jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ayhan Etyemez
Source: Open Chemistry (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Structural, physical, and mechanical properties of the TiO<sub>2</sub> added hydroxyapatite composites Abstract Composites are important as they have been used in a variety of different fields. Therefore, production and testing of composites have become one of the most popular topics for researchers. In this work, the physical, structural, and mechanical properties such as packing density ( V t ), bond dissociation energy ( G t ), Young’s ( E ), bulk ( B ), shear ( S ), longitudinal ( L ), indentation ( E *) moduli, and Poisson’s ratio ( σ ) were obtained for (100 − x )HAP + x TiO 2 composite, where x = 0, 3, 7.5, and 10 wt%. The variations in those properties with TiO 2 rate in HAP composites were tested. Composite material ; Materials science ; Indentation ; Composite number ; Bond-dissociation energy ; Shear (geology) ; Shear modulus ; Dissociation (chemistry) ; Chemistry Radiation Shielding Materials Analysis ; Bone Tissue Engineering Materials ; Thermal and Kinetic Analysis
Authors: Joanna Bons | Deng Pan | Samah Shah | Rosemary Bai | Chira Chen‐Tanyolac | Xianhong Wang | Daffolyn Rachael Fels Elliott | Anatoly Urisman | Amy O’Broin | Nathan Basisty | Jacob Rose | Veena Sangwan | Sophie Camilleri‐Broët | James Tankel | Philippe Gascard | Lorenzo Ferri | Thea D. Tlsty | Birgit Schilling
Source: PROTEOMICS (article, 2022-10-13)
Field: Chemistry — Spectroscopy

Data‐independent acquisition and quantification of extracellular matrix from human lung in chronic inflammation‐associated carcinomas Early events associated with chronic inflammation and cancer involve significant remodeling of the extracellular matrix (ECM), which greatly affects its composition and functional properties. Using lung squamous cell carcinoma (LSCC), a chronic inflammation-associated cancer (CIAC), we optimized a robust proteomic pipeline to discover potential biomarker signatures and protein changes specifically in the stroma. We combined ECM enrichment from fresh human tissues, data-independent acquisition (DIA) strategies, and stringent statistical processing to analyze "Tumor" and matched adjacent histologically normal ("Matched Normal") tissues from patients with LSCC. Overall, 1802 protein groups were quantified with at least two unique peptides, and 56% of those proteins were annotated as "extracellular." Confirming dramatic ECM remodeling during CIAC progression, 529 proteins were significantly altered in the "Tumor" compared to "Matched Normal" tissues. The signature was typified by a coordinated loss of basement membrane proteins and small leucine-rich proteins. The dramatic increase in the stromal levels of SERPINH1/heat shock protein 47, that was discovered using our ECM proteomic pipeline, was validated by immunohistochemistry (IHC) of "Tumor" and "Matched Normal" tissues, obtained from an independent cohort of LSCC patients. This integrated workflow provided novel insights into ECM remodeling during CIAC progression, and identified potential biomarker signatures and future therapeutic targets. Extracellular matrix ; Biomarker ; Stromal cell ; Inflammation ; Proteomics ; Biology ; Pathology ; Immunohistochemistry ; Basement membrane ; Cancer ; Lung cancer ; Stroma ; Proteome ; Quantitative proteomics ; Biomarker discovery ; Elastin ; Cancer research ; Cell biology ; Immunology ; Medicine ; Bioinformatics ; Biochemistry ; Gene Advanced Proteomics Techniques and Applications ; Caveolin-1 and cellular processes ; Cancer, Lipids, and Metabolism
Authors: Simon Crase | Suresh N. Thennadil
Source: PLoS ONE (article, 2022-03-31)
Field: Chemistry — Analytical Chemistry

An analysis framework for clustering algorithm selection with applications to spectroscopy Cluster analysis is a valuable unsupervised machine learning technique that is applied in a multitude of domains to identify similarities or clusters in unlabelled data. However, its performance is dependent of the characteristics of the data it is being applied to. There is no universally best clustering algorithm, and hence, there are numerous clustering algorithms available with different performance characteristics. This raises the problem of how to select an appropriate clustering algorithm for the given analytical purposes. We present and validate an analysis framework to address this problem. Unlike most current literature which focuses on characterizing the clustering algorithm itself, we present a wider holistic approach, with a focus on the user's needs, the data's characteristics and the characteristics of the clusters it may contain. In our analysis framework, we utilize a softer qualitative approach to identify appropriate characteristics for consideration when matching clustering algorithms to the intended application. These are used to generate a small subset of suitable clustering algorithms whose performance are then evaluated utilizing quantitative cluster validity indices. To validate our analysis framework for selecting clustering algorithms, we applied it to four different types of datasets: three datasets of homemade explosives spectroscopy, eight datasets of publicly available spectroscopy data covering food and biomedical applications, a gene expression cancer dataset, and three classic machine learning datasets. Each data type has discernible differences in the composition of the data and the context within which they are used. Our analysis framework, when applied to each of these challenges, recommended differing subsets of clustering algorithms for final quantitative performance evaluation. For each application, the recommended clustering algorithms were confirmed to contain the top performing algorithms through quantitative performance indices. Cluster analysis ; Computer science ; Data mining ; Context (archaeology) ; Machine learning ; Consensus clustering ; Biclustering ; CURE data clustering algorithm ; Artificial intelligence ; Correlation clustering Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Metabolomics and Mass Spectrometry Studies
Authors: Fang Ou | Anne van Klinken | Petar Ševo | Maurangelo Petruzzella | Chenhui Li | Don M. J. van Elst | Kaylee D. Hakkel | Francesco Pagliano | René P. J. van Veldhoven | Andrea Fiore
Source: Sensors (article, 2022-09-16)
Field: Chemistry — Analytical Chemistry

Handheld NIR Spectral Sensor Module Based on a Fully-Integrated Detector Array For decades, near-infrared (NIR) spectroscopy has been a valuable tool for material analysis in a variety of applications, ranging from industrial process monitoring to quality assessment. Traditional spectrometers are typically bulky, fragile and expensive, which makes them unsuitable for portable and in-field use. Thus, there is a growing interest for miniaturized, robust and low-cost NIR sensors. In this study, we demonstrate a handheld NIR spectral sensor module, based on a fully-integrated multipixel detector array, sensitive in the 850-1700 nm wavelength range. Differently from a spectrometer, the spectral sensor measures a limited number of NIR spectral bands. The capabilities of the spectral sensor module were evaluated alongside a commercially available portable spectrometer for two application cases: to quantify the moisture content in rice grains and to classify plastic types. Both devices achieved the two sensing tasks with comparable performance. Moisture quantification was achieved with a root mean square error (RMSE) prediction of 1.4% and 1.1% by the spectral sensor and spectrometer, respectively. Classification of the plastic type was achieved with a prediction accuracy on unknown samples of 100% and 96.4% by the spectral sensor and spectrometer, respectively. The results from this study are promising and demonstrate the potential for the compact NIR modules to be used in a variety of NIR sensing applications. Spectrometer ; Near-infrared spectroscopy ; Detector ; Materials science ; Ranging ; Remote sensing ; Computer science ; Optics ; Physics ; Telecommunications Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Vitaliy Kurlin
Source: Foundations of Computational Mathematics (article, 2022-12-07)
Field: Materials Science — Materials Chemistry

Mathematics of 2-Dimensional Lattices Abstract A periodic lattice in Euclidean space is the infinite set of all integer linear combinations of basis vectors. Any lattice can be generated by infinitely many different bases. This ambiguity was partially resolved, but standard reductions remain discontinuous under perturbations modelling atomic displacements. This paper completes a continuous classification of 2-dimensional lattices up to Euclidean isometry (or congruence), rigid motion (without reflections), and similarity (with uniform scaling). The new homogeneous invariants allow easily computable metrics on lattices considered up to the equivalences above. The metrics up to rigid motion are especially non-trivial and settle all remaining questions on (dis)continuity of lattice bases. These metrics lead to real-valued chiral distances that continuously measure lattice deviations from higher-symmetry neighbours. The geometric methods extend the past work of Delone, Conway, and Sloane. Mathematics ; Integer lattice ; Lattice (music) ; Euclidean geometry ; Congruence (geometry) ; Euclidean space ; Pure mathematics ; Isometry (Riemannian geometry) ; Metric space ; Scaling ; Tetrahedron ; Combinatorics ; Geometry Quasicrystal Structures and Properties ; Topological and Geometric Data Analysis ; Surface Chemistry and Catalysis
Authors: Claire Griesbach | Tyler Gerczak | Yongfeng Zhang | Ramathasan Thevamaran
Source: Journal of Nuclear Materials (article, 2022-12-22)
Field: Materials Science — Materials Chemistry

Microstructural heterogeneity of the buffer layer of TRISO nuclear fuel particles Porosity ; Buffer (optical fiber) ; Materials science ; Microstructure ; Layer (electronics) ; Nuclear fuel ; Characterization (materials science) ; Particle (ecology) ; Composite material ; Chemical engineering ; Nuclear engineering ; Nanotechnology ; Computer science Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Graphite, nuclear technology, radiation studies
Authors: Stepan Vladimirovitch Lomov | N.A. Gudkov | Sergey G. Abaimov
Source: Polymers (article, 2022-11-08)
Field: Materials Science — Materials Chemistry

Uncertainties in Electric Circuit Analysis of Anisotropic Electrical Conductivity and Piezoresistivity of Carbon Nanotube Nanocomposites Electrical conductivity and piezoresistivity of carbon nanotube (CNT) nanocomposites are analyzed by nodal analysis for aligned and random CNT networks dependent on the intrinsic CNT conductivity and tunneling barrier values. In the literature, these parameters are assigned with significant uncertainty; often, the intrinsic resistivity is neglected. We analyze the variability of homogenized conductivity, its sensitivity to deformation, and the validity of the assumption of zero intrinsic resistivity. A fast algorithm for simulation of a gauge factor is proposed. The modelling shows: (1) the uncertainty of homogenization caused by the uncertainty in CNT electrical properties is higher than the uncertainty, caused by the nanocomposite randomness; (2) for defect-prone nanotubes (intrinsic conductivity ~10<sup>4</sup> S/m), the influence of tunneling barrier energy on both the homogenized conductivity and gauge factor is weak, but it becomes stronger for CNTs with higher intrinsic conductivity; (3) the assumption of infinite intrinsic conductivity (defect-free nanotubes) has strong influence on the homogenized conductivity. Electrical resistivity and conductivity ; Carbon nanotube ; Materials science ; Conductivity ; Nanocomposite ; Randomness ; Composite material ; Nanotube ; Anisotropy ; Quantum tunnelling ; Homogenization (climate) ; Condensed matter physics ; Mathematics ; Physics Carbon Nanotubes in Composites ; Graphene research and applications ; Nanopore and Nanochannel Transport Studies
Authors: Sinéad Knight | Davide Gianni | Adam G. Hendricks
Source: SLAS DISCOVERY (review, 2022-01-01)
Field: Chemistry — Organic Chemistry

Fragment-based screening: A new paradigm for ligand and target discovery Significant advances in fragment-based screening, including the emergence of Fully Functionalised Fragments (FFFs) and innovations in Covalent Fragment screening are providing a new paradigm for ligand and target discovery. FFFs offer some key distinct advantages over other screening modalities such as small molecules and genetic screens, including 1) An ability to access diverse chemical space employing a relatively small compound set 2) Ease of screen optimisation given there is no requirement for genetic manipulation and 3) Built-in proteomics tools to facilitate rapid target deconvolution directly in cells. Covalent fragments enable exploration of novel druggable nodes through irreversible fragment-cysteine interactions, complementing their fully functionalized counterparts. Both FFFs and Covalent fragments present the phenotypic screening community with an additional and complementary approach for disease centric target identification. Druggability ; Computational biology ; Fragment (logic) ; Phenotypic screening ; Chemical space ; Ligand efficiency ; Ligand (biochemistry) ; Small molecule ; Identification (biology) ; Combinatorial chemistry ; Drug discovery ; Computer science ; Biology ; Genetics ; Chemistry ; Bioinformatics ; Phenotype ; Gene Click Chemistry and Applications ; Chemical Synthesis and Analysis ; Peptidase Inhibition and Analysis
Authors: Shoulin Wang | Weihua Gong | Zhaowei Wang | Yubin Wei | Yanfang Li | Tingting Zhang | Qinduan Zhang | Lin Zhang | Fugang Song | Wei Zhang | Tongyu Liu
Source: Frontiers in Physics (article, 2022-11-07)
Field: Chemistry — Spectroscopy

Interference fringe suppression in tunable diode laser absorption spectroscopy based on CEEMDAN-WTD Owing to interference fringes in the multireflective gas cell, the detection sensitivity of a system in tunable diode laser absorption spectroscopy (TDLAS) will decrease significantly. In this work, a combined scheme of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet threshold denoising (WTD) is proposed. Theoretical simulations were performed to validate the effects of the proposed algorithm, which was also verified via a CO 2 detection experiment. After CEEMDAN-WTD processing, the noisy intrinsic mode function (IMF), pure IMF, and residual components of the detection signal were identified and reconstructed successfully. Based on analysis of the simulations, CEEMDAN-WTD algorithm improved the signal-to-noise ratio by 1.87 times and decreased the root mean-squared error by 37.6% than the moving average algorithm. For the CO 2 detection system, R 2 = 0.999 was determined by the calibration experiment. Additionally, based on Allan variance analysis and a long-time experiment, the limit of detection was estimated to be 3.08 ppm for an average time of 148 s and measurement accuracy of 0.65%, respectively. The obtained results sufficiently validate that the CEEMDAN-WTD algorithm can effectively suppress interference fringe noise in TDLAS. Noise (video) ; Interference (communication) ; Calibration ; Wavelet ; Sensitivity (control systems) ; Optics ; Hilbert–Huang transform ; Residual ; Signal-to-noise ratio (imaging) ; Laser ; Absorption (acoustics) ; SIGNAL (programming language) ; Physics ; Algorithm ; Mathematics ; Computer science ; Electronic engineering ; Statistics ; White noise ; Artificial intelligence ; Telecommunications ; Engineering Spectroscopy and Laser Applications ; Atmospheric Ozone and Climate ; Atmospheric and Environmental Gas Dynamics
Authors: Moon-Soo Kim | Hyokeun Lee | Hyun Kim | Hyuk‐Jae Lee
Source: IEEE Access (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

WL-WD: Wear-Leveling Solution to Mitigate Write Disturbance Errors for Phase-Change Memory Phase-change memory (PCM) is a promising non-volatile memory device due to its attractive properties such as fast access time and byte-addressability. However, PCM is still difficult to be used as a main memory because of its weakness in endurance and write disturbance. Conventional wear-leveling algorithms have attempted to handle short endurance, but they have not addressed the issue of write disturbance even though both issues are caused by write operations to PCM. This paper proposes a wear-leveling algorithm that addresses not only the endurance, but also the write disturbance. From the observation that the write disturbance errors and short endurance issues mostly take place in hot addresses, the proposed algorithm first detects hot addresses and maps them to ‘hot’ regions, which are customized memory regions designed to be robust to write disturbance errors and to support effective wear-leveling. The ‘hot’ region is not fixed to a specific part, but it moves over an entire memory space to make every cell belong to ‘hot’ regions in a uniform manner. On the other hand, cold addresses are mapped in ‘normal’ memory regions by simple linear mapping to reduce the hardware overhead. The proposed algorithm can reduce the write disturbance errors by more than 70% with only a slight instruction per cycle (IPC) degradation. Moreover, the wear-leveling performance is also enhanced by more than 3% compared to other wear-leveling algorithms. Disturbance (geology) ; Phase-change memory ; Computer science ; Phase (matter) ; Control theory (sociology) ; Phase change ; Geology ; Engineering ; Control (management) ; Artificial intelligence ; Physics Phase-change materials and chalcogenides ; Advanced Memory and Neural Computing ; Parallel Computing and Optimization Techniques
Authors: Marvin Masche | Jierong Liang | Kurt Engelbrecht | C.R.H. Bahl
Source: International Journal of Refrigeration (article, 2022-11-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Efficient modulation of the magnetocaloric refrigerator capacity Magnetocaloric energy conversion devices (e.g., room air conditioners and household refrigerators) have the potential to significantly reduce the emissions associated with refrigerant leakage into the atmosphere but still have lower efficiencies compared to mature vapor compression systems. The efficiency of a magnetocaloric cooling device derives not only from its design characteristics (e.g., solid refrigerant, hydraulic system, and magnet system) and its operating temperature span but also from its modulating capability. Owing to the lack of experimental data regarding this topic, the advantage of modulating the cooling capacity (i.e., the part-load performance) of an active magnetic regenerator prototype is demonstrated experimentally for the first time. The capacity modulation is carried out by means of regulating both the cycle frequency of the device and the volumetric flow rate of the heat transfer fluid. At a 14 K temperature span and a 1.4 Hz frequency, the magnetocaloric refrigerator prototype using 3.8 kg of gadolinium provided a maximum cooling capacity of 452 W with an appreciable coefficient of performance of 3.2, which corresponds to a second-law efficiency of 15.5 %. At part-load operating conditions, the device can produce a cooling capacity of 245 W with an increased second-law efficiency of 29.7 %, or a coefficient of performance of 6.2, making it more competitive with traditional vapor compression systems. In future studies, the experimental data obtained may be implemented in a dynamic building energy model to quantify the energy-saving benefits of part-load operation by estimating the overall system efficiency during a typical cooling season. Magnetic refrigeration ; Cooling capacity ; Refrigerant ; Coefficient of performance ; Vapor-compression refrigeration ; Regenerative heat exchanger ; Cooling load ; Nuclear engineering ; Materials science ; Refrigerator car ; Efficient energy use ; Air conditioning ; Chiller ; Gas compressor ; Environmental science ; Thermodynamics ; Mechanical engineering ; Electrical engineering ; Heat exchanger ; Engineering ; Physics ; Magnetic field Magnetic and transport properties of perovskites and related materials ; Multiferroics and related materials ; Advanced Condensed Matter Physics
Authors: Jiayou Chen | Chongshan Yang | Changbo Yuan | Yang Li | Ting An | Chunwang Dong
Source: Scientific Reports (article, 2022-12-01)
Field: Chemistry — Analytical Chemistry

Moisture content monitoring in withering leaves during black tea processing based on electronic eye and near infrared spectroscopy Monitoring the moisture content of withering leaves in black tea manufacturing remains a difficult task because the external and internal information of withering leaves cannot be simultaneously obtained. In this study, the spectral data and the color/texture information of withering leaves were obtained using near infrared spectroscopy (NIRS) and electronic eye (E-eye), respectively, and then fused to predict the moisture content. Subsequently, the low- and middle-level fusion strategy combined with support vector regression (SVR) was applied to detect the moisture level of withering leaves. In the middle-level fusion strategy, the principal component analysis (PCA) and random frog (RF) were employed to compress the variables and select effective information, respectively. The middle-level-RF (cutoff line = 0.8) displayed the best performance because this model used fewer variables and still achieved a satisfactory result, with 0.9883 and 5.5596 for the correlation coefficient of the prediction set (R<sub>p</sub>) and relative percent deviation (RPD), respectively. Hence, our study demonstrated that the proposed data fusion strategy could accurately predict the moisture content during the withering process. Water content ; Principal component analysis ; Environmental science ; Near-infrared spectroscopy ; Standard deviation ; Correlation coefficient ; Fusion ; Moisture ; Spectroscopy ; Sensor fusion ; Mathematics ; Computer science ; Materials science ; Artificial intelligence ; Statistics ; Optics ; Physics ; Geology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research