jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: W John Lough
Source: ChromCom (article, 2020-09-01)
Field: Chemistry — Spectroscopy

John H. Knox an Appreciation Porous graphitic carbon HPLC packing material developed by John Knox's research group and commercialised as Hypercarb has been available on the market for over thirty years. However, the unique properties of this phase still prove to be fundamentally novel and provide solutions to difficult separation applications. Of these properties, its lack of an elutropic series and hence ability to provide good retention not just in reversed-phase HPLC but even with non-aqueous solvents was put to good effect in this work. Chromatography ; High-performance liquid chromatography ; Chemistry ; Selectivity ; Porosity ; Resolution (logic) ; Homologous series ; Organic chemistry ; Chemical engineering ; Computer science Analytical Chemistry and Chromatography ; Analytical chemistry methods development ; Adsorption, diffusion, and thermodynamic properties of materials
Authors: A Rodriguez-Mata | Abraham Rivera Ugalde | Adrian Lizarraga | Adrin Ramrez | Infinity Control | Adrian Development | Carina Gutierrez Ojeda | Carlos Alberto | Ramrez Fuentes | Carlos Osorio | Quero Towards | Carlos Snchez-Lpez | Carlos Vzquez | Csar Carrillo | Theresa Christeena | Thomas | Lourdes Cinthya | Toledo-Peral | Jessica Citlalli | Romero | Claudia Mendoza-Martnez | Daniel Durini | Daniel Romero | Daniel Ferrusca | Daniel Martinez-Padron | J | Cesar Mendoza
Source:  (article, 2020-11-11)
Field: Materials Science — Materials Chemistry

CCE 2020 Index Synthesis and characterization of zinc oxide nanowires Characterization (materials science) ; Index (typography) ; Nanowire ; Zinc ; Materials science ; Computer science ; Nanotechnology ; Metallurgy ; World Wide Web ZnO doping and properties ; Copper-based nanomaterials and applications ; Nanowire Synthesis and Applications
Authors: Leander Reascos | Fábio Carneiro | André Pereira | N. F. Castro | Ricardo Mendes Ribeiro
Source: arXiv (Cornell University) (preprint, 2020-06-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Berry: A code for the differentiation of Bloch wavefunctions from DFT calculations Density functional calculations of electronic structures of materials is one of the most used techniques in theoretical solid state physics. These calculations retrieve single electron wavefunctions and their eigenenergies. The berry suite of programs amplifies the usefulness of DFT by ordering the eigenstates in analytic bands, allowing the differentiation of the wavefunctions in reciprocal space. It can then calculate Berry connections and curvatures and the second harmonic generation conductivity. The berry software is implemented for two dimensional materials and was tested in hBN and InSe. In the near future, more properties and functionalities are expected to be added. Wave function ; Berry connection and curvature ; Eigenvalues and eigenvectors ; Reciprocal ; Reciprocal lattice ; Quantum mechanics ; Physics ; Density functional theory ; Electron ; Geometric phase Organic and Molecular Conductors Research ; Solid-state spectroscopy and crystallography ; Inorganic Chemistry and Materials
Authors: H Ramezani | T Khan | G Reali | L Felicetti | S Reda | J See Rosenstein | O Resendis-Antonio | M Banzhaf | J Rivera De La Rosa | I See Flores Urquizo | R Roehe | M Wang | B Romeny | F Huang | G Ropmay | N Das | C Rose | J Rosenstein | P Weber | E Kim | J Sello | J Geiser | E Kennedy | C Arcadia | A Dombroski | K Oakley | S Chen | H Tann | B Rubenstein | C Roychaudhuri | N Salama | H See Ammar | H Sanchez Correa | I Flores Urquizo | N Sharifi | S Shi | Y Zhou | G Holmes | Y Chen | P Sharma | L Chouhan | S Sharma | K Deka | V Bhatia | A Shehu | S Tadepalli | S Shende | A Jadhao | U Cheang | X Shi | C See Chen | X Li | A Shrivastava | D Das | R Mahapatra | S Mohanty | L Singh | B See Kaushik | R Singh | S Kumar | G Slaughter | A See Baingane | B Song | L See Pan | J Gao | T Song | Q Zou | P Zheng | F Stradolini | A Tuoheti | H Su | Y Su | S Li | C Zheng | X Zhang | D Sun | H Liu | J Sun | Y Wang | S Sun | J See Zhu | N Akhter
Source: IEEE Transactions on NanoBioscience (article, 2020-10-01)
Field: Materials Science — Materials Chemistry

2020 Index IEEE Transactions on NanoBioscience Vol. 19 Index (typography) ; Computer science ; World Wide Web Advanced Nanomaterials in Catalysis
Authors: Natan Nagar | Noa Ecker | Gil Loewenthal | Oren Avram | Daniella Ben‐Meir | Dvora Biran | Eliora Z. Ron | Tal Pupko
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-10-05)
Field: Chemistry — Spectroscopy

Harnessing machine learning to unravel protein degradation in <i>Escherichia coli</i> Abstract Degradation of intracellular proteins in Gram-negative bacteria regulates various cellular processes and serves as a quality control mechanism by eliminating damaged proteins. To understand what causes the proteolytic machinery of the cell to degrade some proteins while sparing others, we employed a quantitative pulsed-SILAC (Stable Isotope Labeling with Amino acids in Cell culture) method followed by mass spectrometry analysis to determine the half-lives for the proteome of exponentially growing Escherichia coli , under standard conditions. We developed a likelihood-based statistical test to find actively degraded proteins, and identified dozens of novel proteins that are fast-degrading. Finally, we used structural, physicochemical and protein-protein interaction network descriptors to train a machine-learning classifier to discriminate fast-degrading proteins from the rest of the proteome. Our combined computational-experimental approach provides means for proteomic-based discovery of fast degrading proteins in bacteria and the elucidation of the factors determining protein half-lives and have implications for protein engineering. Moreover, as rapidly degraded proteins may play an important role in pathogenesis, our findings could identify new potential antibacterial drug targets. Stable isotope labeling by amino acids in cell culture ; Proteome ; Escherichia coli ; Protein degradation ; Biology ; Computational biology ; Proteomics ; Bacteria ; Biochemistry ; Cell biology ; Genetics ; Gene Advanced Proteomics Techniques and Applications ; RNA and protein synthesis mechanisms ; Bacterial Genetics and Biotechnology
Authors: Huolin L. Xin
Source: Microscopy Today (article, 2020-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Microscopy and Microanalysis 2020 in Milwaukee, WI The Microscopy Society of America (MSA) and the Micro- Microanalysis ; Microscopy ; Materials science ; Chemistry ; Optics ; Physics ; Organic chemistry Electron and X-Ray Spectroscopy Techniques ; Advanced X-ray and CT Imaging ; Advanced Materials Characterization Techniques
Authors: Elizabeth Grace Mayer
Source: UNM’s Digital Repository (University of New Mexico) (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

An Improved Method for Spectroscopic Quality Classification Spectral quality classification is a vital step in data cleaning before the analysis of magnetic resonance spectroscopy (MRS) data can be done. This analysis compares five methods of quality classification; three of these are legacy methods, Maudsley et al. (2006), Zhang et al. (2018), and Bustillo et al. (2020), and two newly created methods that used a random forests classifier (RFC) to inform their classifications. We found that the random forest classifier was the most accurate at predicting spectra quality (balanced accuracy for RF of 88% vs legacy of 70%, 72%, or 72%). A Random-Forests-Informed Filtering method (RFIFM) for quality classification was created by bounding four of the highest ranking features in the RFC to mimic the classification methods of the legacy methodologies. The RFIFM had only slightly decreased accuracy compared to the RFC (85% vs 88%), but still outclassed the legacy methods. Overall, the top features in the RFC show that the best measures of quality relate to the frequency of the metabolite peaks in the spectra. Quality (philosophy) ; Computer science ; Artificial intelligence ; Epistemology ; Philosophy Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Guillaume Guarino | Ahmed Samet | Amir Nafi | Denis Cavallucci
Source: SPIRE - Sciences Po Institutional REpository (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

SummaTRIZ: Summarization Networks for Mining Patent Contradiction TRIZ theory is considered as a kind of innovative theory, which mainly functions in solving contradiction and aims to improve engineers creativity.It may reason on different support such as web and scientific documents.Most of the current knowledge, especially in industrial domains lies in patents.They are, nevertheless, an underused resource because of their complexity, their length and the need of domain knowledge to make use of patent.In this paper, we introduce an innovative application of deep learning to mine knowledge from patents.We show that our summarization based approach, called Sum-maTRIZ 1 , enables the extraction of these contradictions essential for TRIZ problem solving engine.A BERT based summarization approach is introduced to retain contradiction sentences.Our approach is experimentally evaluated on a real data set showing the performance of SummaTRIZ. Automatic summarization ; Computer science ; Contradiction ; Information retrieval ; Linguistics ; Philosophy Machine Learning in Materials Science ; Web Data Mining and Analysis ; Topic Modeling
Authors: Paul Boulard
Source: SPIRE - Sciences Po Institutional REpository (preprint, 2020-11-13)
Field: Materials Science — Materials Chemistry

Contribution à l’évaluation multi-physique du risque de retour en criticité du corium en cas d’accident grave dans un réacteur de type REP Following the Fukushima accident, the subject of a possible risk of criticality of corium has been the subject of renewed interest. In a severe accident, the fuel - if not cooled in time - will eventually melt and form a mixture called corium. This mixture can puncture the vessel and spill into the vessel pit. If water is sprayed on, the corium will fragment into small debris (median size observed in FARO experiments ≈ 3 mm). Under certain conditions (porosity, liquid water presence rate) the debris bed can reach criticality. The energy released during a reactivity transient must be estimated in order to evaluate the consequences of such an event.The objective of this thesis is to broaden the knowledge on the criticality of debris beds and to propose a methodology to evaluate the energy deposited in case of a return to criticality.The evaluation of the deposited energy requires that all the physical aspects (neutron and thermal-hydraulic) be simulated in transient regime, which implies very time-consuming numerical simulations. One of the objectives of this work was therefore the search for the optimization of the calculation time (in this thesis through the use of meta-models).Concerning the neutron behaviour, simulated using the APOLLO2 or TRIPOLI-4 codes or via condensed descriptions of "kinetic point" type, the thesis work also focused on the use of random geometries and the possibility to model the size distribution of debris by a "representative" debris size. The use of a 2D mesh (R, Z) to describe the debris bed allows a finer evaluation of the counter-reactions (Doppler and moderator effects) and thus of the energy deposited during the transient simulation.Concerning thermo-hydraulics, a thermal diffusion model has been developed to obtain the temperature within the debris and a multiphase simulation code (liquid water, steam water, debris) MC3D has been selected to simulate the thermo-hydraulic state of the debris bed (temperature and void rate fields).The implementation of a coupling of the disciplines mentioned above made it possible to study the criticality risk as a function of the residual power produced by the corium, or to evaluate the energy deposited in a representative configuration of a debris bed. Humanities ; Corium ; Physics ; Art ; Thermodynamics Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear and radioactivity studies
Authors: Xien Yin Yap | Kim Seng Chia | Hasan Ali Gamal Al-Kaf | Hasan Ali Gamal Al-kaf
Source: International Journal of Integrated Engineering (article, 2020-08-31)
Field: Chemistry — Analytical Chemistry

Investigating the Feasibility of a CMOS Camera in Developing a Shortwave Near Infrared Spectroscopy Near infrared spectroscopy is a non-invasive and optical technology that uses the relative absorption of near infrared light (i.e. 780 to 2500nm). Near infrared spectroscopy has been successfully applied in the evaluation of food quality [1], Near infrared (NIR) sensing technology has been widely implemented in various areas e.g. medical [3]-[7], agriculture [2], [8]-[14], and industries [15]-[17] replace conventional wet chemistry analysis due to its non-Abstract: Near infrared (NIR) sensing technology has been widely implemented in various areas as an alternative to substitute conventional wet chemistry analysis and sensing applications due to its non-invasive, green, and rapid measurement features. Recent researches indicate that a combination of NIR light emitted diode (LEDs) and photodiodes is promising to reduce the financial barriers to carry out NIR research in various applications. However, there is a challenge to detect and remove unwanted signals particularly ambient light and the changes of surrounding. This is because NIR signals are susceptible to the change of temperatures, moisture, and types of samples. This is worth to highlight that camera technology is feasible to remove unwanted backgrounds and insusceptible to the background for various applications e.g. face recognition, unmanned vehicle systems, and object classification. Therefore, this study aims to investigate the feasibility of a complementary metal-oxidesemiconductor (CMOS) camera in developing a shortwave NIR spectroscopy. Firstly, a slit, a NIR grating, and a CMOS camera were positioned and shielded in a black aluminum chassis. A total of six different parameters of the camera were investigated in this study, i.e. the exposure level, gain, white balance, brightness, sharpness, and saturation. Findings show that the CMOS camera with the optimal values of the exposure level and the gain could produce a good quality of NIR spectrum compared with default settling, in which, the signals were saturated. Thus, CMOS camera is feasible to be used to develop a low-cost NIR spectroscopy. Near-infrared spectroscopy ; CMOS ; Shortwave ; Functional near-infrared spectroscopy ; Materials science ; Light-emitting diode ; Spectroscopy ; Optoelectronics ; Photodiode ; Computer science ; Environmental science ; Remote sensing ; Optics ; Physics ; Geography Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Dinesha V C
Source: World Journal of Advanced Research and Reviews (article, 2020-02-28)
Field: Materials Science — Polymers and Plastics

Impact Resistance and Failure Analysis of Bio-Composite Materials Bio-composite materials reinforced with natural fibers have gained significant attention as sustainable alternatives to conventional synthetic composites due to their renewability, low density, reduced environmental impact, and competitive mechanical performance. Among the various mechanical properties, impact resistance is a critical parameter governing the applicability of bio-composites in structures subjected to dynamic and accidental loading conditions. This research article presents a comprehensive investigation into the impact resistance and failure mechanisms of bio-composite materials reinforced with natural fibers such as flax, hemp, jute, sisal, and kenaf combined with thermoset and thermoplastic matrices. The study discusses the influence of material characteristics, fiber architecture, fiber–matrix interfacial bonding, moisture absorption, and strain-rate sensitivity on impact behavior. Standardized low-velocity and high-velocity impact testing methodologies are reviewed, along with advanced non-destructive evaluation and microscopic characterization techniques used to analyze damage initiation and evolution. Typical failure modes including matrix cracking, fiber breakage, delamination, and fiber–matrix debonding are examined in detail. Computational modeling approaches, including finite element analysis, cohesive zone modeling, and multi-scale simulations, are also discussed to highlight their role in predicting impact response and guiding material design. The findings indicate that properly engineered bio-composites can achieve impact energy absorption comparable to glass fiber composites, making them suitable for automotive, construction, and consumer product applications. This work provides valuable insights into the design, characterization, and optimization of impact-resistant bio-composite materials and supports their broader adoption in sustainable engineering applications. Kenaf ; Natural fiber ; Impact resistance ; Finite element method ; Glass fiber ; Thermosetting polymer ; Delamination (geology) ; Epoxy ; Fiber ; Work (physics) Natural Fiber Reinforced Composites ; Bamboo properties and applications ; Mechanical Behavior of Composites
Authors: Stephen T. Skowron | Chen Huang | Angus I. Kirkland | Andrey Chuvilin | Elena Besley
Source: Microscopy and Microanalysis (article, 2020-07-30)
Field: Materials Science — Materials Chemistry

Probing Chemical Kinetics in Two Dimensional Materials Using Atomic Resolution Imaging. Materials science ; Kinetics ; Chemical imaging ; Analytical Chemistry (journal) ; Nanotechnology ; Chemistry ; Physics ; Environmental chemistry ; Geology ; Remote sensing Electronic and Structural Properties of Oxides ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Cameron J. Brown | Diego A. Maldonado | Antony D. Vassileiou | Blair F. Johnston | Alastair J. Florence
Source: ChemRxiv (preprint, 2020-08-11)
Field: Materials Science — Materials Chemistry

Data Mining Crystallization Kinetics Population balance model is a valuable modelling tool which facilitates the optimization and understanding of crystallization processes. However, in order to use this tool, it is necessary to have previous knowledge of the crystallization kinetics, specifically crystal growth and nucleation. The majority of approaches to achieve proper estimations of kinetic parameters required experimental data. Across time, a vast literature about the estimation of kinetic parameters and population balances have been published. Considering the availability of data, this work built a database with information on solute, solvent, kinetic expression, parameters, crystallization method and seeding. Correlations were assessed and clusters structures identified by hierarchical clustering analysis. The final database contains 336 data of kinetic parameters from 185 different sources. The data were analysed using kinetic parameters of the most common expressions. Subsequently, clusters were identified for each kinetic model. With these clusters, classification random forest models were made using solute descriptors, seeding, solvent, and crystallization methods as classifiers. Random forest models had an overall classification accuracy higher than 70% whereby they were useful to provide rough estimates of kinetic parameters, although these methods have some limitations. Crystallization ; Nucleation ; Cluster analysis ; Kinetic energy ; Seeding ; Population ; Data mining ; Computer science ; Biological system ; Random forest ; Experimental data ; Materials science ; Mathematics ; Statistics ; Machine learning ; Thermodynamics ; Physics Crystallization and Solubility Studies
Authors: Julia Stille | Jevgenijs Tjutrins | Guanyu Wang | Felipe A. Venegas | Christopher Hennecker | Andres Mauricio Rueda | Caitlin E. Miron | Sharon Pinus | Anne Labarre | Jessica Plescia | Mihai Burai Patrascu | Danielle Vlaho | Mitchell Huot | Anthony Mittermaier | Nicolas Moitessier
Source: ChemRxiv (preprint, 2020-10-15)
Field: Chemistry — Organic Chemistry

Design, Synthesis and Biological Evaluation of Novel SARS-CoV-2 3CLpro Covalent Inhibitors Severe diseases such as the ongoing COVID-19 pandemic, as well as the previous SARS and MERS outbreaks, are the result of coronavirus infections and have demonstrated the urgent need for antiviral drugs to combat these deadly viruses. Due to its essential role in viral replication and function, 3CLpro has been identified as a promising target for the development of antiviral drugs. Previously reported SARS-CoV 3CLpro non-covalent inhibitors were used as a starting point for the development of covalent inhibitors of SARS-CoV-2 3CLpro. We report herein our efforts in design and synthesis which led to submicromolar covalent inhibitors when the enzymatic activity of the viral protease was used as a screening platform. Covalent bond ; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ; Coronavirus ; Virology ; Coronavirus disease 2019 (COVID-19) ; Viral replication ; Chemistry ; Enzyme ; Virus ; Pharmacology ; Biology ; Biochemistry ; Medicine ; Infectious disease (medical specialty) Click Chemistry and Applications ; Computational Drug Discovery Methods ; Cancer therapeutics and mechanisms
Authors: Jordan Beth Vincent | Hui Wang | Omar Nibouche | Paul Maguire
Source: ChemRxiv (preprint, 2020-02-26)
Field: Chemistry — Spectroscopy

Detecting Trace Methane Levels with Plasma Optical Emission Spectroscopy and Supervised Machine Learning Trace methane detection in the parts per million range is reported using a novel detection scheme based on optical emission spectra from low temperature atmospheric pressure microplasmas. These bright low-cost plasma sources were operated under non-equilibrium conditions, producing spectra with a complex and variable sensitivity to trace levels of added gases. A data-driven machine learning approach based on Partial Least Squares Discriminant Analysis (PLS-DA) was implemented for CH4 concentrations up to 100 ppm in He, to provide binary classification of samples above or below a threshold of 2 ppm. With a low-resolution spectrometer and a custom spectral alignment procedure, a prediction accuracy of 98% was achieved, demonstrating the power of machine learning with otherwise prohibitively complex spectral analysis. This work establishes proof of principle for low cost and high-resolution trace gas detection with the potential for field deployment and autonomous remote monitoring. Trace gas ; Methane ; TRACE (psycholinguistics) ; Spectroscopy ; Linear discriminant analysis ; Spectrometer ; Analytical Chemistry (journal) ; Support vector machine ; Artificial intelligence ; Chemistry ; Computer science ; Optics ; Physics ; Environmental chemistry Spectroscopy and Laser Applications ; Advanced Chemical Sensor Technologies ; Mass Spectrometry Techniques and Applications