jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Francisco Burgos-Flórez | Alexander Rodríguez | Eliana Cervera | Valtencir Zucolotto | Marco Sanjuán | Pedro Villalba
Source: PLoS ONE (article, 2022-02-07)
Field: Chemistry — Electrochemistry

TBISTAT: An open-source, wireless portable, electrochemical impedance spectroscopy capable potentiostat for the point-of-care detection of S100B in plasma samples Point-of-Care (POC) testing for biomarker detection demands techniques that are easy to use, readily available, low-cost, and with rapid response times. This paper describes the development of a fully open-source, modular, wireless, battery-powered, smartphone-controlled, low-cost potentiostat capable of conducting electrochemical impedance spectroscopy for the electrochemical detection of the S100B protein captured in an ANTI-S100B functionalized thin-film gold interdigitated electrode platform to support traumatic brain injury diagnosis and treatment. EIS results from the developed potentiostat were validated with a commercial benchtop potentiostat by comparing impedance magnitude and phase values along the EIS frequency range. In addition, an experimental design was performed for detecting S100B in spiked human plasma samples with S100B concentrations of clinical utility, and a calibration curve was found for quantifying S100B detection. No statistically significant differences were found between EIS results from the developed potentiostat and the commercial potentiostat. Statistically significant differences in the changes in charge transfer resistance signal between each tested S100B concentration (p < 0.05) were found, with a limit of detection of 35.73 pg/mL. The modularity of the proposed potentiostat allows easier component changes according to the application demands in power, frequency excitation ranges, wireless communication protocol, signal amplification and transduction, precision, and sampling frequency of ADC, among others, when compared to state-of-the-art open-source EIS potentiostats. In addition, the use of minimal, easy acquirable open-source hardware and software, high-level filtering, accurate ADC, Fast Fourier Transform with low spectral leakage, wireless communication, and the simple user interface provides a framework for facilitating EIS analysis and developing new affordable instrumentation for POC biosensors integrated systems. Potentiostat ; Dielectric spectroscopy ; Materials science ; Point of care ; Detection limit ; Computer science ; Electronic engineering ; Analytical Chemistry (journal) ; Electrode ; Chemistry ; Engineering ; Medicine ; Electrochemistry Electrochemical Analysis and Applications ; S100 Proteins and Annexins ; Analytical Chemistry and Sensors
Authors: Maedeh Taheri | Jonas Brown | Adil Rehman | Nicholas Sesing | Fariborz Kargar | Tina T. Salguero | Sergey Rumyantsev | Alexander A. Balandin
Source: ACS Nano (article, 2022-10-31)
Field: Materials Science — Materials Chemistry

Electrical Gating of the Charge-Density-Wave Phases in Two-Dimensional <i>h</i>-BN/1T-TaS<sub>2</sub> Devices We report on the electrical gating of the charge-density-wave phases and current in h-BN-capped three-terminal 1T-TaS2 heterostructure devices. It is demonstrated that the application of a gate bias can shift the source–drain current–voltage hysteresis associated with the transition between the nearly commensurate and incommensurate charge-density-wave phases. The evolution of the hysteresis and the presence of abrupt spikes in the current while sweeping the gate voltage suggest that the effect is electrical rather than self-heating. We attribute the gating to an electric-field effect on the commensurate charge-density-wave domains in the atomic planes near the gate dielectric. The transition between the nearly commensurate and incommensurate charge-density-wave phases can be induced by both the source–drain current and the electrostatic gate. Since the charge-density-wave phases are persistent in 1T-TaS2 at room temperature, one can envision memory applications of such devices when scaled down to the dimensions of individual commensurate domains and few-atomic plane thicknesses. Charge density wave ; Materials science ; Hysteresis ; Gating ; Charge density ; Condensed matter physics ; Dielectric ; Voltage ; Optoelectronics ; Heterojunction ; Electric field ; Electrical engineering ; Physics ; Superconductivity 2D Materials and Applications ; Organic and Molecular Conductors Research ; Ferroelectric and Negative Capacitance Devices
Authors: M. Chandler Bennett | Guoxiang Hu | Guangming Wang | Olle Heinonen | Paul R. C. Kent | Jaron T. Krogel | Panchapakesan Ganesh
Source: Physical Review Research (article, 2022-04-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Origin of metal-insulator transitions in correlated perovskite metals The mechanisms that drive metal-to-insulator transitions (MIT) in correlated solids are not fully understood, though intricate couplings of charge, spin, orbital, and lattice degrees of freedom have been implicated. For example, the perovskite SrCoO 3 is a ferromagnetic metal, while the oxygen-deficient (n-doped) brownmillerite SrCoO 2.5 is an antiferromagnetic insulator. Given the magnetic and structural transitions that accompany the MIT, the driving force for such a MIT transition is unclear. We also observe that, interestingly, the perovskite metals LaNiO 3 , SrFeO 3 , and SrCoO 3 also undergo MIT when n-doped via high-to-low valence compositional changes, i.e., Ni 3+ Fe 4+ , Sr 2+ La 3+ , and Sr 2+ La 3+ , respectively. On the other hand, pressurizing the insulating brownmillerite SrCoO 2.5 phase drives a gap closing. Here we demonstrate that the ABO 3 perovskites most prone to MIT are self-hole-doped materials, reminiscent of a negative charge-transfer metal, using a combination of density functional and fixed-node diffusion quantum Monte Carlo calculations. Upon n doping the negative charge-transfer metallic phase, an underlying charge-lattice (or electron-phonon) coupling drives the metal to a charge and bond-disproportionated gapped insulating state, thereby achieving ligand-hole passivation at certain sites only. The size of the band gap is linearly correlated with the degree of hole passivation at these ligand sites. Further, metallization via pressure is also stabilized by a similar increase in the ligand hole, which in turn stabilizes the ferromagnetic coupling. These results suggest that the interaction that drives the band-gap opening to realize a MIT even in correlated metals is the charge-transfer energy, while it couples with the underlying phonons to enable the transition to the insulating phase. Other orderings (magnetic, charge, orbital etc.) driven by weaker interactions may assist gap openings at low doping levels, but it is the charge-transfer energy that predominantly determines the band gap, with a negative energy preferring the metallic phase. This n doping can be achieved by modulations in oxygen stoichiometry or metal composition or pressure. Hence, controlling the amount of the ligand hole, set by the charge-transfer energy, is the key factor in controlling MIT. Condensed matter physics ; Brownmillerite ; Materials science ; Ferromagnetism ; Perovskite (structure) ; Antiferromagnetism ; Metal–insulator transition ; Doping ; Charge ordering ; Passivation ; Metal ; Chemistry ; Crystallography ; Charge (physics) ; Nanotechnology ; Physics Magnetic and transport properties of perovskites and related materials ; Advanced Condensed Matter Physics ; Electronic and Structural Properties of Oxides
Authors: Liyao Ma | Peng Wei | Xinhua Qu | Shuhui Bi | Yuan Zhou | Tao Shen
Source: CAAI Transactions on Intelligence Technology (article, 2022-09-20)
Field: Chemistry — Analytical Chemistry

Apple grading method based on neural network with ordered partitions and evidential ensemble learning Abstract In order to improve the performance of the automatic apple grading and sorting system, in this paper, an ensemble model of ordinal classification based on neural network with ordered partitions and Dempster–Shafer theory is proposed. As a non‐destructive grading method, apples are graded into three grades based on the Soluble Solids Content value, with features extracted from the preprocessed near‐infrared spectrum of apple serving as model inputs. Considering the uncertainty in grading labels, mass generation approach and evidential encoding scheme for ordinal label are proposed, with uncertainty handled within the framework of Dempster–Shafer theory. Constructing neural network with ordered partitions as the base learner, the learning procedure of the Bagging‐based ensemble model is detailed. Experiments on Yantai Red Fuji apples demonstrate the satisfactory grading performances of proposed evidential ensemble model for ordinal classification. Grading (engineering) ; Artificial intelligence ; Artificial neural network ; Pattern recognition (psychology) ; Ensemble learning ; Dempster–Shafer theory ; Computer science ; Evidential reasoning approach ; Machine learning ; Mathematics ; Data mining ; Engineering ; Decision support system Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing and Land Use
Authors: Thomas C. Terwilliger | Billy K. Poon | Pavel V. Afonine | Christopher J. Schlicksup | Tristan I. Croll | Claudia Millán | Jane S. Richardson | Randy J. Read | Paul D. Adams
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-01-07)
Field: Materials Science — Materials Chemistry

Improved AlphaFold modeling with implicit experimental information Machine learning prediction algorithms such as AlphaFold 1 and RoseTTAFold 2 can create remarkably accurate protein models, but these models usually have some regions that are predicted with low confidence or poor accuracy 3–6 . We hypothesized that by implicitly including experimental information, a greater portion of a model could be predicted accurately, and that this might synergistically improve parts of the model that were not fully addressed by either machine learning or experiment alone. An iterative procedure was developed in which AlphaFold models are automatically rebuilt based on experimental density maps and the rebuilt models are used as templates in new AlphaFold predictions. We find that including experimental information improves prediction beyond the improvement obtained with simple rebuilding guided by the experimental data. This procedure for AlphaFold modeling with density has been incorporated into an automated procedure for crystallographic and electron cryo-microscopy map interpretation. Computer science ; Simple (philosophy) ; Interpretation (philosophy) ; Artificial intelligence ; Experimental data ; Machine learning ; Algorithm ; Data mining ; Mathematics ; Statistics Enzyme Structure and Function ; Protein Structure and Dynamics ; Advanced Electron Microscopy Techniques and Applications
Authors: Conor J. McCluskey | Matthew G. Colbear | James P. V. McConville | Shane J. McCartan | Jesi R. Maguire | Michele Conroy | Kalani Moore | Alan Harvey | Felix Trier | U. Bangert | Alexei Gruverman | Manuel Bibès | Amit Kumar | Raymond G. P. McQuaid | J. M. Gregg
Source: Advanced Materials (article, 2022-06-23)
Field: Materials Science — Materials Chemistry

Ultrahigh Carrier Mobilities in Ferroelectric Domain Wall Corbino Cones at Room Temperature Recently, electrically conducting heterointerfaces between dissimilar band insulators (such as lanthanum aluminate and strontium titanate) have attracted considerable research interest. Charge transport and fundamental aspects of conduction have been thoroughly explored. Perhaps surprisingly, similar studies on conceptually much simpler conducting homointerfaces, such as domain walls, are not nearly so well developed. Addressing this disparity, magnetoresistance is herein reported in approximately conical 180° charged domain walls, in partially switched ferroelectric thin-film single-crystal lithium niobate. This system is ideal for such measurements: first, the conductivity difference between domains and domain walls is unusually large (a factor of 10<sup>13</sup> ) and hence currents driven through the thin film, between planar top and bottom electrodes, are overwhelmingly channeled along the walls; second, when electrical contact is made to the top and bottom of the domain walls and a magnetic field is applied along their cone axes, then the test geometry mirrors that of a Corbino disk: a textbook arrangement for geometric magnetoresistance measurement. Data imply carriers with extremely high room-temperature Hall mobilities of up to ≈3700 cm<sup>2</sup> V<sup>-1</sup> s<sup>-1</sup> . This is an unparalleled value for oxide interfaces (and for bulk oxides) comparable to mobilities in other systems seen at cryogenic, rather than at room, temperature. Materials science ; Mobilities ; Ferroelectricity ; Domain (mathematical analysis) ; Condensed matter physics ; Optoelectronics ; Physics Electronic and Structural Properties of Oxides ; Ferroelectric and Piezoelectric Materials ; Acoustic Wave Resonator Technologies
Authors: Hideaki Iwasawa | Tetsuro Ueno | T. Masui | S. Tajima
Source: npj Quantum Materials (article, 2022-02-18)
Field: Materials Science — Materials Chemistry

Unsupervised clustering for identifying spatial inhomogeneity on local electronic structures Abstract Spatial inhomogeneity on the electronic structure is one of the vital keys to provide a better understanding of the emergent quantum phenomenon. Given the recent developments on spatially resolved ARPES (ARPES: angle-resolved photoemission spectroscopy), the information on the spatial inhomogeneity on the local electronic structure is now accessible. However, the next challenge becomes apparent as the conventional analysis encounters difficulty handling a large volume of a spatial mapping dataset, typically generated in the spatially resolved ARPES experiments. Here, we propose a machine-learning-based approach using unsupervised clustering algorithms (K-means and fuzzy-c-means) to examine the spatial mapping dataset. Our analysis methods enable automated categorization of the spatial mapping dataset with a much-reduced human intervention and workload, thereby allowing quick identification and visualization of the spatial inhomogeneity on the local electronic structures. Angle-resolved photoemission spectroscopy ; Spatial analysis ; Computer science ; Cluster analysis ; Artificial intelligence ; Electronic structure ; Visualization ; Pattern recognition (psychology) ; Data mining ; Physics ; Remote sensing ; Geography Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Expert finding and Q&A systems
Authors: Pavel Dmitriev | Boris L. Kozlovsky | Denis P. Kupriushkin | Anastasiya A. Dmitrieva | Vishnu D. Rajput | Vasiliy Chokheli | Ekaterina Tarik | Olga Kapralova | Valeriy K. Tokhtar | Tatiana Minkina | Tatiana V. Varduni
Source: Remote Sensing (article, 2022-05-19)
Field: Chemistry — Analytical Chemistry

Assessment of Invasive and Weed Species by Hyperspectral Imagery in Agrocenoses Ecosystem The present study aimed to investigate the possibility of using hyperspectral imaging data to identify the invasive and weed species in agrocenoses ecosystem. The most common weeds in grain agrocenoses, i.e., Ambrosia artemisiifolia L., Euphorbia seguieriana Neck., Atriplex tatarica L., Glycyrrhiza glabra L., Setaria pumila (Poir.) Roem. and Schult, served as objects. The population of weeds, especially Ambrosia artemisiifolia is invasive for the selected region of study. Therefore, the shooting of objects was carried out with a hyperspectral camera, Cubert UHD185, and the values of 100 spectral channels were obtained from hyperspectral images. The values of 80 vegetation indices (VIs) were calculated. The material was processed using mathematical statistics (analysis of variance, t-test) and search methods of data analysis (principal component analysis, decision tree, and random forest). Using statistical methods, the simultaneous use of several VIs differentiated between species more deliberately and precisely. The combination of VIs Derivative index (D1), Chlorophyll content index (Datt3), and Pigment specific normalized difference (PSND) can be used for weeds identification. Using the decision tree method, VIs established a good division of weeds into groups; (1) perennial rhizomatous weeds (Euphorbia seguieriana, and Glycyrrhiza glabra), and (2) annual weeds (A. artemisiifolia, A. tatarica, and S. pumila); These Vis are Chlorophyll index (CI), D1, and Datt3. Using the random forest method, the VIs that have the greatest impact on Mean Decrease Accuracy and Mean Decrease Gini are D1, Datt3, PSND, and Double Peak Index (DPI). The use of spectral channel values for the identification of plant species using the principal component analysis, decision tree, and random forest methods showed worse results than when using VIs. A great similarity of the results was obtained with the help of statistical and search methods of data analysis. Hyperspectral imaging ; Ambrosia artemisiifolia ; Weed ; Botany ; Biology ; Environmental science ; Remote sensing ; Geography ; Ragweed Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Kendall R. Johnson | Michal Greguš | Alexander R. Ivanov
Source: Journal of Proteome Research (article, 2022-09-16)
Field: Chemistry — Spectroscopy

Coupling High-Field Asymmetric Ion Mobility Spectrometry with Capillary Electrophoresis-Electrospray Ionization-Tandem Mass Spectrometry Improves Protein Identifications in Bottom-Up Proteomic Analysis of Low Nanogram Samples In this work, we pioneered the assessment of coupling high-field asymmetric waveform ion mobility spectrometry (FAIMS) with ultrasensitive capillary electrophoresis hyphenated with tandem mass spectrometry (CE-MS/MS) to achieve deeper proteome coverage of low nanogram amounts of digested cell lysates. An internal stepping strategy using three or four compensation voltages per analytical run with varied cycle times was tested to determine optimal FAIMS settings and MS parameters for the CE-FAIMS-MS/MS method. The optimized method applied to bottom-up proteomic analysis of 1 ng of HeLa protein digest standard identified 1314 ± 30 proteins, 4829 ± 200 peptide groups, and 7577 ± 163 peptide spectrum matches (PSMs) corresponding to a 16, 25, and 22% increase, respectively, over CE-MS/MS alone, without FAIMS. Furthermore, the percentage of acquired MS/MS spectra that resulted in PSMs increased nearly 2-fold with CE-FAIMS-MS/MS. Label-free quantitation of proteins and peptides was also assessed to determine the precision of replicate analyses from FAIMS methods with increased cycle times. Our results also identified from 1 ng of HeLa protein digest without any prior enrichment 76 ± 9 phosphopeptides, 18% of which were multiphosphorylated. These results represent a 46% increase in phosphopeptide identifications over the control experiments without FAIMS yielding 2.5-fold more multiphosphorylated peptides. Chemistry ; Ion-mobility spectrometry ; Chromatography ; Tandem mass spectrometry ; Mass spectrometry ; Electrospray ionization ; Isobaric labeling ; Proteome ; Electrospray ; Analytical Chemistry (journal) ; Capillary electrophoresis ; Phosphopeptide ; Peptide ; Protein mass spectrometry ; Biochemistry Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Microfluidic and Capillary Electrophoresis Applications
Authors: Vali Rasooli Sharabiani | Araz Soltani-Nazarloo | Ebrahim Taghinezhad | Ibham Veza | Antoni Szumny | Adam Figiel
Source: Food Science & Nutrition (article, 2022-10-06)
Field: Chemistry — Analytical Chemistry

Prediction of winter wheat leaf chlorophyll content based on <scp>VIS</scp>/<scp>NIR</scp> spectroscopy using <scp>ANN</scp> and <scp>PLSR</scp> Visible-near-infrared spectroscopy is known for its rapid and nondestructive characteristics designed to predict leaf chlorophyll content (LCC) of winter wheat. It is believed that the nonlinear technique is preferable to the linear method. The canopy reflectance was applied to generate the LCC prediction model. To accomplish such an objective, artificial neural networks (ANN), along with partial least squares regression (PLSR), nonlinear, and linear evaluation methods have been employed and evaluated to predict wheat LCC. The wheat leaves reflectance spectra were initially preprocessed using Savitzky-Golay smoothing, differentiation (first derivative), SNV (Standard Normal Variate), MSC (Multiplicative Scatter Correction), and their combinations. Afterward, a model for LCC using the reflectance spectra was developed by means of the PLS and ANN. The vis/NIR spectroscopy samples at the 350-1400 nm wavelength were preprocessed using S. Golay smoothing, D<sub>1</sub>, SNV, and MSC. The preprocessing with SNV-S.G, followed by PLS and ANN modeling, was able to achieve the most accurate prediction, with the correlation coefficient of 0.92 and 0.97, along with the root mean square error of 0.9131 and 0.7305 receptivity. The experimental findings also revealed that the suggested method utilizing the PLS and ANN model with SNV-S. G preprocessing was practically feasible to estimate the chlorophyll content of a particular winter wheat leaf area according to the visible and near-infrared spectroscopy sensors, achieving improved precision and accuracy. The nonlinear technique was proposed as a more refined technique for LCC estimating. Partial least squares regression ; Biological system ; Correlation coefficient ; Mean squared error ; Mathematics ; Near-infrared spectroscopy ; Coefficient of determination ; Artificial neural network ; Hyperspectral imaging ; Remote sensing ; Analytical Chemistry (journal) ; Chemistry ; Algorithm ; Artificial intelligence ; Computer science ; Statistics ; Biology ; Optics ; Physics ; Chromatography Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Remote Sensing in Agriculture