
Field: Chemistry — Spectroscopy

Pepid: a Highly Modifiable, Bioinformatics-Oriented Peptide Search Engine Abstract Motivation Current peptide search engines are optimized for wet-lab workflows, i.e. they operate in an “end-to-end” manner to achieve good identification results, not to be modified or provide algorithmic insight. This makes developing new software methods to solve problems in peptide identification methods difficult, often requiring a full engine rewrite. Recently, many deep learning methods were proposed as solutions to various parts of the peptide identification task, but virtually none of those methods have been implemented in any actual peptide search process. We believe that the lack of a reliable bioinformatics research platform for peptide identification that enables such integrations is slowing down proteomics research as a whole. Results We present pepid, a bioinformatics research-oriented peptide search engine. Unlike other search engines, pepid is specifically designed with ease of computational research in mind. Our design is highly flexible and allows easy modifications with little required software development expertise, allowing researchers to focus on analysing and improving peptide identification methods.It also takes recent computational trends into account, such as the recent slew of deep learning publications in proteomics, and features a multi-phased batched operations design that is more appropriate than the spectrum batch “end-to-end” designs of existing search engines for those approaches. We show that pepid is competitive with common engines in terms of both identification rates and runtime, forming a minimum required baseline to enable further identification research. Availability and Implementation Pepid is available as open source software under the MIT license at https://github.com/lemieux-lab/pepid . Other data referenced in the text is 3rd party. The selected yeast proteome can be found on SwissProt with accession ID UP000002311 while the human proteome’s accession ID is UP0000005640. The ProteomeTools spectra can be found in the PRIDE archive under accession D PXD004732 and the One Hour Yeast Proteome can be found at the ChorusProject at https://chorusproject.org/anonymous/download/experiment/-8823069691100997209 and https://chorusproject.org/anonymous/download/experiment/449795368199176159 . Identification (biology) ; Computer science ; Software ; Workflow ; Process (computing) ; Mascot ; Search engine ; Database search engine ; Software engineering ; Data mining ; Information retrieval ; Programming language ; Database Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Machine Learning in Bioinformatics
Authors: Tingting Wang | Zhenyu Xu | Huiqiang Hu | Huaxing Xu | Yuping Zhao | Xiaobo Mao
Source: Molecules (article, 2023-09-04)
Field: Chemistry — Analytical Chemistry

Identification of Turtle-Shell Growth Year Using Hyperspectral Imaging Combined with an Enhanced Spatial–Spectral Attention 3DCNN and a Transformer Turtle shell (<i>Chinemys reecesii</i>) is a prized traditional Chinese dietary therapy, and the growth year of turtle shell has a significant impact on its quality attributes. In this study, a hyperspectral imaging (HSI) technique combined with a proposed deep learning (DL) network algorithm was investigated for the objective determination of the growth year of turtle shells. The acquisition of hyperspectral images was carried out in the near-infrared range (948.72-2512.97 nm) from samples spanning five different growth years. To fully exploit the spatial and spectral information while reducing redundancy in hyperspectral data simultaneously, three modules were developed. First, the spectral-spatial attention (SSA) module was developed to better protect the spectral correlation among spectral bands and capture fine-grained spatial information of hyperspectral images. Second, the 3D convolutional neural network (CNN), more suitable for the extracted 3D feature map, was employed to facilitate the joint spatial-spectral feature representation. Thirdly, to overcome the constraints of convolution kernels as well as better capture long-range correlation between spectral bands, the transformer encoder (TE) module was further designed. These modules were harmoniously orchestrated, driven by the need to effectively leverage both spatial and spectral information within hyperspectral data. They collectively enhance the model's capacity to extract joint spatial and spectral features to discern growth years accurately. Experimental studies demonstrated that the proposed model (named SSA-3DTE) achieved superior classification accuracy, with 98.94% on average for five-category classification, outperforming traditional machine learning methods using only spectral information and representative deep learning methods. Also, ablation experiments confirmed the effectiveness of each module to improve performance. The encouraging results of this study revealed the potentiality of HSI combined with the DL algorithm as an efficient and non-destructive method for the quality control of turtle shells. Hyperspectral imaging ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Convolutional neural network ; Leverage (statistics) ; Spectral signature ; Feature learning ; Spatial analysis ; Spectral bands ; Remote sensing ; Geography Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality