jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ming‐Ching Chang | Chi-Huan Tung | Shou-Yi Chang | Jan‐Michael Y. Carrillo | Yangyang Wang | Bobby G. Sumpter | Guan-Rong Huang | Changwoo Do | Wei‐Ren Chen
Source: Communications Physics (article, 2022-02-28)
Field: Materials Science — Materials Chemistry

A machine learning inversion scheme for determining interaction from scattering Abstract Small angle scattering techniques have now been routinely used to quantitatively determine the potential of mean force in colloidal suspensions. However the numerical accuracy of data interpretation is often compounded by the approximations adopted by liquid state analytical theories. To circumvent this long standing issue, here we outline a machine learning strategy for determining the effective interaction in the condensed phases of matter using scattering. Via a case study of colloidal suspensions, we show that the effective potential can be probabilistically inferred from the scattering spectra without any restriction imposed by model assumptions. Comparisons to existing parametric approaches demonstrate the superior performance of this method in accuracy, efficiency, and applicability. This method can effectively enable quantification of interaction in highly correlated systems using scattering and diffraction experiments. Scattering ; Inversion (geology) ; Parametric statistics ; Diffraction ; Statistical physics ; Computer science ; Algorithm ; Physics ; Optics ; Mathematics ; Statistics ; Geology Material Dynamics and Properties ; Electrostatics and Colloid Interactions ; Spectroscopy and Quantum Chemical Studies
Authors: Nesrine Amor | Muhammad Tayyab Noman | Michal Petrů | Neethu Sebastian
Source: Scientific Reports (article, 2022-04-15)
Field: Materials Science — Polymers and Plastics

Comfort evaluation of ZnO coated fabrics by artificial neural network assisted with golden eagle optimizer model This paper introduces a novel technique to evaluate comfort properties of zinc oxide nanoparticles (ZnO NPs) coated woven fabrics. The proposed technique combines artificial neural network (ANN) and golden eagle optimizer (GEO) to ameliorate the training process of ANN. Neural networks are state-of-the-art machine learning models used for optimal state prediction of complex problems. Recent studies showed that the use of metaheuristic algorithms improve the prediction accuracy of ANN. GEO is the most advanced methaheurstic algorithm inspired by golden eagles and their intelligence for hunting by tuning their speed according to spiral trajectory. From application point of view, this study is a very first attempt where GEO is applied along with ANN to improve the training process of ANN for any textiles and composites application. Furthermore, the proposed algorithm ANN with GEO (ANN-GEO) was applied to map out the complex input-output conditions for optimal results. Coated amount of ZnO NPs, fabric mass and fabric thickness were selected as input variables and comfort properties were evaluated as output results. The obtained results reveal that ANN-GEO model provides high performance accuracy than standard ANN model, ANN models trained with latest metaheuristic algorithms including particle swarm optimizer and crow search optimizer, and conventional multiple linear regression. Artificial neural network ; Particle swarm optimization ; Computer science ; Artificial intelligence ; Metaheuristic ; Process (computing) ; Machine learning ; Algorithm Textile materials and evaluations ; Color Science and Applications ; Color perception and design
Authors: Nicolás M. Morato | Hannah Marie Brown | Diogo Moniz‐Garcia | Erik H. Middlebrooks | Mark E. Jentoft | Kaisorn L. Chaichana | Alfredo Quiñones‐Hinojosa | R. Graham Cooks
Source: Scientific Reports (article, 2022-11-07)
Field: Chemistry — Spectroscopy

High-throughput analysis of tissue microarrays using automated desorption electrospray ionization mass spectrometry Tissue microarrays (TMAs) are commonly used for the rapid analysis of large numbers of tissue samples, often in morphological assessments but increasingly in spectroscopic analysis, where specific molecular markers are targeted via immunostaining. Here we report the use of an automated high-throughput system based on desorption electrospray ionization (DESI) mass spectrometry (MS) for the rapid generation and online analysis of high-density (6144 samples/array) TMAs, at rates better than 1 sample/second. Direct open-air analysis of tissue samples (hundreds of nanograms) not subjected to prior preparation, plus the ability to provide molecular characterization by tandem mass spectrometry (MS/MS), make this experiment versatile and applicable to both targeted and untargeted analysis in a label-free manner. These capabilities are demonstrated in a proof-of-concept study of frozen brain tissue biopsies where we showcase (i) a targeted MS/MS application aimed at identification of isocitrate dehydrogenase mutation in glioma samples and (ii) an untargeted MS tissue type classification using lipid profiles and correlation with tumor cell percentage estimates from histopathology. The small sample sizes and large sample numbers accessible with this methodology make for a powerful analytical system that facilitates the identification of molecular markers for later use in intraoperative applications to guide precision surgeries and ultimately improve patient outcomes. Desorption electrospray ionization ; Mass spectrometry ; Tissue microarray ; Computational biology ; Isocitrate dehydrogenase ; Mass spectrometry imaging ; Sample preparation ; Tandem mass spectrometry ; Electrospray ionization ; MALDI imaging ; Computer science ; Chemistry ; Chromatography ; Pathology ; Matrix-assisted laser desorption/ionization ; Biology ; Desorption ; Immunohistochemistry ; Ionization ; Medicine ; Chemical ionization ; Biochemistry Mass Spectrometry Techniques and Applications ; Ion-surface interactions and analysis ; Metabolomics and Mass Spectrometry Studies
Authors: Vishal Chaudhary
Source: Frontiers in Nanotechnology (article, 2022-11-01)
Field: Materials Science — Materials Chemistry

Prospects of green nanotechnology for efficient management of neurodegenerative diseases Current theranostics for neurodegenerative diseases (NDD) management are majorly symptomatic due to a lack of identification of early-stage biomarkers and the inefficiency of drugs to penetrate through the blood-brain barrier. Recently, the Neuro-nanotechnology interface has emerged as a potential strategy for diagnosis, monitoring, and treatment of NDDs owing to smaller particle size, high specific surface area, tunable physicochemical attributes and rich surface functionalities. However, toxicity and biocompatibility are two significant challenges restricting their commercial prospect in NDD management. On the contrary, green nanosystems fabricated using plant extracts, microorganisms, biome-based precursors, repurposed-byproducts, exosomes, and protein-based bio-nanomaterials are economical, eco-friendly, biocompatible and renewable due to the abundance of biodiversity. This prospect explores the novel and cutting-edge interface of green nanosystems and NDDs for developing diagnostic and implantable devices, targeted drug delivery strategies, surgical prostheses, therapeutics, treatment, nanoscaffolds for neurogeneration, and immunity development. Besides, it discusses the challenges, alternate solutions and advanced prospects of green nanosystems with the integration of modern-age technologies for the development of sustainable green Neuro-nanotechnology for efficient management of NDDs. Nanotechnology ; Biocompatible material ; Drug delivery ; Materials science ; Medicine ; Biomedical engineering Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Molecular Communication and Nanonetworks
Authors: Suzan Özdemir | İpek Yalçın-Eniş | Baturalp Yalçınkaya | Fatma Yalçinkaya
Source: Membranes (review, 2022-09-25)
Field: Materials Science — Biomaterials

An Investigation of the Constructional Design Components Affecting the Mechanical Response and Cellular Activity of Electrospun Vascular Grafts Cardiovascular disease is anticipated to remain the leading cause of death globally. Due to the current problems connected with using autologous arteries for bypass surgery, researchers are developing tissue-engineered vascular grafts (TEVGs). The major goal of vascular tissue engineering is to construct prostheses that closely resemble native blood vessels in terms of morphological, mechanical, and biological features so that these scaffolds can satisfy the functional requirements of the native tissue. In this setting, morphology and cellular investigation are usually prioritized, while mechanical qualities are generally addressed superficially. However, producing grafts with good mechanical properties similar to native vessels is crucial for enhancing the clinical performance of vascular grafts, exposing physiological forces, and preventing graft failure caused by intimal hyperplasia, thrombosis, aneurysm, blood leakage, and occlusion. The scaffold's design and composition play a significant role in determining its mechanical characteristics, including suturability, compliance, tensile strength, burst pressure, and blood permeability. Electrospun prostheses offer various models that can be customized to resemble the extracellular matrix. This review aims to provide a comprehensive and comparative review of recent studies on the mechanical properties of fibrous vascular grafts, emphasizing the influence of structural parameters on mechanical behavior. Additionally, this review provides an overview of permeability and cell growth in electrospun membranes for vascular grafts. This work intends to shed light on the design parameters required to maintain the mechanical stability of vascular grafts placed in the body to produce a temporary backbone and to be biodegraded when necessary, allowing an autologous vessel to take its place. Tissue engineering ; Scaffold ; Intimal hyperplasia ; Extracellular matrix ; Biomedical engineering ; Blood vessel ; Mechanical strength ; Decellularization ; Vascular occlusion ; Thrombosis ; Medicine ; Materials science ; Surgery ; Chemistry ; Smooth muscle Electrospun Nanofibers in Biomedical Applications ; Tissue Engineering and Regenerative Medicine ; Infectious Aortic and Vascular Conditions
Authors: Charles M. Farber | Dmitry Kurouski
Source: Frontiers in Plant Science (article, 2022-04-26)
Field: Chemistry — Analytical Chemistry

Raman Spectroscopy and Machine Learning for Agricultural Applications: Chemometric Assessment of Spectroscopic Signatures of Plants as the Essential Step Toward Digital Farming A growing body of evidence suggests that Raman spectroscopy (RS) can be used for diagnostics of plant biotic and abiotic stresses. RS can be also utilized for identification of plant species and their varieties, as well as assessment of the nutritional content and commercial values of seeds. The power of RS in such cases to a large extent depends on chemometric analyses of spectra. In this work, we critically discuss three major approaches that can be used for advanced analyses of spectroscopic data: summary statistics, statistical testing and chemometric classification. On the example of Raman spectra collected from roses, we demonstrate the outcomes and the potential of all three types of spectral analyses. We anticipate that our findings will help to design the most optimal spectral processing and preprocessing that is required to achieved the desired results. We also expect that reported collection of results will be useful to all researchers who work on spectroscopic analyses of plant specimens. Preprocessor ; Raman spectroscopy ; Identification (biology) ; Computer science ; Biological system ; Artificial intelligence ; Chemometrics ; Data pre-processing ; Machine learning ; Pattern recognition (psychology) ; Agricultural engineering ; Environmental science ; Remote sensing ; Botany ; Biology ; Engineering ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Bee Products Chemical Analysis
Authors: Yang Shen | Yuchao Hua | Han-Ling Li | С. Л. Соболев | Bing Cao
Source: IEEE Transactions on Electron Devices (article, 2022-04-29)
Field: Materials Science — Materials Chemistry

Spectral Thermal Spreading Resistance of Wide-Bandgap Semiconductors in Ballistic-Diffusive Regime To develop efficient thermal management strategies for wide-bandgap (WBG) semiconductor devices, it is essential to have a clear understanding of the heat transport process within the device and accurately predict the junction temperature. In this article, we use the phonon Monte Carlo (MC) method with the phonon dispersion of several typical WBG semiconductors, including GaN, SiC, AlN, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> -Ga <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> , to investigate the thermal spreading resistance in a ballistic-diffusive regime. This work shows that when compared with Fourier’s law-based predictions, the increase in the thermal resistance caused by the ballistic effects is strongly related to the phonon dispersion. Based on the model derived under the gray-medium approximation and the results of dispersion MC, we obtained a thermal resistance model that can well address the issues of thermal spreading, ballistic effects, and the influence of phonon dispersion. The model can be easily coupled with finite-element method (FEM)-based thermal analysis and applied to different materials. This article can provide a clearer understanding of the influence of phonon dispersion on the thermal transport process, and it can be useful for the prediction of junction temperatures and the development of thermal management strategies for WBG semiconductor devices. Materials science ; Semiconductor ; Band gap ; Optoelectronics ; Thermal resistance ; Thermal ; Wide-bandgap semiconductor ; Engineering physics ; Ballistic conduction ; Condensed matter physics ; Physics ; Electron Thermal properties of materials ; Ga2O3 and related materials ; GaN-based semiconductor devices and materials
Authors: Alexandra K. Richardson | Rachel C. Irlam | Helena Rapp-Wright | Graham A. Mills | Gary R. Fones | Stephen R. Stürzenbaum | David J. Neep | Leon Barron
Source: The Science of The Total Environment (article, 2022-05-26)
Field: Chemistry — Analytical Chemistry

A miniaturized passive sampling-based workflow for monitoring chemicals of emerging concern in water The miniaturization of a full workflow for identification and monitoring of contaminants of emerging concern (CECs) is presented. Firstly, successful development of a low-cost small 3D-printed passive sampler device (3D-PSD), based on a two-piece methacrylate housing that held up to five separate 9 mm disk sorbents, is discussed. Secondly, a highly sensitive liquid chromatography-tandem mass spectrometry (LC-MS/MS) method reduced the need for large scale in-laboratory apparatus, solvent, reagents and reference material quantities for in-laboratory passive sampler device (PSD) calibration and extraction. Using hydrophilic-lipophilic balanced sorbents, sampling rates (R<sub>s</sub>) were determined after a low 50 ng L<sup>-1</sup> exposure over seven days for 39 pesticides, pharmaceuticals, drug metabolites and illicit drugs over the range 0.3 to 12.3 mL day<sup>-1</sup>. The high sensitivity LC-MS/MS method enabled rapid analysis of river water using only 10 μL of directly injected sample filtrate to measure occurrence of 164 CECs and sources along 19 sites on the River Wandle, (London, UK). The new 3D-PSD was then field-tested over seven days at the site with the highest number and concentration of CECs, which was down-river from a wastewater treatment plant. Almost double the number of CECs were identified in 3D-PSD extracts across sites in comparison to water samples (80 versus 42 CECs, respectively). Time-weighted average CEC concentrations ranged from 8.2 to 845 ng L<sup>-1</sup>, which were generally comparable to measured concentrations in grab samples. Lastly, high resolution mass spectrometry-based suspect screening of 3D-PSD extracts enabled 113 additional compounds to be tentatively identified via library matching, many of which are currently or are under consideration for the EU Watch List. This miniaturized workflow represents a new, cost-effective, and more practically efficient means to perform passive sampling chemical monitoring at a large scale. SYNOPSIS: Miniaturized, low cost, multi-disk passive samplers enabled more efficient multi-residue chemical contaminant characterization, potentially for large-scale monitoring programs. Chromatography ; Contamination ; Chemistry ; Mass spectrometry ; Environmental science ; Miniaturization ; Environmental chemistry ; Wastewater ; Liquid chromatography–mass spectrometry ; Detection limit ; Passive sampling ; Tandem mass spectrometry ; Sampling (signal processing) ; Calibration ; Environmental engineering ; Materials science ; Computer science Analytical chemistry methods development ; Pharmaceutical and Antibiotic Environmental Impacts ; Advanced Chemical Sensor Technologies
Authors: Johannes Nagel‐Held | Leonie Kaiser | C. Friedrich H. Longin | Bernd Hitzmann
Source: Cereal Chemistry (article, 2022-03-09)
Field: Chemistry — Analytical Chemistry

Prediction of wheat quality parameters combining Raman, fluorescence, and near‐infrared spectroscopy (NIRS) Abstract Background and Objectives A high amount of the world wheat production is used for bread making. Due to its simplicity, wheat samples are traded based on protein content, although this is only an indirect measurement of baking quality. As direct bread quality traits like loaf volume, water absorption, and dough properties are cumbersome to measure along wheat supply chains and in wheat breeding programs, this study aims to predict directly dough properties and baking behavior by combining complementary information from near‐infrared, Raman, and fluorescence spectroscopy. Several proven data preprocessing and regression algorithms were evaluated, including partial least‐squares regression (PLSR) and genetic algorithm (GA). Findings A highly diverse sample set of 415 wheat samples from a wheat hybrid breeding program was analyzed as whole grain, whole grain flour, and extracted flour. The best predictive models yielded a 10‐fold cross‐validated coefficient of determination R 2 of .973, .873, .774, .835, .369, .447, .311, .512, .536, .723, and .715 for protein content, wet gluten, loaf volume, water uptake, elongation resistance to extension, extensibility, ratio number, extensograph energy, maltose number, plant height, and grain yield, respectively. Conclusion The prediction accuracies are well suited for screening purposes in wheat breeding and allow better quality assessment compared to protein content along wheat supply chains. Significance and Novelty In this study, complementary spectroscopic techniques are combined for the first time to achieve higher prediction accuracy for a wide range of wheat quality parameters. In addition, Raman and fluorescence spectroscopy were used to predict quality data from a large sample set. Partial least squares regression ; Chemistry ; Wheat flour ; Chemometrics ; Biological system ; Analytical Chemistry (journal) ; Food science ; Mathematics ; Statistics ; Chromatography Spectroscopy and Chemometric Analyses ; Food composition and properties ; Food Science and Nutritional Studies
Authors: Binbin Fan | Rongguang Zhu | Dongyu He | Shichang Wang | Xiaomin Cui | Xuedong Yao
Source: Foods (article, 2022-07-30)
Field: Chemistry — Analytical Chemistry

Evaluation of Mutton Adulteration under the Effect of Mutton Flavour Essence Using Hyperspectral Imaging Combined with Machine Learning and Sparrow Search Algorithm The evaluation of mutton adulteration faces new challenges because of mutton flavour essence, which achieves a similar flavour between the adulterant and mutton. Hence, methods for classifying and quantifying the adulterated mutton under the effect of mutton flavour essence, based on near-infrared hyperspectral imaging (NIR-HSI, 1000-2500 nm) combined with machine learning (ML) and sparrow search algorithm (SSA), were proposed in this study. After spectral preprocessing via first derivative combined with multiple scattering correction (1D + MSC), classification and quantification models were established using back propagation neural network (BP), extreme learning machine (ELM) and support vector machine/regression (SVM/SVR). SSA was further used to explore the global optimal parameters of these models. Results showed that the performance of models improves after optimisation via the SSA. SSA-SVM achieved the optimal discrimination result, with an accuracy of 99.79% in the prediction set; SSA-SVR achieved the optimal prediction result, with an R<sub>P</sub><sup>2</sup> of 0.9304 and an RMSEP of 0.0458 g·g<sup>-1</sup>. Hence, NIR-HSI combined with ML and SSA is feasible for classification and quantification of mutton adulteration under the effect of mutton flavour essence. This study can provide a theoretical and practical reference for the evaluation and supervision of food quality under complex conditions. Flavour ; Support vector machine ; Artificial intelligence ; Hyperspectral imaging ; Pattern recognition (psychology) ; Preprocessor ; Machine learning ; Mathematics ; Computer science ; Food science ; Chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Huan Yuan | Jiagui Wu | Jinping Zhang | Xun Pu | Zhenfu Zhang | Yang Yu | Junbo Yang
Source: Nanomaterials (article, 2022-02-17)
Field: Materials Science — Materials Chemistry

Non-Volatile Programmable Ultra-Small Photonic Arbitrary Power Splitters A series of reconfigurable compact photonic arbitrary power splitters are proposed based on the hybrid structure of silicon and Ge<sub>2</sub>Sb<sub>2</sub>Se<sub>4</sub>Te<sub>1</sub> (GSST), which is a new kind of non-volatile optical phase change material (O-PCM) with low absorption. Our pixelated meta-hybrid has an extremely small photonic integrated circuit (PIC) footprint with a size comparable to that of the most advanced electronic integrated circuits (EICs). The power-split ratio can be reconfigured in a completely digital manner through the amorphous and crystalline switching of the GSST material, which only coated less than one-fifth of the pattern allocation area. The target power-split ratio between the output channels can be arbitrarily reconfigured digitally with high precision and in the valuable C-band (1530-1560 nm) based on the analysis of three-dimensional finite-difference time-domain. The 1 × 2, 1 × 3, and 1 × 4 splitting configurations were all investigated with a variety of power-split ratios for each case, and the corresponding true value tables of GSST distribution are given. These non-volatile hybrid photonic splitters offer the advantages of an extremely small footprint and non-volatile digital programmability, which are favorable to the truly optoelectronic fusion chip. Photonics ; Footprint ; Photonic integrated circuit ; Materials science ; Power (physics) ; Computer science ; Optoelectronics ; Photonic crystal ; Chip ; Electronic engineering ; Optics ; Telecommunications ; Physics ; Engineering Phase-change materials and chalcogenides ; Photonic and Optical Devices ; Photonic Crystals and Applications