jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Peter Creveling | John Fisher | Noel LeBaron | Michael W. Czabaj
Source: Acta Materialia (article, 2020-10-21)
Field: Materials Science — Ceramics and Composites

4D Imaging of ceramic matrix composites during polymer infiltration and pyrolysis Materials science ; Porosity ; Pyrolysis ; Composite material ; Ceramic ; Ceramic matrix composite ; Shrinkage ; Microstructure ; Polymer ; Infiltration (HVAC) ; Composite number ; Tomography ; Chemical engineering ; Optics Advanced ceramic materials synthesis ; Additive Manufacturing and 3D Printing Technologies ; Composite Material Mechanics
Authors: Ming Hu | Zhonghua Yang
Source: Physical Chemistry Chemical Physics (article, 2020-11-21)
Field: Materials Science — Materials Chemistry

Perspective on multi-scale simulation of thermal transport in solids and interfaces Phonon-mediated thermal transport is inherently multi-scale. The wave-length of phonons (considering phonons as waves) is typically at the nanometer scale; the typical size of a phonon wave energy packet is tens of nanometers, while the phonon mean free path (MFP) can be as long as microns. At different length scales, the phonons will interact with structures of different feature sizes, which can be as small as 0D defects (point defects), short to medium range linear defects (dislocations), medium to large range 2D planar defects (stacking faults and twin boundaries), and large scale 3D defects (voids, inclusions, and various microstructures). The nature of multi-scale thermal transport is that there are different heat transfer physics across different length scales and in the meantime the physics crossing the different scales is interdependent and coupled. Since phonon behavior is usually mode dependent, thermal transport in materials with a combined micro-/nano-structure complexity becomes complicated, making modeling this kind of transport process very challenging. In this perspective, we first summarize the advantages and disadvantages of computational methods for mono-scale heat transfer and the state-of-the-art multi-scale thermal transport modeling. We then discuss a few important aspects of the future development of multi-scale modeling, in particular with the aid of modern machine learning and uncertainty quantification techniques. As more sophisticated theoretical and computational methods continue to advance thermal transport predictions, novel heat transfer physics and thermally functional materials will be discovered for the pertaining energy systems and technologies. Thermal ; Scale (ratio) ; Perspective (graphical) ; Phonon ; Heat transfer ; Statistical physics ; Length scale ; Thermal transfer ; Chemical physics ; Physics ; Materials science ; Nanotechnology ; Thermodynamics ; Computer science ; Mechanics ; Condensed matter physics ; Quantum mechanics Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies
Authors: Yang Liu | Pengfei Yin | Jiareng Chen | Bin Cui | Chao Zhang | Feng Wu
Source: International Journal of Polymer Science (article, 2020-02-06)
Field: Materials Science — Polymers and Plastics

Conducting Polymer-Based Composite Materials for Therapeutic Implantations: From Advanced Drug Delivery System to Minimally Invasive Electronics Conducting polymer-based composites have recently becoming popular in both academic research and industrial practices due to their high conductivity, ease of process, and tunable electrical properties. The multifunctional conducting polymer-based composites demonstrated great application potential for in vivo therapeutics and implantable electronics, including drug delivery, neural interfacing, and minimally invasive electronics. In this review article, the state-of-the-art conducting polymer-based composites in the mentioned biological fields are discussed and summarized. The recent progress on the synthesis, structure, properties, and application of the conducting polymer-based composites is presented, aimed at revealing the structure-property relationship and the corresponding functional applications of the conducting polymer-based composites. Furthermore, key issues and challenges regarding the implantation performance of these composites are highlighted in this paper. Interfacing ; Materials science ; Electronics ; Drug delivery ; Nanotechnology ; Polymer ; Composite material ; Computer science ; Engineering ; Electrical engineering Conducting polymers and applications ; Neuroscience and Neural Engineering ; Advanced Sensor and Energy Harvesting Materials
Authors: Matthew L. Mendonca | Debmalya Ray | Christopher J. Cramer | Randall Q. Snurr
Source: ACS Applied Materials & Interfaces (article, 2020-07-06)
Field: Chemistry — Inorganic Chemistry

Exploring the Effects of Node Topology, Connectivity, and Metal Identity on the Binding of Nerve Agents and Their Hydrolysis Products in Metal–Organic Frameworks Recent studies have shown that metal-organic frameworks (MOFs) built from hexanuclear M(IV) oxide cluster nodes are effective catalysts for nerve agent hydrolysis, where the properties of the active sites on the nodes can strongly influence the reaction energetics. The connectivity and metal identity of these M<sub>6</sub> nodes can be easily tuned, offering extensive opportunities for computational screening to predict promising new materials. Thus, we used density functional theory (DFT) to examine the effects of node topology, connectivity, and metal identity on the binding energies of multiple nerve agents and their corresponding hydrolysis products. By computing an optimization metric based on the relative binding strengths of key hydrolysis reaction species (water, agent, and bidentate-bound products), we predicted optimal M<sub>6</sub> nodes for hydrolyzing specific nerve agent and simulant molecules, where our results are in qualitative agreement with observed experimental trends. This analysis highlighted the notion that no single metal or node topology is optimal for all possible organophosphates, suggesting that MOFs should be selected based on the agent of interest. Using the large amount of data generated from our DFT calculations, we then derived quantitative structure-activity relationship (QSAR) models to help explain the complex trends observed in the binding energies. Through linear regression, we identified the most important descriptors for describing the binding of nerve agents and their hydrolysis products to M<sub>6</sub> nodes. These results suggested that both molecular and node properties, including both structural and chemical features, collectively contribute to the binding energetics. By performing a thorough statistical analysis, we showed that our QSAR models are capable of making quantitatively accurate binding energy predictions for nerve agents and their hydrolysis products in a wide variety of M(IV)-MOFs. The insights gained herein can be used to guide future experiments for the synthesis of MOFs with enhanced catalytic activity for organophosphate hydrolysis. Topology (electrical circuits) ; Molecule ; Node (physics) ; Denticity ; Binding energy ; Hydrolysis ; Density functional theory ; Metal ; Nerve agent ; Metric (unit) ; Materials science ; Computational chemistry ; Computer science ; Chemistry ; Mathematics ; Physics ; Enzyme ; Organic chemistry ; Quantum mechanics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Crystallography and molecular interactions
Authors: Zizhen Zhou | Guohua Cao | Jianghui Liu | Huijun Liu
Source: npj Computational Materials (article, 2020-10-08)
Field: Materials Science — Materials Chemistry

High-throughput prediction of the carrier relaxation time via data-driven descriptor Abstract It has been demonstrated that many promising thermoelectric materials, such as tetradymite compounds are also three-dimensional topological insulators. In both cases, a fundamental question is the evaluation of carrier relaxation time, which is usually a rough task due to the complicated scattering mechanisms. Previous works using the simple deformation potential theory or considering complete electron-phonon coupling are, however, restricted to small systems. By adopting a data-driven method named SISSO (Sure Independence Screening and Sparsifying Operator) with the training data obtained via deformation potential theory, we propose an efficient and physically interpretable descriptor to evaluate the relaxation time, using tetradymites as prototypical examples. Without any input from first-principles calculations, the descriptor contains only several elemental properties of the constituent atoms, and could be utilized to quickly and reliably predict the carrier relaxation time of a substantial number of tetradymites with arbitrary stoichiometry. Relaxation (psychology) ; Independence (probability theory) ; Computer science ; Coupling (piping) ; Task (project management) ; Operator (biology) ; Throughput ; Deformation (meteorology) ; Scattering ; Simple (philosophy) ; Topology (electrical circuits) ; Statistical physics ; Materials science ; Physics ; Mathematics ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Thermal properties of materials
Authors: Jonas Hedman | David Nilebo | Elin Langhammer | Fredrik Björefors
Source: ChemSusChem (article, 2020-08-21)
Field: Materials Science — Polymers and Plastics

Fibre Optic Sensor for Characterisation of Lithium‐Ion Batteries The interaction between a fibre optic evanescent wave sensor and the positive electrode material, lithium iron phosphate, in a battery cell is presented. The optical-electrochemical combination was investigated in a reflection-based and a transmission-based configuration, both leading to comparable results. Both constant current cycling and cyclic voltammetry were employed to link the optical response to the charge and discharge of the battery cells, and the results demonstrated that the optical signal changed consistently with lithium ion insertion and extraction. More precisely, cyclic voltammetry showed that the intensity increased when iron was oxidised during charge and then decreased as iron was reduced during discharge. Cyclic voltammetry also revealed that the optical signal remained unchanged when essentially no oxidation or reduction of the electrode material took place. This shows that optical fibre sensors may be used as a way of monitoring state of charge and electrode properties under dynamic conditions. Cyclic voltammetry ; Electrode ; Lithium (medication) ; Materials science ; Lithium iron phosphate ; Electrochemistry ; Ion ; Lithium niobate ; SIGNAL (programming language) ; Analytical Chemistry (journal) ; Battery (electricity) ; Optical fiber ; Optoelectronics ; Chemistry ; Optics Conducting polymers and applications ; Advanced Battery Technologies Research ; Analytical Chemistry and Sensors
Authors: Weijie Lan | Sylvie Bureau | Songchao Chen | Alexandre Leca | Catherine M.G.C. Renard | Benoît Jaillais
Source: Food Control (article, 2020-08-15)
Field: Chemistry — Analytical Chemistry

Visible, near- and mid-infrared spectroscopy coupled with an innovative chemometric strategy to control apple puree quality Cultivar ; Food science ; Partial least squares regression ; Malic acid ; Chemometrics ; Mathematics ; Chemistry ; Horticulture ; Chromatography ; Citric acid ; Biology ; Statistics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Fermentation and Sensory Analysis
Authors: Mohammad Zarezadeh | Mohammad Aboonajmi | Mahdi Ghasemi‐Varnamkhasti
Source: Food Science & Nutrition (article, 2020-11-04)
Field: Chemistry — Analytical Chemistry

Fraud detection and quality assessment of olive oil using ultrasound Today, food safety is recognized as one of the most important human priorities, so effective and new policies have been implemented to improve and develop the position of effective laws in the food industry. Extra virgin olive oil (EVOO) has many amazing benefits for human body's health. Due to the nutritional value and high price of EVOO, there is a lot of cheating in it. The ultrasound approach has many advantages in the food studies, and it is fast and nondestructive for quality evaluation. In this study, to fraud detection of EVOO four ultrasonic properties of oil in five levels of adulteration (5%, 10%, 20%, 35%, and 50%) were extracted. The 2 MHz ultrasonic probes were used in the DOI 1,000 STARMANS diagnostic ultrasonic device in a "probe holding mechanism." The four extracted ultrasonic features include the following: "percentage of amplitude reduction, time of flight (TOF), the difference between the first and second maximum amplitudes of the domain (in the time-amplitude diagram), and the ratio of the first and second maximum of amplitude." Seven classification algorithms including "Naïve Bayes, support vector machine, gradient boosting classifier, K-nearest neighbors, artificial neural network, logistic regression, and AdaBoost" were used to classify the preprocessed data. Results showed that the Naïve Bayes algorithm with 90.2% provided the highest accuracy among the others, and the support vector machine and gradient boosting classifier with 88.2% were in the next ranks. Olive oil ; Quality (philosophy) ; Ultrasound ; Quality assessment ; Environmental science ; Computer science ; Medicine ; Engineering ; Reliability engineering ; Radiology ; Food science ; Chemistry ; Evaluation methods ; Physics Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Food Supply Chain Traceability
Authors: Jing Huang | Guangxin Ren | Yemei Sun | Shanshan Jin | Luqing Li | Yujie Wang | Jingming Ning | Zhengzhu Zhang
Source: Food Science & Nutrition (article, 2020-02-28)
Field: Chemistry — Analytical Chemistry

Qualitative discrimination of Chinese dianhong black tea grades based on a handheld spectroscopy system coupled with chemometrics The evaluation of Chinese dianhong black tea (CDBT) grades was an important indicator to ensure its quality. A handheld spectroscopy system combined with chemometrics was utilized to assess CDBT from eight grades. Both variables selection methods, namely genetic algorithm (GA) and successive projections algorithm (SPA), were employed to acquire the feature variables of each sample spectrum. A partial least-squares discriminant analysis (PLS-DA) and support vector machine (SVM) algorithms were applied for the establishment of the grading discrimination models based on near-infrared spectroscopy (NIRS). Comparisons of the portable and benchtop NIRS systems were implemented to obtain the optimal discriminant models. Experimental results showed that GA-SVM models by the handheld sensors yielded the best predictive performance with the correct discriminant rate (CDR) of 98.75% and 100% in the training set and prediction set, respectively. This study demonstrated that the handheld system combined with a suitable chemometric and feature information selection method could successfully be used for the rapid and efficient discrimination of CDBT rankings. It was promising to establish a specific economical portable NIRS sensor for in situ quality assurance of CDBT grades. Chemometrics ; Linear discriminant analysis ; Feature selection ; Mobile device ; Support vector machine ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Discriminant ; Quality assurance ; Partial least squares regression ; Mathematics ; Machine learning ; Engineering Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Torsha Moitra | Pijush Karak | Sayantani Chakraborty | Kenneth Ruud | Swapan Chakrabarti
Source: Physical Chemistry Chemical Physics (article, 2020-11-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Behind the scenes of spin-forbidden decay pathways in transition metal complexes The interpretation of the ultrafast photophysics of transition metal complexes following photo-absorption is quite involved as the heavy metal center leads to a complicated and entangled singlet-triplet manifold. This opens up multiple pathways for deactivation, often with competitive rates. As a result, intersystem crossing (ISC) and phosphorescence are commonly observed in transition metal complexes. A detailed understanding of such an excited-state structure and dynamics calls for state-of-the-art experimental and theoretical methodologies. In this review, we delve into the inability of non-relativistic quantum theory to describe spin-forbidden transitions, which can be overcome by taking into account spin-orbit coupling, whose importance grows with increasing atomic number. We present the quantum chemical theory of phosphorescence and ISC together with illustrative examples. Finally, a few applications are highlighted, bridging the gap between theoretical studies and experimental applications, such as photofunctional materials. Spin (aerodynamics) ; Transition metal ; Chemical physics ; Transition (genetics) ; Atomic physics ; Metal ; Chemistry ; Physics ; Materials science ; Crystallography ; Condensed matter physics ; Thermodynamics ; Catalysis ; Organic chemistry Magnetism in coordination complexes ; Lanthanide and Transition Metal Complexes ; Organometallic Complex Synthesis and Catalysis
Authors: Michael Woolman | Jimmy Qiu | Claudia M. Kuzan-Fischer | Isabelle Ferry | Delaram Dara | Lauren Katz | Fowad Daud | Megan Wu | Manuela Ventura | Nicholas Bernards | Harley Chan | Inga B. Fricke | Mark Zaidi | Bradly G. Wouters | James T. Rutka | Sunit Das | Jonathan Irish | Robert Weersink | Howard J. Ginsberg | David A. Jaffray | Arash Zarrine‐Afsar
Source: Chemical Science (article, 2020-01-01)
Field: Chemistry — Spectroscopy

<i>In situ</i> tissue pathology from spatially encoded mass spectrometry classifiers visualized in real time through augmented reality Integration between a hand-held mass spectrometry desorption probe based on picosecond infrared laser technology (PIRL-MS) and an optical surgical tracking system demonstrates <i>in situ</i> tissue pathology from point-sampled mass spectrometry data. Spatially encoded pathology classifications are displayed at the site of laser sampling as color-coded pixels in an augmented reality video feed of the surgical field of view. This is enabled by two-way communication between surgical navigation and mass spectrometry data analysis platforms through a custom-built interface. Performance of the system was evaluated using murine models of human cancers sampled <i>in situ</i> in the presence of body fluids with a technical pixel error of 1.0 ± 0.2 mm, suggesting a 84% or 92% (excluding one outlier) cancer type classification rate across different molecular models that distinguish cell-lines of each class of breast, brain, head and neck murine models. Further, through end-point immunohistochemical staining for DNA damage, cell death and neuronal viability, spatially encoded PIRL-MS sampling is shown to produce classifiable mass spectral data from living murine brain tissue, with levels of neuronal damage that are comparable to those induced by a surgical scalpel. This highlights the potential of spatially encoded PIRL-MS analysis for <i>in vivo</i> use during neurosurgical applications of cancer type determination or point-sampling <i>in vivo</i> tissue during tumor bed examination to assess cancer removal. The interface developed herein for the analysis and the display of spatially encoded PIRL-MS data can be adapted to other hand-held mass spectrometry analysis probes currently available. Mass spectrometry ; In situ ; Mass spectrometry imaging ; Tracking (education) ; Pathology ; Analytical Chemistry (journal) ; Computer science ; Chemistry ; Medicine ; Chromatography ; Psychology Mass Spectrometry Techniques and Applications ; Species Distribution and Climate Change ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Ksh. Robert Singh | Saurabh Chaudhury
Source: Complex & Intelligent Systems (article, 2020-02-12)
Field: Chemistry — Analytical Chemistry

A cascade network for the classification of rice grain based on single rice kernel Abstract This paper describes the classification of four different varieties of rice grain based on four sets of features, namely morphology, colour, texture and wavelet. The classification is carried out on single rice kernel using image pre-processing steps followed by a cascade network classifier. The performance of the classifiers based on the above feature sets is also compared. It is found that morphological feature is more suitable for the classification of rice kernels, as compared to other features. The number of input features is reduced by a feature selection process using statistical analysis system (SAS) software. The classification accuracy based on selected features is compared with that of original features using different classifiers. It is found that the selected features are able to provide classification accuracy very close to the original features. The performance of the proposed cascade classifier is also tested against standard datasets from the University of California, Irvine (UCI), and the results are compared with other classifiers. The results show that the proposed classifier provides better classification accuracy as compared to other classifiers. Pattern recognition (psychology) ; Artificial intelligence ; Classifier (UML) ; Feature selection ; Computer science ; Kernel (algebra) ; Cascade ; Cascading classifiers ; Contextual image classification ; Mathematics ; Random subspace method ; Image (mathematics) ; Engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Andreas Blutke | Na Sun | Zhihao Xu | Achim Buck | Luke Harrison | Sonja C. Schriever | Paul T. Pfluger | David Wiles | Thomas Kunzke | Katharina J. Huber | Jürgen Schlegel | Michaela Aichler | Annette Feuchtinger | Kaspar Matiasek | Stefanie M. Hauck | Axel Walch
Source: Scientific Reports (article, 2020-09-02)
Field: Chemistry — Spectroscopy

Light sheet fluorescence microscopy guided MALDI-imaging mass spectrometry of cleared tissue samples Light sheet fluorescence microscopy (LSFM) of optically cleared biological samples represents a powerful tool to analyze the 3-dimensional morphology of tissues and organs. Multimodal combinations of LSFM with additional analyses of the identical sample help to limit the consumption of restricted specimen and reduce inter-sample variation. Here, we demonstrate the proof-of-concept that LSFM of cleared brain tissue samples can be combined with Matrix Assisted Laser Desorption/Ionization-Mass Spectrometry Imaging (MALDI-MSI) for detection and quantification of proteins. Samples of freshly dissected murine brain and of archived formalin-fixed paraffin-embedded (FFPE) human brain tissue were cleared (3DISCO). Tissue regions of interest were defined by LSFM and excised, (re)-embedded in paraffin, and sectioned. Mouse sections were coated with sinapinic acid matrix. Human brain sections were pre-digested with trypsin and coated with α-cyano-4-hydroxycinnamic acid matrix. Subsequently, sections were subjected to MALDI-time-of-flight (TOF)-MSI in mass ranges between 0.8 to 4 kDa (human tissue sections), or 2.5-25 kDa (mouse tissue sections) with a lateral resolution of 50 µm. Protein- and peptide-identities corresponding to acquired MALDI-MSI spectra were confirmed by parallel liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis. The spatial abundance- and intensity-patterns of established marker proteins detected by MALDI-MSI were also confirmed by immunohistochemistry. Clearance ; MALDI imaging ; Mass spectrometry imaging ; Chemistry ; Mass spectrometry ; Microscopy ; Matrix-assisted laser desorption/ionization ; Matrix (chemical analysis) ; Fluorescence ; Chromatography ; Pathology ; Desorption ; Optics Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications