jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Md. Fahim Bin Alam | Md. Touhidul Islam | Md. Monir Hossan | Md Mizanur Rahman
Source: Textile & Leather Review (article, 2022-11-23)
Field: Materials Science — Polymers and Plastics

Assessing the Impact of Speed Change on Energy Consumption and Productivity: Investigating Ring Frame Process Parameters A concern in the contemporary period is to find balance in the world's ever-increasing demand and its severely constricted output given by the present energy crisis brought on by the post-pandemic and ongoing Russia-Ukraine war. One of the most fundamental human needs is met by the production of textiles. Spinning is the first step in the production of textiles, where the ring frame machine is essential for creating yarn. In this study, a few process parameters were altered in a ring frame machine to boost production while saving time and ensuring financial feasibility. Comparative analysis was performed based on financial aspects as well as quality concerns like yarn count variation, Count Strength Product (CSP), and end yarn breakage rate. The increased machine speed of the spinning ring frame consumes higher energy but yields higher productivity without compromising quality parameters. The findings enable the spinning industry's decision-makers to understand better how process parameters change when machine speed varies, as well as offer policymakers a clear picture of the correlation between energy consumption and profitability. The machine produces roughly 420 kg/day consuming 567 kWh of energy at 14000 rpm, with 2.57% breakages/100 spindles/hour of 18.48 tex count variation and CSP of 1320. However, the production and energy usage of a machine at 15000 rpm, is approximately 449 kg and 630.44 kWh/day. Respectively, with breakages 3.06%/100 spindles/hour of count variation 18.62 tex and CSP of 1325. A potential increase in profit of 112.58 USD/machine/day, or 9682 BDT resulted from the increase in machine speed. Spinning ; Energy consumption ; Yarn ; Productivity ; Profitability index ; Consumption (sociology) ; Computer science ; Engineering ; Automotive engineering ; Agricultural engineering ; Mechanical engineering ; Business ; Economics ; Electrical engineering ; Finance Textile materials and evaluations ; Sustainable Supply Chain Management ; Additive Manufacturing and 3D Printing Technologies
Authors: Arulmurugan Senthilnathan | Iman Javaheri | Hengduo Zhao | Veera Sundararaghavan | Marc DeGraef | Pınar Acar
Source: Computational Materials Science (article, 2022-09-23)
Field: Materials Science — Materials Chemistry

Uncertainty quantification of metallic microstructures using principal image moments Uncertainty quantification ; Microstructure ; Gaussian ; Kriging ; Propagation of uncertainty ; Scale (ratio) ; Markov random field ; Materials science ; Measurement uncertainty ; Algorithm ; Texture (cosmology) ; Titanium alloy ; Statistical physics ; Computer science ; Mathematics ; Artificial intelligence ; Image (mathematics) ; Machine learning ; Alloy ; Physics ; Statistics ; Metallurgy ; Image segmentation Machine Learning in Materials Science ; Image Processing Techniques and Applications ; Metallurgy and Material Forming
Authors: Anuj Kumthekar | Sathiskumar A. Ponnusami | Sybrand van der Zwaag | Sergio Turteltaub
Source: Materials & Design (article, 2022-07-22)
Field: Materials Science — Materials Chemistry

Uncertainty quantification of the lifetime of self-healing thermal barrier coatings based on surrogate modelling of thermal cyclic fracture and healing Computationally-efficient surrogate models based on a Polynomial Chaos Expansion (PCE) are developed to quantify the uncertainties in the fracture behavior and lifetime of a self-healing thermal barrier coating system (SH-TBC) and a benchmark conventional TBC system. The surrogate models are built using deterministic information from micromechanical finite element simulations of thermal cycling of the systems, which are conducted until failure by spallation. Fracture and healing events are simulated using a cohesive-zone based crack healing model. The thermally-grown oxide layer (TGO) interface amplitude and its growth rate, the diameter and volume fraction of healing particles, and the mean distance of particles from the interface are used as training variables. Statistical characteristics and sensitivity indices are obtained from the trained models. It is found that the interface amplitude is the most significant contributor to the variance in the TBC lifetime, with other parameters displaying a relatively minor influence. Healing particles extend the expected value of TBC lifetime, however they also increase the uncertainty of thermal fatigue life. The analysis of self-healing TBCs exemplifies how PCE-based surrogate models can serve as a powerful tool for deriving design insights in complex material systems. Materials science ; Thermal barrier coating ; Spallation ; Temperature cycling ; Fracture (geology) ; Surrogate model ; Benchmark (surveying) ; Self-healing ; Composite material ; Volume fraction ; Amplitude ; Coating ; Uncertainty quantification ; Thermal ; Structural engineering ; Computer science ; Thermodynamics ; Optics ; Engineering Thermal properties of materials ; Fire dynamics and safety research ; Flame retardant materials and properties
Authors: Vivek Oommen | Khemraj Shukla | Somdatta Goswami | Rémi Dingreville | George Em Karniadakis
Source: arXiv (Cornell University) (preprint, 2022-04-11)
Field: Materials Science — Materials Chemistry

Learning two-phase microstructure evolution using neural operators and autoencoder architectures Phase-field modeling is an effective but computationally expensive method for capturing the mesoscale morphological and microstructure evolution in materials. Hence, fast and generalizable surrogate models are needed to alleviate the cost of computationally taxing processes such as in optimization and design of materials. The intrinsic discontinuous nature of the physical phenomena incurred by the presence of sharp phase boundaries makes the training of the surrogate model cumbersome. We develop a framework that integrates a convolutional autoencoder architecture with a deep neural operator (DeepONet) to learn the dynamic evolution of a two-phase mixture and accelerate time-to-solution in predicting the microstructure evolution. We utilize the convolutional autoencoder to provide a compact representation of the microstructure data in a low-dimensional latent space. DeepONet, which consists of two sub-networks, one for encoding the input function at a fixed number of sensors locations (branch net) and another for encoding the locations for the output functions (trunk net), learns the mesoscale dynamics of the microstructure evolution from the autoencoder latent space. The decoder part of the convolutional autoencoder then reconstructs the time-evolved microstructure from the DeepONet predictions. The trained DeepONet architecture can then be used to replace the high-fidelity phase-field numerical solver in interpolation tasks or to accelerate the numerical solver in extrapolation tasks. Autoencoder ; Solver ; Extrapolation ; Computer science ; Representation (politics) ; Interpolation (computer graphics) ; Artificial intelligence ; Deep learning ; Convolutional neural network ; Encoding (memory) ; Mesoscale meteorology ; Speedup ; Algorithm ; Computational science ; Parallel computing ; Mathematics ; Physics Solidification and crystal growth phenomena ; Magnetic Properties and Applications ; Metallurgy and Material Forming
Authors: David J. Sanabria‐Ríos | Denisse Alequin-Torres | Alenis Jesus | Giovanni Cortes | Néstor M. Carballeira | Victorio Jauregui Matos
Source: Medical Research Archives (article, 2022-01-01)
Field: Chemistry — Organic Chemistry

Vinyl halogenated fatty acids display antibacterial activity against clinical isolates of methicillin-resistant Staphylococcus aureus Methicillin-resistant <i>Staphylococcus aureus</i> is a pathogen responsible for skin and wound infections, pneumonia, and bloodstream infections. Serious attention is needed because Methicillin-resistant <i>S. aureus</i> is also resistant to many other commonly used antibiotics. This study explores the potential of vinyl halogenated fatty acids as antibacterial agents. Specifically, the total synthesis of vinyl halogenated fatty acids was performed to investigate their antibacterial activity against clinical isolates of methicillin-resistant <i>S. aureus</i>. The novel synthesis of the vinyl halogenated fatty acids was carried out by treating either 2-dodecynoic acid or 2-hexadecynoic acid with an allyl halide and 5 mol% of bis(benzonitrile)palladium (II) chloride as catalyst. Our results demonstrate that vinyl halogenated fatty acids displayed significant antibacterial activity against clinical isolates of methicillin-resistant <i>S. aureus</i> and low cytotoxicity against eukaryotic Vero Cells. Moreover, it was demonstrated that vinyl brominated fatty acids could disrupt the <i>S. aureus</i> plasma membrane and inhibit the expression of the <i>norB</i> gene. Staphylococcus aureus ; Antibacterial activity ; Microbiology ; Fatty acid ; Chemistry ; Methicillin-resistant Staphylococcus aureus ; Vinyl chloride ; Antibiotics ; Bacteria ; Biology ; Biochemistry ; Organic chemistry ; Copolymer Synthesis of Indole Derivatives ; Essential Oils and Antimicrobial Activity ; Antimicrobial agents and applications
Authors: Sankha Bhattacharya | Satyam Sharma | Bhupendra G. Prajapati
Source: Frontiers in Oncology (article, 2022-10-26)
Field: Materials Science — Biomaterials

Development of D-α-Tocopherol polyethylene glycol 1000 succinate fabricated nanostructural lipid carrier of sorafenib tosylate for metastatic colorectal targeting application: Stability, physical characterization, cytotoxicity, and apoptotic studies against SW48 cells PTEN The study aimed to create D-α-Tocopherol polyethylene glycol 1000 succinate (TPGS) nanostructured lipid carriers (NLC) of sorafenib tosylate (ST) as lymphatic delivery systems (LDDS) to fight Metastatic colorectal cancer. Initially, ST-SLN, ST-NLC, and ST-LNE were formulated considering oleic acid (OA), glycerol monolinoleate (GMO), glycerol monolinoleate (GML) as solid lipid and further characterised, and tested for stability. The most stable ST-NLC was fabricated with TPGS to produce ST-TPGS-NLC and evaluated by performing <i>in vitro</i> drug profiling, <i>in vitro</i> cytotoxicity, and apoptotic studies against human female colorectal adenocarcinoma cell lines (SW48 Cells PTEN). Stability studies on three lipidic nanoparticles (ST-SLN, ST-NLC, ST-LEN) showed particle size, polydispersity index, and zeta potential ranging from 165 nm to 298 nm, 0.125 to 0.288, and -31 mV to -16 mV. At 1600 minutes, more than 80% of ST-NLC1 was released, confirming the sustained release pattern of the formulation. ST-NLC and ST-TPGS-NLC have entrapment efficiencies above 50%. Pure ST's IC50 at 72 hr was 3.45 µg/mL, while 1.56 µg/mL was for ST-TPGS-NLC. The ST-TPGS-NLC reduced the number of livings SW48 Cells PTEN from 91% to 5%, compared to 75% to 8% of pure ST. The ST-TPGS-NLC is a promising LDDS for delivering ST for metastatic colorectal cancer. Polyethylene glycol ; Chemistry ; Cytotoxicity ; Sorafenib ; In vitro ; Pharmacology ; Cancer research ; Medicine ; Biochemistry ; Hepatocellular carcinoma Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; Curcumin's Biomedical Applications
Authors: Jonathan P. Wright | Carlotta Giacobbe | Eleanor Lawrence Bright
Source: Crystals (article, 2022-02-14)
Field: Materials Science — Materials Chemistry

Using Powder Diffraction Patterns to Calibrate the Module Geometry of a Pixel Detector The precision and accuracy of diffraction measurements with 2D area detectors depends on how well the experimental geometry is known. A method is described to measure the module geometry in order to obtain accurate strain data using a new Eiger2 4M CdTe detector. Smooth Debye–Scherrer powder diffraction rings with excellent signal to noise were collected by using a fine-grained sample of CeO2. From these powder patterns, the different components of the module alignment errors could be observed when the overall detector position was moved. A least squares fitting method was used to refine the detector module and scattering geometry for a series of powder patterns with different beam centers. A precision that is around 1/350 pixel for the module positions was obtained from the fit. This calibration was checked by free refinement of the unit cell of a silicon crystal that gave a maximum residual strain value of 2.1 × 10−5 as the deviation from cubic symmetry. Detector ; Calibration ; Optics ; Diffraction ; Geometry ; Pixel ; Powder diffraction ; Position (finance) ; Debye ; Materials science ; Scattering ; Goniometer ; Physics ; Mathematics X-ray Diffraction in Crystallography ; Advanced X-ray Imaging Techniques ; High-pressure geophysics and materials
Authors: Shadrack Barnabas | Timo Böhme | Stephen Boyer | Matthias Irmer | Christoph Ruttkies | Ian Wetherbee | Todor Kondić | Emma Schymanski | Lutz T. Weber
Source: ChemRxiv (preprint, 2022-05-18)
Field: Chemistry — Physical and Theoretical Chemistry

Extraction of Chemical Structures from Literature and Patent Documents using Open Access Chemistry Toolkits: A Case Study with PFAS The extraction of chemical information from documents is a demanding task in cheminformatics due to the variety of text and image-based representations of chemistry. The present work describes the extraction of chemical compounds with unique chemical structures from the open access CORE (COnnecting REpositories) and Google Patents full text document repositories. The importance of structure normalization is demonstrated using three open access cheminformatics toolkits: the Chemistry Development Kit (CDK), RDKit and OpenChemLib (OCL). Each toolkit was used for structure parsing, normalization and subsequent substructure searching, using SMILES as structure representations of chemical molecules and International Chemical Identifiers (InChIs) for comparison. Per- and polyfluoroalkyl substances (PFAS) were chosen as a case study to perform the substructure search, due to their high environmental relevance, their presence in both literature and patent corpuses, and the current lack of community consensus on their definition. Three different structural definitions of PFAS were chosen to highlight the implications of various definitions from a cheminformatics perspective. Since CDK, RDKit and OCL implement different criteria and methods for SMILES parsing and normalization, different numbers of parsed compounds were extracted, which were then evaluated using the three PFAS definitions. A comparison of these toolkits and definitions is provided, along with a discussion of the implications for PFAS screening and text mining efforts in cheminformatics. Finally, the extracted PFAS (~1.7 M PFAS from patents and ~27K from CORE) were compared against various existing PFAS lists and are provided in various formats for further community research efforts. Cheminformatics ; Computer science ; Parsing ; Information retrieval ; Normalization (sociology) ; Data science ; Chemistry ; Artificial intelligence History and advancements in chemistry ; Chemistry and Chemical Engineering ; Various Chemistry Research Topics
Authors: Adam Karpiński | Julio César Torres Elguera | Anne Sanner | Witold Konopka | Leszek Kaczmarek | Dominic Winter | Anna Konopka | Ewa Bulska
Source: Biomedicines (article, 2022-10-02)
Field: Chemistry — Spectroscopy

Study on Tissue Homogenization Buffer Composition for Brain Mass Spectrometry-Based Proteomics Mass spectrometry-based proteomics aims to study the proteome both qualitatively and quantitatively. A key step in proteomic analysis is sample preparation, which is crucial for reliable results. We investigated the effect of the composition of the homogenization buffer used to extract proteins from brain tissue on the yield of protein extraction and the number and type of extracted proteins. Three different types of buffers were compared-detergent-based buffer (DB), chaotropic agent-based buffer (CAB) and buffer without detergent and chaotropic agent (DFB). Based on label-free quantitative protein analysis, detergent buffer was identified as the most suitable for global proteomic profiling of brain tissue. It allows the most efficient extraction of membrane proteins, synaptic and synaptic membrane proteins along with ribosomal, mitochondrial and myelin sheath proteins, which are of particular interest in the field of neurodegenerative disorders research. Homogenization (climate) ; Proteomics ; Mass spectrometry ; Chromatography ; Composition (language) ; Chemistry ; Brain tissue ; Mass spectrometry imaging ; Biology ; Biochemistry ; Anatomy ; Ecology Advanced Proteomics Techniques and Applications ; Bioinformatics and Genomic Networks ; Metabolomics and Mass Spectrometry Studies
Authors: Stefan J. D. Smith | Colin D. Wood | Paul Feron | Hamidreza Mahdavi | Roger J. Mulder | Cara M. Doherty | Matthew R. Hill | Xavier Mulet
Source: Science China Materials (article, 2022-03-11)
Field: Materials Science — Materials Chemistry

Porous solid inspired hyper-crosslinked polymer liquids with highly efficient regeneration for gas purification Materials science ; Polymer ; Adsorption ; Chemical engineering ; Porosity ; Methane ; Porous medium ; Selectivity ; Organic chemistry ; Composite material ; Catalysis ; Chemistry Covalent Organic Framework Applications ; Membrane Separation and Gas Transport ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Andréa Rodrigues Chaves | Rafael Oliveira Martins | Lanaia Maciel | Allyster Silva | Daniel Gondim | Júlia Fortalo | Steffany Santos | Jussara V. Roque | Boniek G. Vaz
Source: Brazilian Journal of Analytical Chemistry (article, 2022-04-07)
Field: Chemistry — Spectroscopy

Ambient Ionization Mass Spectrometry: Applications and New Trends for Environmental Matrices Analysis Since its introduction, ambient mass spectrometry methods have been demonstrated as potential approaches for a rapid and sensitive analysis of many compounds in complex matrices with a minimum or no sample preparation step performed. Some of these methods include low-cost devices and in situ methodologies that are included in the new trend of green analytical chemistry. The application of ambient methods for environmental analysis has been reported in the last decades for qualitative and quantitative analysis. This study aims to contribute with an overview of the 2016 to 2021 period of ambient mass spectrometry methods for applications in environmental analysis. In this context, this review reports especially applications for qualitative and quantitative analysis of contaminants using desorption electrospray ionization (DESI), direct analysis in real-time (DART), paper spray ionization (PSI), and extractive electrospray ionization (EESI) methods. DART ion source ; Desorption electrospray ionization ; Ambient ionization ; Mass spectrometry ; Extractive electrospray ionization ; Context (archaeology) ; Environmental analysis ; Electrospray ionization ; Analytical Chemistry (journal) ; Direct electron ionization liquid chromatography–mass spectrometry interface ; Qualitative analysis ; Chemistry ; Ionization ; Environmental chemistry ; Chemical ionization ; Chromatography ; Sample preparation in mass spectrometry ; Electron ionization ; Ion ; Organic chemistry ; Qualitative research Mass Spectrometry Techniques and Applications ; Advanced Chemical Sensor Technologies ; Analytical Chemistry and Chromatography
Authors: Ken Broeckhoven | Dwight R. Stoll
Source: LCGC North America (article, 2022-01-01)
Field: Chemistry — Spectroscopy

But Why Doesn’t It Get Better? Kinetic Plots for Liquid Chromatography, Part I: Basic Concepts Choosing a liquid chromatography (LC) column for a particular application can be a surprisingly challenging task. On the one hand, column manufacturers give us many options to choose from, including particle types, pore sizes, particle sizes, and different lengths and diameters. On the other hand, we usually do not have time to experimentally evaluate many combinations of these parameters, and sometimes we end up picking something similar to the columns that are already in the drawer. The “kinetic plot” is a powerful graphical tool that can help leverage the best available theory to help us understand how different combinations of parameters (such as particle size and length) will perform in relation to the time needed to get to a particular column efficiency (and thus resolution), and therefore make well-informed decisions when choosing columns. Leverage (statistics) ; Column (typography) ; Chemistry ; Plot (graphics) ; Chromatography ; Kinetic energy ; Task (project management) ; Particle (ecology) ; Particle size ; Resolution (logic) ; Biological system ; Computer science ; Artificial intelligence ; Mathematics ; Statistics ; Engineering ; Geometry ; Physics Analytical Chemistry and Chromatography ; Microfluidic and Capillary Electrophoresis Applications ; Protein purification and stability
Authors: Iulia M. Lazar | Arba Karcini | Joshua Haueis
Source: PLoS ONE (article, 2022-08-01)
Field: Chemistry — Spectroscopy

Mapping the cell-membrane proteome of the SKBR3/HER2+ cell line to the cancer hallmarks The hallmarks of biological processes that underlie the development of cancer have been long recognized, yet, existing therapeutic treatments cannot prevent cancer from continuing to be one of the leading causes of death worldwide. This work was aimed at exploring the extent to which the cell-membrane proteins are implicated in triggering cancer hallmark processes, and assessing the ability to pinpoint tumor-specific therapeutic targets through a combined membrane proteome/cancer hallmark perspective. By using GO annotations, a database of human proteins associated broadly with ten cancer hallmarks was created. Cell-membrane cellular subfractions of SKBR3/HER2+ breast cancer cells, used as a model system, were analyzed by high resolution mass spectrometry, and high-quality proteins (FDR<3%) identified by at least two unique peptides were mapped to the cancer hallmark database. Over 1,400 experimentally detected cell-membrane or cell-membrane associated proteins, representing ~18% of the human cell-membrane proteome, could be matched to the hallmark database. Representative membrane constituents such as receptors, CDs, adhesion and transport proteins were distributed over the entire genome and present in every hallmark category. Sustained proliferative signaling/cell cycle, adhesion/tissue invasion, and evasion of immune destruction emerged as prevalent hallmarks represented by the membrane proteins. Construction of protein-protein interaction networks uncovered a high level of connectivity between the hallmark members, with some receptor (EGFR, ERBB2, FGFR, MTOR, CSF1R), antigen (CD44), and adhesion (MUC1) proteins being implicated in most hallmark categories. An illustrative subset of 138 hallmark proteins that included 42 oncogenes, 24 tumor suppressors, 9 oncogene/tumor suppressor, and 45 approved drug targets was subjected to a more in-depth analysis. The existing drug targets were implicated mainly in signaling processes. Network centrality analysis revealed that nodes with high degree, rather than betweenness, represent a good resource for informing the selection of putative novel drug targets. Through heavy involvement in supporting cancer hallmark processes, we show that the functionally diverse and networked landscape of cancer cell-membrane proteins fosters unique opportunities for guiding the development of novel therapeutic interventions, including multi-agent, immuno-oncology and precision medicine applications. Biology ; Proteome ; Cancer cell ; Membrane protein ; Cancer ; Cell adhesion ; Cell biology ; Proteomics ; Cell ; CD44 ; Bioinformatics ; Biochemistry ; Genetics ; Gene Advanced Proteomics Techniques and Applications ; Monoclonal and Polyclonal Antibodies Research ; Bioinformatics and Genomic Networks