jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yizhan Yang | Jiankang Chen | Yunbo Wang
Source: Micromachines (article, 2022-11-29)
Field: Materials Science — Materials Chemistry

The Limit Tuning Effects Exerted by the Mechanically Induced Artificial Potential Barriers on the I–V Characteristics of Piezoelectric PN Junctions A mechanically induced artificial potential barrier (MIAPB) in piezoelectric semiconductor devices is set up under the action of a pair of tensile/compressive mechanical loadings. Three factors, namely, the barrier height, width and position, affect the nature and extent of interaction between the MIAPB and the contact barrier, and the tuning characteristics (generated under conditions of the artificial barrier) of the piezoelectric PN junctions were studied. The influence of these factors resulted in variations in the interaction intensities, superposition effects, carrier inversion degrees and carrier redistribution ranges. Subsequently, the limit tuning effects exerted by the tensile/compressive-mode MIAPB on the PN junctions were studied. The inconsistency between the left and right end of the tensile-mode MIAPB under conditions of the offset loading state proves that the maximum tuning effect is generated when both sides of the interface are symmetrically loaded. The range of carrier redistribution and the over-inversion of local carriers, affected by the width and height of MIAPB, result in a second competitive mechanism. The carrier redistribution range and the carrier inversion degree require that the compressive-mode MIAPB be sufficiently wide. The interaction intensities and the superposition effects, affected by the position and height of the MIAPB, contribute to the second competing mechanism. We logically clarify the relationship between multiple competition and find that the emergence of multiple competitive mechanisms proves the existence of the limit tuning effect of MIAPB on the <i>I</i>-<i>V</i> properties of PN junctions. The results reported herein provide a platform for understanding the mechanical tuning laws governing the functions of piezoelectric PN junctions and piezoelectric devices. Superposition principle ; Piezoelectricity ; Materials science ; Ultimate tensile strength ; Redistribution (election) ; Nanotechnology ; Optoelectronics ; Composite material ; Physics Thermal properties of materials ; Acoustic Wave Resonator Technologies ; Adhesion, Friction, and Surface Interactions
Authors: Qian Xu | Wen Li | Yajun Yin | Jianxin Zhou | Hai Nan
Source: China Foundry (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Finite element simulation of real cavity closure in cast Ti6Al4V alloy during hot isostatic pressing The healing behavior of shrinkage cavity inside the cast Ti6Al4V alloy during hot isostatic pressing (HIP) was investigated experimentally by interrupted hot isostatic pressing tests. The X-ray micro computed tomography was used to record the morphology changes before and after hot isostatic pressing. The two-dimensional geometry obtained by the microCT scan was used in simulation to study the evolution of the real shrinkage cavity during hot isostatic pressing. Shrinkage cavities, shrinkage porosity and small gas pores can be effectively eliminated under proper HIP conditions. The two-dimensional morphology in the simulation results agrees well with the experimental results. This study reveals that plastic deformation, creep and diffusion are the main mechanisms of cavity closure during hot isostatic pressing. In addition, the simplified elliptical pores with aspect ratios at different positions were used to replace the real pores to further study the factors affecting the position of dimples after HIP by simulation. It is found that the position of the dimples mainly depends on the aspect ratio of the elliptical pore and the distance between the pore surface and the external surface of the geometric model. Hot isostatic pressing ; Materials science ; Shrinkage ; Titanium alloy ; Porosity ; Dimple ; Finite element method ; Composite material ; Alloy ; Pressing ; Deformation (meteorology) ; Structural engineering Titanium Alloys Microstructure and Properties ; Orthopaedic implants and arthroplasty ; Advanced materials and composites
Authors: Nasrin Zahedi | Bahare Dehdashti | Farzaneh Mohammadi | Maryam Razaghi | Zeynab Moradmand | Mohammad Mehdi Amin
Source: International Journal of Chemical Engineering (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Using Sono‐Electro‐Persulfate Process for Atenolol Removal from Aqueous Solutions: Prediction and Optimization with the ANFIS Model and Genetic Algorithm Atenolol (ATN) is a drug that is widely used to treat some heart diseases, and since it cannot be completely decomposed in the human body, some amounts of it are found in surface water. These amounts may bring risks to the environment and humans, and for this reason, its removal is a must. In the present study, the combined sono‐electro‐persulfate method was used for ATN removal. Based on the design of the experiment conducted by response surface methodology (RSM), the effects of 5 main factors (pH, time, PS concentration, current intensity, and initial ATN concentration) have been investigated at 5 levels. After passing the test steps in different conditions, the remaining amount of ATN has been measured by high‐performance liquid chromatography (HPLC). Finally, an adaptive neuro‐fuzzy inference system (ANFIS) with 99.63% accuracy and a genetic algorithm (GA) were used to analyze and interpret data and predict optimal conditions. The obtained results indicate the possibility of a maximum efficiency of 99.8% in the mentioned conditions (Ph of 7.4, time of 18 min, PS concentration of 2000 mg/L, current intensity of 3.35 A, and initial ATN concentration of 11.2 mg/L). According to the obtained results, the initial concentration of ATN can be considered as the most effective factor in this process, and the best Ph range for this experiment was the neutral range. The sono‐electro persulfate process can be mentioned as a new and effective method for removing ATN from water sources. Persulfate ; Response surface methodology ; Aqueous solution ; Adaptive neuro fuzzy inference system ; Process (computing) ; Genetic algorithm ; Range (aeronautics) ; Box–Behnken design ; Algorithm ; Chromatography ; Computer science ; Chemistry ; Biological system ; Fuzzy logic ; Control theory (sociology) ; Materials science ; Fuzzy control system ; Machine learning ; Artificial intelligence ; Control (management) Ultrasound and Cavitation Phenomena ; Advanced oxidation water treatment ; Gas Sensing Nanomaterials and Sensors
Authors: Emanuel Gonçalves | Rebecca C. Poulos | Zhaoxiang Cai | Syd Barthorpe | Srikanth S. Manda | Natasha Lucas | Alexandra Beck | Daniel Bucio-Noble | Michael Dausmann | Caitlin Hall | Michael Hecker | Jennifer M.S. Koh | Sadia Mahboob | Iman Mali | James Morris | Laura Richardson | Akila J. Seneviratne | Erin K. Sykes | Frances Thomas | Sara Valentini | Steven G. Williams | Yangxiu Wu | Dylan Xavier | Karen L. MacKenzie | Peter G. Hains | Brett Tully | Phillip J. Robinson | Qing Zhong | Mathew J. Garnett | Roger R. Reddel
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-03-01)
Field: Chemistry — Spectroscopy

Pan-cancer proteomic map of 949 human cell lines reveals principles of cancer vulnerabilities Summary The proteome provides unique insights into biology and disease beyond the genome and transcriptome. Lack of large proteomic datasets has restricted identification of new cancer biomarkers. Here, proteomes of 949 cancer cell lines across 28 tissue types were analyzed by mass spectrometry. Deploying a clinically-relevant workflow to quantify 8,498 proteins, these data capture evidence of cell type and post-transcriptional modifications. Integrating multi-omics, drug response and CRISPR-Cas9 gene essentiality screens with a deep learning-based pipeline revealed thousands of protein-specific biomarkers of cancer vulnerabilities. Proteomic data had greater power to predict drug response than the equivalent portion of the transcriptome. Further, random downsampling to only 1,500 proteins had limited impact on predictive power, consistent with protein networks being highly connected and co-regulated. This pan-cancer proteomic map (ProCan-DepMapSanger), available at https://cellmodelpassports.sanger.ac.uk , is a comprehensive resource revealing principles of protein regulation with important implications for future clinical studies. Proteome ; Computational biology ; Transcriptome ; Biology ; Proteomics ; Cancer ; Workflow ; Identification (biology) ; Proteogenomics ; Bioinformatics ; Gene ; Computer science ; Genetics ; Gene expression ; Database Advanced Proteomics Techniques and Applications ; Bioinformatics and Genomic Networks ; Ubiquitin and proteasome pathways
Authors: Alberto Celma | Richard Bade | Juan V. Sancho | Félix Hernández | Melissa Humpries | Lubertus Bijslma
Source: Research Square (preprint, 2022-01-13)
Field: Chemistry — Analytical Chemistry

Prediction of Retention Time and Collision Cross Section (CCSH+, CCSH- and CCSNa+) of emerging contaminants using Multiple Adaptive Regression Splines Abstract Ultra-high performance liquid chromatography coupled to ion mobility separation and high-resolution mass spectrometry instruments have proven very valuable for screening of emerging contaminants in the aquatic environment. However, when applying suspect or non-target approaches ( i.e. when no reference standards are available) there is no information on retention time (RT) and collision cross section (CCS) values to facilitate identification. In-silico prediction tools of RT and CCS can therefore be of great utility to decrease the number of candidates to investigate. In this work, Multiple Adaptive Regression Splines (MARS) was evaluated for the prediction of both RT and CCS. MARS prediction models were developed and validated using a database of 477 protonated molecules, 169 deprotonated molecules and 249 sodium adducts. Multivariate and univariate models were evaluated showing a better fit for univariate models to the empirical data. The RT model ( R 2 =0.855) showed a deviation between predicted and empirical data of ± 2.32 min (95% confidence intervals). The deviation observed for CCS data of protonated molecules using CCS H model ( R 2 =0.966) was ± 4.05% with 95% confidence intervals. The CCS H model was also tested for the prediction of deprotonated molecules resulting in deviations below ± 5.86% for the 95% of the cases. Finally, a third model was developed for sodium adducts (CCS Na , R 2 =0.954) with deviation below ± 5.25% for the 95% of the cases. The developed models have been incorporated in an open access and user-friendly online platform which represents a great advantage for third-party research laboratories for predicting both RT and CCS data. Collision ; Section (typography) ; Regression ; Cross section (physics) ; Retention time ; Computer science ; Statistics ; Environmental science ; Mathematics ; Chromatography ; Chemistry ; Physics ; Computer security Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Air Quality Monitoring and Forecasting
Authors: Akara Thammastitkul | Jitsanga Petsuwan
Source: IAES International Journal of Artificial Intelligence (article, 2022-12-12)
Field: Chemistry — Analytical Chemistry

Thai Hom Mali rice grading using machine learning and deep learning approaches &lt;span lang="EN-US"&gt;Thai Jasmine rice or Thai Hom Mali rice is a well-known rice type that originated in Thailand. Rice grain qualities are important in determining market pricing and are used in grading systems. The purpose of this research is to use machine learning and deep learning to improve the grading of Thai Hom Mali rice following standardized grading criteria. The appearance of grains and foreign items will determine the grade of rice. The experiment has two parts: grain categorization and rice grading. Multi-class support vector machine (SVM) and convolutional neural network (CNN) are proposed. There are 15 features used as input for multi-class SVM, including morphology and color features. With ImageNet pre-trained weights, CNN with DenseNet201 architecture is implemented. The experiment also tested into how CNN worked with both original and preprocessed images. The results are then compared to a neural network (NN) baseline approach. The CNN approach, which identified each rice variety using preprocessed images, archieved the greatest accuracy rate of 98.25%, with an average accuracy of 94.52% across six categories of rice grading.&lt;/span&gt; Grading (engineering) ; Artificial intelligence ; Convolutional neural network ; Computer science ; Support vector machine ; Categorization ; Deep learning ; Pattern recognition (psychology) ; Artificial neural network ; Machine learning ; Engineering Spectroscopy and Chemometric Analyses ; GABA and Rice Research
Authors: Adithya S. Charan
Source: Meat Technology (review, 2022-01-01)
Field: Chemistry — Analytical Chemistry

A Review of Hyperspectral Imaging in the Quality Evaluation of Meat, Fish, Poultry and Their Products A b s t r a c t: Meat and meat products are rich sources of nutrition in the daily diet. Quality and safety assessments of foods, including meats, are essential due to their perishability and vulnerability. The need to analyse food products in real time has stimulated the invention of non-destructive measuring systems. Hyperspectral imaging (HSI) combined with various statistical analysis methods such as multiple linear regression (MLR), least squares-support vector machine (LS-SVM), or partial least squares regression (PLSR), was created as a rapid, non-destructive, non-intrusive and chemical-free process to determine important quality aspects and chemo metrics of foods. The HSI system is used to collect spectral and spatial data. This review discusses the recent developments and application of HSI for detecting quality and safety attributes of tenderness, colour, pH, moisture content, marbling, fat, microbial level and adulteration in meat, fish and poultry meat and products. Overall, HSI technology has tremendous potential to classify different parameters in meat and its products. Hyperspectral imaging ; Fish <Actinopterygii> ; Poultry meat ; Quality (philosophy) ; Fishery ; Environmental science ; Food science ; Biology ; Computer science ; Artificial intelligence ; Physics Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality
Authors: Sujal Shelke | Monika Ingole | Priyanka Yadav | Minakshi Dobale
Source: International Journal of Innovations in Engineering and Science (article, 2022-05-18)
Field: Chemistry — Analytical Chemistry

Fruits Classification On The Basis of Different Diseases and Quality Using Deep Learning The automatic fruit quality monitoring system will be used to filter and sort various fruits. The main purpose of this program is to replace the manual testing program. This will help speed up the process, improve accuracy and reduce efficiency. In recent years, researchers have come up with innumerable contradictory ways of distinguishing fruits. Color, texture and morphological features are the most commonly used to identify the stage of fruit diseases. The proposed system will collect a camera image placed in front of a portable computer or mobile phone and after processing the images we will specify the type of fruit. The main emphasis in this work is the purchase of an analysis of the methods of diagnosing various fruit diseases. After that, these features are used to train the system module. In order to train the system we need a database of different fruits. We will therefore collect this fruit database from the data library. The database contains information and images of fruits that describe their quality and type. An in-depth reading module will read this database and learn the content / features of various fruits. The fruit element will be highlighted to show the type of fruit and the quality of the fruit will also stand out. The classification will be based on the shape, size and color of the fruit Basis (linear algebra) ; Quality (philosophy) ; Artificial intelligence ; Computer science ; Machine learning ; Mathematics ; Epistemology ; Philosophy Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Zhuo Liu | Yanjie Li
Source: Journal of Fungi (article, 2022-09-19)
Field: Chemistry — Analytical Chemistry

Fungi Classification in Various Growth Stages Using Shortwave Infrared (SWIR) Spectroscopy and Machine Learning Dark septate endophytes (DSEs) fungi are beneficial to host plants with regard to abiotic stress. Here, we examined the capability of SWIR spectroscopy to classify fungus types and detected the growth stages of DSEs fungi in a timely, non-destructive and time-saving manner. The SWIR spectral data of five DSEs fungi in six growth stages were collected, and three pre-processing methods and sensitivity analysis (SA) variable selection methods were performed using a machine learning model. The results showed that the De-trending + first Derivative (DET_FST) processing spectra combined with the support vector machine (SVM) model yielded the best classification accuracy for fungi classification at different growth stages and growth stage detection on different fungus types. The mean accuracy of generic model for fungi classification and growth stage detection are 0.92 and 0.99 on the calibration set, respectively. Seven important bands, 1164, 1456, 2081, 2272, 2278, 2448 and 2481 nm, were found to be related to the SVM fungi classification. This study provides a rapid and efficient method for the classification of fungi in different growth stages and the detection of fungi growth stage of various types of fungi and could serve as a tool for fungi study. Support vector machine ; Fungus ; Artificial intelligence ; Biology ; Pattern recognition (psychology) ; Botany ; Machine learning ; Biological system ; Computer science Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Essential Oils and Antimicrobial Activity
Authors: Sk Aftabul Alam | Pradipta Saha
Source: Journal of Experimental Biology and Agricultural Sciences (article, 2022-12-31)
Field: Materials Science — Materials Chemistry

Functional, and phylogenetic analysis of maleylacetate reductase of Pseudomonas sp strain PNPG3: An in-silico approach Shrinking freshwater ecosystems are under tremendous pollution threat due to anthropocentric activities. Para nitrophenol (PNP), a well-documented priority pollutant extensively used in dyes, petrochemical, pharmaceutical, explosives, pesticides, leather industries, and agrochemicals, is responsible for contaminating aquatic ecosystems globally. It is highly toxic and has carcinogenic and mutagenic effects on living organisms like humans and several animal models. Bioremediation approaches mainly involving bacteria are considered the best, most eco-friendly, cost-effective, green, and clean method for effective removal PNP from its contaminated sites. This manuscript highlights the structural and functional analysis of a lower pathway enzyme involved in PNP degradation, maleylacetate reductase (MR), from Pseudomonas sp strain PNPG3, which was recently isolated from a freshwater ecosystem. This enzyme plays a role in converting maleylacetate to 3-oxoadipate. Despite its crucial functional role, no model is available for this protein in the protein database (PDB). Therefore, attempts were made for the computational investigation of physicochemical, functional, and structural properties, including secondary, and tertiary structure prediction, model quality analysis, and phylogenetic assessment using several standard bioinformatics tools. This enzyme has a molecular weight of about ~37.6 kDa, is acidic and thermostable, belonging to a member of iron-containing alcohol dehydrogenase. Moreover, this study will benefit the scientific community in deciphering the prediction of the function of similar proteins of interest. Bioremediation ; In silico ; Biology ; Computational biology ; Chemistry ; Bacteria ; Biochemistry ; Genetics ; Gene Enzyme Structure and Function ; Microbial Metabolic Engineering and Bioproduction ; Bacterial Genetics and Biotechnology
Authors: Kate J. Putman | Matthew R. Rowles | Nigel A. Marks | Jacob W. Martin | Carla de Tomás | Irene Suarez‐Martinez
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Defining Graphenic Crystallites in Disordered Carbon: Moving Beyond the Platelet Model Crystallite ; Carbon fibers ; Materials science ; Nanotechnology ; Composite material ; Metallurgy ; Composite number Graphene research and applications ; Carbon Nanotubes in Composites ; Fiber-reinforced polymer composites
Authors: R. A. Sokolov | В. Ф. Новиков | Ilja Kovenskij | К. Р. Муратов | Anatolii Venediktov | Larisa Chaugarova
Source: Metal Working and Material Science (article, 2022-12-15)
Field: Materials Science — Materials Chemistry

The effect of heat treatment on the formation of MnS compound in low-carbon structural steel 09Mn2Si Introduction. The properties of steels are determined by many factors, including the manufacturing process and subsequent treatment. Some features of these processes lead to the fact that in steel, apart from alloying elements added to obtain a certain level of physical and mechanical properties, there are also foreign impurities that enter it at various stages. Foreign elements can not only dissolve in the matrix, but also participate in the formation of particles of nonmetallic inclusions acting as defects. Its presence significantly affects the performance characteristics of the material. That is why it is necessary to understand the processes that lead to the appearance of nonmetallic inclusions and affect its shape. Purpose: to consider the effect of heat treatment, leading to the appearance of a ferrite-martensitic structure, on the shape and size of nonmetallic inclusions; to determine its influence on the physical and mechanical properties of the material. In the work, samples of rolled steel 09Mn2Si after heat treatment are studied. Research methods. To study the properties and structure of steel 09Mn2Si, the following methods were used: scanning electron microscopy – to study the structure of the material, chemical composition in the local area and the site under study and to determine the accumulation of impurities; SIAMS 800 software and hardware complex – to compare the structure of the material with the atlas of microstructures, to determine the score of the grain structure, differences in the structural and phase composition occurring during heat treatment; portable X-ray fluorescence analyzer of metals and alloys X-MET 7000 - to determine the chemical composition of the samples under study in percentage terms; Vickers hardness tester with a preload of 20 kg – to measure the hardness of the samples under study. Results and discussions. It is found that in the low-alloy low-carbon structural steel 09Mn2Si in most cases there are nonmetallic inclusions of the type of manganese sulfide formed during its manufacture. When this steel is heated to the temperatures of the intercritical transition, this compound is formed in the area of grain boundaries in the form of spherical inclusions. The presence of these inclusions significantly affects the strength and corrosion properties. Manganese sulfide acts as the point of the corrosion process initiation. Microstructure ; Materials science ; Impurity ; Martensite ; Metallurgy ; Ferrite (magnet) ; Scanning electron microscope ; Grain size ; Carbon steel ; Composite material ; Corrosion ; Chemistry Material Properties and Failure Mechanisms ; Material Properties and Applications ; Microstructure and Mechanical Properties of Steels