jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hao Wu | Huimin Xu | Yuxin Shi | Ting Yuan | Ting Meng | Yang Zhang | Wenjing Xie | Xiaohong Li | Yunchao Li | Louzhen Fan
Source: Chinese Journal of Chemistry (article, 2020-12-19)
Field: Materials Science — Materials Chemistry

Recent Advance in Carbon Dots: From Properties to Applications Abstract As a new type of nanomaterial, carbon dots (CDs) have been recognized as a versatile nanoplatform with enormous potential for biomedical and optoelectronic applications because of low cytotoxicity, photostability, biocompatibility and versatility in addition to their tunable photoluminescence. In this review, we aim at updating the latest research on preparation, optoelectronic and chemical properties of CDs as well as their biomedical and optoelectronic applications. We highlight the applications of CDs for stem cell imaging, cell nucleus imaging, two‐photon fluorescence imaging, tumor diagnosis and therapy and their optoelectronic applications including light‐emitting diodes (LEDs), solar cells and electrocatalysis. Furthermore, we also discuss current challenges and breakthroughs on machine‐learning‐driven synthesis of CDs as well as nucleic acid detection with an eye towards the future. Finally, we hope that this review will give further impulses to target‐oriented research on CDs for biomedical and optoelectronic applications. Nanotechnology ; Biocompatibility ; Nanomaterials ; Chemistry ; Photoluminescence ; Fluorescence ; Quantum dot ; Carbon fibers ; Optoelectronics ; Materials science ; Composite number Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Zhenpeng Yao | Benjamín Sánchez-Lengeling | N. Scott Bobbitt | Benjamin J. Bucior | Sai Govind Hari Kumar | Sean P. Collins | Thomas Burns | Tom K. Woo | Omar K. Farha | Randall Q. Snurr | Alán Aspuru‐Guzik
Source: ChemRxiv (preprint, 2020-04-27)
Field: Chemistry — Inorganic Chemistry

Inverse Design of Nanoporous Crystalline Reticular Materials with Deep Generative Models Reticular frameworks are crystalline porous materials that form &lt;i&gt;via&lt;/i&gt; the self-assembly of molecular building blocks (&lt;i&gt;i.e.&lt;/i&gt;, nodes and linkers) in different topologies. Many of them have high internal surface areas and other desirable properties for gas storage, separation, and other applications. The notable variety of the possible building blocks and the diverse ways they can be assembled endow reticular frameworks with a near-infinite combinatorial design space, making reticular chemistry both promising and challenging for prospective materials design. Here, we propose an automated nanoporous materials discovery platform powered by a supramolecular variational autoencoder (SmVAE) for the generative design of reticular materials with desired functions. We demonstrate the automated design process with a class of metal-organic framework (MOF) structures and the goal of separating CO&lt;sub&gt;2&lt;/sub&gt; from natural gas or flue gas. Our model exhibits high fidelity in capturing structural features and reconstructing MOF structures. We show that the autoencoder has a promising optimization capability when jointly trained with multiple top adsorbent candidates identified for superior gas separation. MOFs discovered here are strongly competitive against some of the best-performing MOFs/zeolites ever reported. This platform lays the groundwork for the design of reticular frameworks for desired applications. Reticular connective tissue ; Nanoporous ; Computer science ; Autoencoder ; Nanotechnology ; Materials science ; Artificial intelligence ; Deep learning Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis
Authors: Wei Zhang | Changchun Chai | Qingyang Fan | Yanxing Song | Yintang Yang
Source: Materials (article, 2020-04-19)
Field: Materials Science — Materials Chemistry

Penta-C20: A Superhard Direct Band Gap Carbon Allotrope Composed of Carbon Pentagon A metastable <i>sp</i><sup>3</sup>-bonded carbon allotrope, Penta-C<sub>20</sub>, consisting entirely of carbon pentagons linked through bridge-like bonds, was proposed and studied in this work for the first time. Its structure, stability, and electronic and mechanical properties were investigated based on first-principles calculations. Penta-C<sub>20</sub> is thermodynamically and mechanically stable, with equilibrium total energy of 0.718 and 0.184 eV/atom lower than those of the synthesized T-carbon and supercubane, respectively. Penta-C<sub>20</sub> can also maintain dynamic stability under a high pressure of 100 GPa. Ab initio molecular dynamics (AIMD) simulations indicates that this new carbon allotrope can maintain thermal stability at 800 K. Its Young's modulus exhibits mechanical anisotropy. The calculated ideal tensile and shear strengths confirmed that Penta-C<sub>20</sub> is a superhard material with a promising application prospect. Furthermore, Penta-C<sub>20</sub> is a direct band gap carbon based semiconducting material with band gap of 2.89 eV. Pentagon ; Materials science ; Carbon fibers ; Band gap ; Nanotechnology ; Composite material ; Optoelectronics ; Geometry Boron and Carbon Nanomaterials Research ; Fullerene Chemistry and Applications ; Graphene research and applications
Authors: Juan Ivorra Martínez | Isabel Seguí Verdú | O. Fenollar | Lourdes Sánchez‐Nacher | Rafael Balart | Luis Quiles‐Carrillo
Source: Polymers (article, 2020-05-14)
Field: Materials Science — Biomaterials

Manufacturing and Properties of Binary Blend from Bacterial Polyester Poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) and Poly(caprolactone) with Improved Toughness Polyhydroxyalkanoates (PHAs) represent a promising group of bacterial polyesters for new applications. Poly(3-hydroxybutyrate-<i>co</i>-3-hydroxyhexanoate) (PHBH) is a very promising bacterial polyester with potential uses in the packaging industry; nevertheless, as with many (almost all) bacterial polyesters, PHBH undergoes secondary crystallization (aging) which leads to an embrittlement. To overcome or minimize this, in the present work a flexible petroleum-derived polyester, namely poly(ε-caprolactone), was used to obtain PHBH/PCL blends with different compositions (from 0 to 40 PCL wt %) using extrusion followed by injection moulding. The thermal analysis of the binary blends was studied by means of differential scanning calorimetry (DSC) and thermogravimetry (TGA). Both TGA and DSC revealed immiscibility between PHBH and PCL. Mechanical dynamic thermal analysis (DMTA) allowed a precise determination of the glass transition temperatures (<i>T</i><sub>g</sub>) as a function of the blend composition. By means of field emission scanning electron microscopy (FESEM), an internal structure formed by two phases was observed, with a PHBH-rich matrix phase and a finely dispersed PCL-rich phase. These results confirmed the immiscibility between these two biopolymers. However, the mechanical properties obtained through tensile and Charpy tests, indicated that the addition of PCL to PHBH considerably improved toughness. PHBH/PCL blends containing 40 PCL wt % offered an impact resistance double that of neat PHBH. PCL addition also contributed to a decrease in brittleness and an improvement in toughness and some other ductile properties. As expected, an increase in ductile properties resulted in a decrease in some mechanical resistant properties, e.g., the modulus and the strength (in tensile and flexural conditions) decreased with increasing wt % PCL in PHBH/PCL blends. Materials science ; Polyester ; Differential scanning calorimetry ; Toughness ; Dynamic mechanical analysis ; Caprolactone ; Polycaprolactone ; Composite material ; Charpy impact test ; Polymer blend ; Polymer ; Polymerization ; Copolymer biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Carbon dioxide utilization in catalysis
Authors: Anees Mohammed | Juan I. Melecio | Siniša Đurović
Source: IEEE Sensors Journal (article, 2020-02-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electrical Machine Permanent Magnets Health Monitoring and Diagnosis Using an Air-Gap Magnetic Sensor This study proposes a magnetic sensor design and application for monitoring the health of rotor magnets in permanent magnet (PM) electrical machines through in-situ observation of the air-gap magnetic flux density. The reported device employs the concept of Fibre Bragg Grating (FBG) strain sensing fusion with magnetostrictive material to deliver a machine stator slot wedge integrated sensor that allows straightforward installation and retrofit with no invasive action to core elements of the machine. The sensing theory, design, prototyping, calibration and installation of the proposed magnetic sensing scheme are detailed in the paper. The sensor was installed into an inverter driven surface mount PM synchronous machine (SPMSM) and its performance for in-situ observation of rotor PM magnetization conditions validated in a range of healthy and demagnetised PM conditions tests. The obtained experimental data demonstrate the reported device's capability to enable recognition of rotor PMs' magnetisation level and thus their health monitoring. Finally, a fault index is proposed and experimentally validated that allows the application of in-situ magnetic sensor measurements for relative quantification of PM demagnetization fault severity. Magnet ; Stator ; Magnetostriction ; Air gap (plumbing) ; Magnetization ; Rotor (electric) ; Electronic engineering ; Acoustics ; Materials science ; Engineering ; Electrical engineering ; Mechanical engineering ; Magnetic field ; Physics Magnetic Properties and Applications ; Electric Motor Design and Analysis ; Magnetic Bearings and Levitation Dynamics
Authors: Ningliang Liu | Yaxiong Guo | Houmin Jiang | Weisong Yi
Source: Journal of Biomedical Optics (article, 2020-06-27)
Field: Chemistry — Analytical Chemistry

Gastric cancer diagnosis using hyperspectral imaging with principal component analysis and spectral angle mapper These results suggest that NIR-HSI has the potential as a cutting-edge optical diagnostic technique for gastric cancer diagnosis with suitable chemometrics. Hyperspectral imaging ; Chemometrics ; Principal component analysis ; Imaging spectroscopy ; Pixel ; Near-infrared spectroscopy ; Spectral imaging ; Optics ; Pattern recognition (psychology) ; Materials science ; Artificial intelligence ; Computer science ; Chemistry ; Physics ; Chromatography Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Optical Imaging and Spectroscopy Techniques
Authors: Diana Samodova | Christopher M. Hosfield | Christian N. Cramer | Maria Valeria Giuli | Enrico Cappellini | Giulia Franciosa | Michael M. Rosenblatt | Christian D. Kelstrup | Jesper V. Olsen
Source: Molecular & Cellular Proteomics (article, 2020-10-05)
Field: Chemistry — Spectroscopy

ProAlanase is an Effective Alternative to Trypsin for Proteomics Applications and Disulfide Bond Mapping Trypsin is the protease of choice in bottom-up proteomics. However, its application can be limited by the amino acid composition of target proteins and the pH of the digestion solution. In this study we characterize ProAlanase, a protease from the fungus <i>Aspergillus niger</i> that cleaves primarily on the C-terminal side of proline and alanine residues. ProAlanase achieves high proteolytic activity and specificity when digestion is carried out at acidic pH (1.5) for relatively short (2 h) time periods. To elucidate the potential of ProAlanase in proteomics applications, we conducted a series of investigations comprising comparative multi-enzymatic profiling of a human cell line proteome, histone PTM analysis, ancient bone protein identification, phosphosite mapping and <i>de novo</i> sequencing of a proline-rich protein and disulfide bond mapping in mAb. The results demonstrate that ProAlanase is highly suitable for proteomics analysis of the arginine- and lysine-rich histones, enabling high sequence coverage of multiple histone family members. It also facilitates an efficient digestion of bone collagen thanks to the cleavage at the C terminus of hydroxyproline which is highly prevalent in collagen. This allows to identify complementary proteins in ProAlanase- and trypsin-digested ancient bone samples, as well as to increase sequence coverage of noncollagenous proteins. Moreover, digestion with ProAlanase improves protein sequence coverage and phosphosite localization for the proline-rich protein Notch3 intracellular domain (N3ICD). Furthermore, we achieve a nearly complete coverage of N3ICD protein by <i>de novo</i> sequencing using the combination of ProAlanase and tryptic peptides. Finally, we demonstrate that ProAlanase is efficient in disulfide bond mapping, showing high coverage of disulfide-containing regions in a nonreduced mAb. Disulfide bond ; Proteomics ; Trypsin ; Chemistry ; Computational biology ; Computer science ; Biochemistry ; Biology ; Enzyme Advanced Proteomics Techniques and Applications ; Biotin and Related Studies ; Molecular Biology Techniques and Applications
Authors: Rui Yao | Jun Li | Meng Hui | Lin Bai | Qisheng Wu
Source: IEEE Access (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Feature Selection Based on Random Forest for Partial Discharges Characteristic Set Since the dimension of combined feature set for partial discharge (PD) pattern recognition is higher, the corresponding sample size increases, as does the required amount of storage space and calculation, and there are features with less category-related characteristics in the feature parameters, which may contain redundant information between them. To solve the problem of higher feature dimension and complicated classification model required for the identification of partial discharge insulation defect type in this paper. Random forest sequential forward selection method based on variance analysis (RF-VA) is proposed for the optimal subset selection. This method is improved in two aspects. Firstly, a method based on variance analysis is proposed, which measures feature differences between categories, and obtains a modified arrangement displacement scheme to guide rearrangement of the order of values taken on data sample out of bag. Secondly, the sequence forward search method used to do feature selection could get iteration evaluation results, which solves randomness to determine the size of feature subset and instability of the results existing in the original algorithm. The results show RF-VA can obtain a better subset of features. It is feasible to reduce the dimension of partial discharge characteristic set, and effectively improve the identification rate of partial discharge defect type. Feature selection ; Random forest ; Computer science ; Feature (linguistics) ; Selection (genetic algorithm) ; Set (abstract data type) ; Pattern recognition (psychology) ; Artificial intelligence ; Data mining High voltage insulation and dielectric phenomena ; Geophysical Methods and Applications ; Geoscience and Mining Technology
Authors: Nusrat Sahiba | Shikha Agarwal
Source: Topics in Current Chemistry (review, 2020-08-10)
Field: Chemistry — Organic Chemistry

Recent Advances in the Synthesis of Perimidines and their Applications Chemistry ; Nanotechnology ; Combinatorial chemistry ; Materials science Synthesis and Biological Evaluation ; Phenothiazines and Benzothiazines Synthesis and Activities ; Chemical Reaction Mechanisms
Authors: Hamidreza Siampour | Ou Wang | Vladimir A. Zenin | Sergejs Boroviks | Petr Siyushev | Yuanqing Yang | Valery A. Davydov | L. F. Kulikova | V. Agafonov | Alexander Kubanek | N. Asger Mortensen | Fedor Jelezko | Sergey I. Bozhevolnyi
Source: Nanophotonics (article, 2020-02-01)
Field: Materials Science — Materials Chemistry

Ultrabright single‐photon emission from germanium‐vacancy zero‐phonon lines: deterministic emitter‐waveguide interfacing at plasmonic hot spots Abstract Striving for nanometer‐sized solid‐state single‐photon sources, we investigate atom‐like quantum emitters based on single germanium‐vacancy (GeV) centers isolated in crystalline nanodiamonds (NDs). Cryogenic characterization indicated symmetry‐protected and bright (&gt;10 6 counts/s with off‐resonance excitation) zero‐phonon optical transitions with up to 6‐fold enhancement in energy splitting of their ground states as compared to that found for GeV centers in bulk diamonds (i.e. up to 870 GHz in highly strained NDs vs. 150 GHz in bulk). Utilizing lithographic alignment techniques, we demonstrate an integrated nanophotonic platform for deterministic interfacing plasmonic waveguides with isolated GeV centers in NDs, which enables 10‐fold enhancement of single‐photon decay rates along with the emission direction control by judiciously designing and positioning a Bragg reflector. This approach allows one to realize the unidirectional emission from single‐photon dipolar sources, thereby opening new perspectives for the realization of quantum optical integrated circuits. Plasmon ; Photon ; Phonon ; Optoelectronics ; Germanium ; Purcell effect ; Physics ; Single-photon source ; Quantum optics ; Spontaneous emission ; Materials science ; Optics ; Condensed matter physics ; Laser ; Silicon Diamond and Carbon-based Materials Research ; Advanced Fiber Laser Technologies ; Mechanical and Optical Resonators
Authors: Guoxiang Wang | Haizhou Shi | Andriy Lotnyk | Daotian Shi | Rongping Wang
Source: NPG Asia Materials (article, 2020-02-14)
Field: Materials Science — Materials Chemistry

Conversion of p–n conduction type by spinodal decomposition in Zn-Sb-Bi phase-change alloys Abstract Phase-change films with multiple resistance levels are promising for increasing the storage density in phase-change memory technology. Diffusion-dominated Zn 2 Sb 3 films undergo transitions across three states, from high through intermediate to low resistance, upon annealing. The properties of the Zn 2 Sb 3 material can be further optimized by doping with Bi. Based on scanning transmission electron microscopy combined with electrical transport measurements, at a particular Bi concentration, the conduction of Zn-Sb-Bi compounds changes from p- to n-type, originating from spinodal decomposition. Simultaneously, the change in the temperature coefficient of resistivity shows a metal-to-insulator transition. Further analysis of microstructure characteristics reveals that the distribution of the Bi-Sb phase may be the origin of the driving force for the p–n conduction and metal-to-insulator transitions and therefore may provide us with another way to improve multilevel data storage. Moreover, the Bi doping promotes the thermoelectric properties of the studied alloys, leading to higher values of the power factor compared to known reported structures. The present study sheds valuable light on the spinodal decomposition process caused by Bi doping, which can also occur in a wide variety of chalcogenide-based phase-change materials. In addition, the study provides a new strategy for realizing novel p–n heterostructures for multilevel data storage and thermoelectric applications. Materials science ; Spinodal decomposition ; Chalcogenide ; Seebeck coefficient ; Doping ; Electrical resistivity and conductivity ; Annealing (glass) ; Thermoelectric effect ; Microstructure ; Phase-change memory ; Analytical Chemistry (journal) ; Phase (matter) ; Condensed matter physics ; Chemical physics ; Nanotechnology ; Optoelectronics ; Thermodynamics ; Metallurgy ; Composite material ; Thermal conductivity ; Chemistry Phase-change materials and chalcogenides ; Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films
Authors: Yuan Tian | Ruihao Yuan | Dezhen Xue | Yumei Zhou | Xiangdong Ding | Jun Sun | Turab Lookman
Source: Journal of Applied Physics (article, 2020-07-02)
Field: Materials Science — Materials Chemistry

Role of uncertainty estimation in accelerating materials development via active learning An active learning strategy using sampling based on uncertainties shows the promise of accelerating the development of new materials. We study the efficiencies of the active learning iteration loop with different uncertainty estimators to find the “best” material in four different experimental datasets. We use a bootstrap approach aggregating with support vector regression as the base learner to obtain uncertainties associated with model predictions. If the bootstrap replicate number B is small, the variance estimated by the empirical standard error estimator is found to be close to the true variance, whereas the jackknife based estimators give an upward or downward biased estimation of variance. As B increases, the bias of the jackknife based estimators decreases and the variance estimated finally converges to the true one. Therefore, the empirical standard error estimator needs the least number of iteration loops to find the best material in the datasets, especially when the bootstrap replicate number B is small. Our work demonstrates that an appropriate Bootstrap replicate B is conducive to minimizing calculation costs during the materials property optimization by active learning. Jackknife resampling ; Estimator ; Replicate ; Variance (accounting) ; Sampling (signal processing) ; Computer science ; Mathematics ; Statistics ; Active learning (machine learning) Machine Learning in Materials Science ; Machine Learning and Algorithms ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Valentin Blanchard | Damien Garçon | Catherine Jaunet | Kévin Chemello | Stéphanie Billon‐Crossouard | Audrey Aguesse | Aya Garfa | Gilles Famchon | Amada Torres | Cédric Le May | Matthieu Pichelin | Edith Bigot‐Corbel | Gilles Lambert | Bertrand Cariou | Samy Hadjadj | Michel Krempf | Kalyane Bach‐Ngohou | Mikaël Croyal
Source: Journal of Lipid Research (article, 2020-05-13)
Field: Chemistry — Spectroscopy

A high-throughput mass spectrometry-based assay for large-scale profiling of circulating human apolipoproteins Apolipoproteins govern lipoprotein metabolism and are promising biomarkers of metabolic and cardiovascular diseases. Unlike immunoassays, MS enables the quantification and phenotyping of multiple apolipoproteins. Hence, here, we aimed to develop a LC-MS/MS assay that can simultaneously quantitate 18 human apolipoproteins [A-I, A-II, A-IV, A-V, B48, B100, C-I, C-II, C-III, C-IV, D, E, F, H, J, L1, M, and (a)] and determined apoE, apoL1, and apo(a) phenotypes in human plasma and serum samples. The plasma and serum apolipoproteins were trypsin digested through an optimized procedure and peptides were extracted and analyzed by LC-MS/MS. The method was validated according to standard guidelines in samples spiked with known peptide amounts. The LC-MS/MS results were compared with those obtained with other techniques, and reproducibility, dilution effects, and stabilities were also assessed. Peptide markers were successfully selected for targeted apolipoprotein quantification and phenotyping. After optimization, the assay was validated for linearity, lower limits of quantification, accuracy (biases: -14.8% to 12.1%), intra-assay variability [coefficients of variation (CVs): 1.5-14.2%], and inter-assay repeatability (CVs: 4.1-14.3%). Bland-Altman plots indicated no major statistically significant differences between LC-MS/MS and other techniques. The LC-MS/MS results were reproducible over five repeated experiments (CVs: 1.8-13.7%), and we identified marked differences among the plasma and serum samples. The LC-MS/MS assay developed here is rapid, requires only small sampling volumes, and incurs reasonable costs, thus making it amenable for a wide range of studies of apolipoprotein metabolism. We also highlight how this assay can be implemented in laboratories. Chromatography ; Repeatability ; Coefficient of variation ; Chemistry ; Mass spectrometry ; Selected reaction monitoring ; Reproducibility ; Apolipoprotein B ; Tandem mass spectrometry ; Cholesterol ; Biochemistry Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Diabetes, Cardiovascular Risks, and Lipoproteins