jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Timothy A. Coulther | Jaeju Ko | Mary Jo Ondrechen
Source: The Journal of Chemical Physics (article, 2021-05-17)
Field: Chemistry — Organic Chemistry

Amino acid interactions that facilitate enzyme catalysis Interactions in enzymes between catalytic and neighboring amino acids and how these interactions facilitate catalysis are examined. In examples from both natural and designed enzymes, it is shown that increases in catalytic rates may be achieved through elongation of the buffer range of the catalytic residues; such perturbations in the protonation equilibria are, in turn, achieved through enhanced coupling of the protonation equilibria of the active ionizable residues with those of other ionizable residues. The strongest coupling between protonation states for a pair of residues that deprotonate to form an anion (or a pair that accept a proton to form a cation) is achieved when the difference in the intrinsic pK<sub>a</sub>s of the two residues is approximately within 1 pH unit. Thus, catalytic aspartates and glutamates are often coupled to nearby acidic residues. For an anion-forming residue coupled to a cation-forming residue, the elongated buffer range is achieved when the intrinsic pK<sub>a</sub> of the anion-forming residue is higher than the intrinsic pK<sub>a</sub> of the (conjugate acid of the) cation-forming residue. Therefore, the high pK<sub>a</sub>, anion-forming residues tyrosine and cysteine make good coupling partners for catalytic lysine residues. For the anion-cation pairs, the optimum difference in intrinsic pK<sub>a</sub>s is a function of the energy of interaction between the residues. For the energy of interaction ε expressed in units of (ln 10)RT, the optimum difference in intrinsic pK<sub>a</sub>s is within ∼1 pH unit of ε. Protonation ; Chemistry ; Residue (chemistry) ; Catalysis ; Acid dissociation constant ; Stereochemistry ; Amino acid ; Cysteine ; Enzyme ; Ion ; Crystallography ; Organic chemistry ; Aqueous solution ; Biochemistry Free Radicals and Antioxidants ; Protein Structure and Dynamics ; Photosynthetic Processes and Mechanisms
Authors: Jack W. Jordan | Kayleigh L. Y. Fung | Stephen T. Skowron | Christopher S. Allen | Johannes Biskupek | Graham N. Newton | Ute Kaiser | Andrei N. Khlobystov
Source: Chemical Science (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Single-molecule imaging and kinetic analysis of intermolecular polyoxometalate reactions We induce and study reactions of polyoxometalate (POM) molecules, [PW<sub>12</sub>O<sub>40</sub>]<sup>3-</sup> (Keggin) and [P<sub>2</sub>W<sub>18</sub>O<sub>62</sub>]<sup>6-</sup> (Wells-Dawson), at the single-molecule level. Several identical carbon nanotubes aligned side by side within a bundle provided a platform for spatiotemporally resolved imaging of <i>ca.</i> 100 molecules encapsulated within the nanotubes by transmission electron microscopy (TEM). Due to the entrapment of POM molecules their proximity to one another is effectively controlled, limiting molecular motion in two dimensions but leaving the third dimension available for intermolecular reactions between pairs of neighbouring molecules. By coupling the information gained from high resolution structural and kinetics experiments <i>via</i> the variation of key imaging parameters in the TEM, we shed light on the reaction mechanism. The dissociation of W-O bonds, a key initial step of POM reactions, is revealed to be reversible by the kinetic analysis, followed by an irreversible bonding of POM molecules to their nearest neighbours, leading to a continuous tungsten oxide nanowire, which subsequently transforms into amorphous tungsten-rich clusters due to progressive loss of oxygen atoms. The overall intermolecular reaction can therefore be described as a step-wise reductive polycondensation of POM molecules, <i>via</i> an intermediate state of an oxide nanowire. Kinetic analysis enabled by controlled variation of the electron flux in TEM revealed the reaction to be highly flux-dependent, which leads to reaction rates too fast to follow under the standard TEM imaging conditions. Although this presents a challenge for traditional structural characterisation of POM molecules, we harness this effect by controlling the conditions around the molecules and tuning the imaging parameters in TEM, which combined with theoretical modelling and image simulation, can shed light on the atomistic mechanisms of the reactions of POMs. This approach, based on the direct space and real time chemical reaction analysis by TEM, adds a new method to the arsenal of single-molecule kinetics techniques. Polyoxometalate ; Intermolecular force ; Kinetic energy ; Molecule ; Chemistry ; Computational chemistry ; Chemical physics ; Organic chemistry ; Catalysis ; Physics Polyoxometalates: Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications
Authors: Montserrat Pérez | A. Ramirez | Zainab Khan | Manola Moretti | Charlotte A. E. Hauser
Source: Advances in Polymer Technology (article, 2021-01-12)
Field: Materials Science — Biomaterials

Supramolecular Biopolymers for Tissue Engineering Supramolecular biopolymers (SBPs) are those polymeric units derived from macromolecules that can assemble with each other by noncovalent interactions. Macromolecular structures are commonly found in living systems such as proteins, DNA/RNA, and polysaccharides. Bioorganic chemistry allows the generation of sequence-specific supramolecular units like SBPs that can be tailored for novel applications in tissue engineering (TE). SBPs hold advantages over other conventional polymers previously used for TE; these materials can be easily functionalized; they are self-healing, biodegradable, stimuli-responsive, and nonimmunogenic. These characteristics are vital for the further development of current trends in TE, such as the use of pluripotent cells for organoid generation, cell-free scaffolds for tissue regeneration, patient-derived organ models, and controlled delivery systems of small molecules. In this review, we will analyse the 3 subtypes of SBPs: peptide-, nucleic acid-, and oligosaccharide-derived. Then, we will discuss the role that SBPs will be playing in TE as dynamic scaffolds, therapeutic scaffolds, and bioinks. Finally, we will describe possible outlooks of SBPs for TE. Supramolecular chemistry ; Tissue engineering ; Materials science ; Macromolecule ; Nanotechnology ; Bioorganic chemistry ; Biomedical engineering ; Molecule ; Biochemistry ; Biology ; Chemistry ; Organic chemistry Supramolecular Self-Assembly in Materials ; 3D Printing in Biomedical Research ; Chemical Synthesis and Analysis
Authors: Yuri Haraguchi | Yasuhiko Igarashi | Hiroaki Imai | Yuya Oaki
Source: Digital Discovery (article, 2021-12-18)
Field: Materials Science — Materials Chemistry

Sparse modeling for small data: case studies in controlled synthesis of 2D materials Straightforward, interpretable, and modifiable linear-regression prediction models with appropriate accuracy are constructed by sparse modeling coupled with our chemical perspectives as researchers on small data, such as experimental data in laboratories. Computer science ; Data mining ; Linear regression ; Regression ; Regression analysis ; Machine learning ; Mathematics ; Statistics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Piotr Cysewski | Maciej Przybyłek | Anna Kowalska-Kępczyńska | Natalia Tymorek
Source: International Journal of Molecular Sciences (article, 2021-07-08)
Field: Chemistry — Organic Chemistry

Thermodynamics and Intermolecular Interactions of Nicotinamide in Neat and Binary Solutions: Experimental Measurements and COSMO-RS Concentration Dependent Reactions Investigations In this study, the temperature-dependent solubility of nicotinamide (niacin) was measured in six neat solvents and five aqueous-organic binary mixtures (methanol, 1,4-dioxane, acetonitrile, DMSO and DMF). It was discovered that the selected set of organic solvents offer all sorts of solvent effects, including co-solvent, synergistic, and anti-solvent features, enabling flexible tuning of niacin solubility. In addition, differential scanning calorimetry was used to characterize the fusion thermodynamics of nicotinamide. In particular, the heat capacity change upon melting was measured. The experimental data were interpreted by means of COSMO-RS-DARE (conductor-like screening model for realistic solvation-dimerization, aggregation, and reaction extension) for concentration dependent reactions. The solute-solute and solute-solvent intermolecular interactions were found to be significant in all of the studied systems, which was proven by the computed mutual affinity of the components at the saturated conditions. The values of the Gibbs free energies of pair formation were derived at an advanced level of theory (MP2), including corrections for electron correlation and zero point vibrational energy (ZPE). In all of the studied systems the self-association of nicotinamide was found to be a predominant intermolecular complex, irrespective of the temperature and composition of the binary system. The application of the COSMO-RS-DARE approach led to a perfect match between the computed and measured solubility data, by optimizing the parameter of intermolecular interactions. Intermolecular force ; Thermodynamics ; Binary number ; Chemistry ; Nicotinamide ; Organic chemistry ; Physics ; Molecule ; Mathematics Free Radicals and Antioxidants ; Thermodynamic properties of mixtures ; Analytical Chemistry and Chromatography
Authors: Mohammad Najafi | Nadia Esfandiari | Bizhan Honarvar | Zahra Arab Aboosadi
Source: Periodica Polytechnica Chemical Engineering (article, 2021-06-22)
Field: Chemistry — Analytical Chemistry

Production of Rosuvastatin Calcium Nanoparticles Using Gas Antisolvent Technique The activity of pharmaceutical substances crucially depends on the bioavailability of the substances. The bioavailability of drugs in body and their rate of dissolution in the biological fluids are increased if the particle size is decreased. In the present paper, the Gas Anti-Solvent (GAS) method was used to lower the size of rosuvastatin particles. The effects of temperature (313–338 K), pressure (105–180 bar) and initial solute concentration (20–60 mg/ml) were evaluated by Response Surface Methodology (RSM). The optimum initial solute concentration, temperature and pressure were found to be 20 mg/ml, 313 K and 180 bar, respectively which resulted in the minimum particle size. Furthermore, the particles were characterized by Differential Scanning Calorimetry (DSC), Dynamic Light Scattering (DLS), Fourier Transform Infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), and X-Ray Diffraction (XRD). The analyses showed that the rosuvastatin particles (60.3 nm) precipitated by GAS process become significantly smaller than the initial particles (45.8 µm). Differential scanning calorimetry ; Dynamic light scattering ; Fourier transform infrared spectroscopy ; Dissolution ; Particle size ; Bioavailability ; Scanning electron microscope ; Analytical Chemistry (journal) ; Chemistry ; Bar (unit) ; Nanoparticle ; Materials science ; Particle (ecology) ; Chromatography ; Chemical engineering ; Nuclear chemistry ; Nanotechnology ; Thermodynamics ; Organic chemistry ; Composite material ; Physical chemistry Analytical Methods in Pharmaceuticals ; Crystallization and Solubility Studies ; Drug Solubulity and Delivery Systems
Authors: Elizma van Wyngaard | Erna Blancquaert | H.H. Nieuwoudt | José Luis Aleixandre-Tudó
Source: Frontiers in Plant Science (review, 2021-09-03)
Field: Chemistry — Analytical Chemistry

Infrared Spectroscopy and Chemometric Applications for the Qualitative and Quantitative Investigation of Grapevine Organs The fourth agricultural revolution is leading us into a time of using data science as a tool to implement precision viticulture. Infrared spectroscopy provides the means for rapid and large-scale data collection to achieve this goal. The non-invasive applications of infrared spectroscopy in grapevines are still in its infancy, but recent studies have reported its feasibility. This review examines near infrared and mid infrared spectroscopy for the qualitative and quantitative investigation of intact grapevine organs. Qualitative applications, with the focus on using spectral data for categorization purposes, is discussed. The quantitative applications discussed in this review focuses on the methods associated with carbohydrates, nitrogen, and amino acids, using both invasive and non-invasive means of sample measurement. Few studies have investigated the use of infrared spectroscopy for the direct measurement of intact, fresh, and unfrozen grapevine organs such as berries or leaves, and these studies are examined in depth. The chemometric procedures associated with qualitative and quantitative infrared techniques are discussed, followed by the critical evaluation of the future prospects that could be expected in the field. Infrared spectroscopy ; Spectroscopy ; Infrared ; Near-infrared spectroscopy ; Chemometrics ; Environmental science ; Computer science ; Biological system ; Chemistry ; Biology ; Optics ; Machine learning ; Physics Spectroscopy and Chemometric Analyses ; Horticultural and Viticultural Research ; Fermentation and Sensory Analysis
Authors: Serge Mignani | Xiangyang Shi | Andrii Karpus | Giovanni Lentini | Jean‐Pierre Majoral
Source: Pharmaceutics (review, 2021-09-18)
Field: Materials Science — Polymers and Plastics

Functionalized Dendrimer Platforms as a New Forefront Arsenal Targeting SARS-CoV-2: An Opportunity The novel human coronavirus SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) has caused a pandemic. There are currently several marketed vaccines and many in clinical trials targeting SARS-CoV-2. Another strategy is to repurpose approved drugs to decrease the burden of the COVID-19 (official name for the coronavirus disease) pandemic. as the FDA (U.S. Food and Drug Administration) approved antiviral drugs and anti-inflammatory drugs to arrest the cytokine storm, inducing the production of pro-inflammatory cytokines. Another view to solve these unprecedented challenges is to analyze the diverse nanotechnological approaches which are able to improve the COVID-19 pandemic. In this original minireview, as promising candidates we analyze the opportunity to develop biocompatible dendrimers as drugs themselves or as nanocarriers against COVID-19 disease. From the standpoint of COVID-19, we suggest developing dendrimers as shields against COVID-19 infection based on their capacity to be incorporated in several environments outside the patients and as important means to stop transmission of SARS-CoV-2. Coronavirus disease 2019 (COVID-19) ; Pandemic ; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ; Cytokine storm ; Nanocarriers ; 2019-20 coronavirus outbreak ; Coronavirus ; Medicine ; Face shield ; Drug ; Virology ; Intensive care medicine ; Disease ; Pharmacology ; Infectious disease (medical specialty) ; Outbreak Dendrimers and Hyperbranched Polymers ; RNA Interference and Gene Delivery ; Advanced biosensing and bioanalysis techniques
Authors: Kalani Moore | U. Bangert | Michele Conroy
Source: APL Materials (article, 2021-02-01)
Field: Materials Science — Materials Chemistry

Aberration corrected STEM techniques to investigate polarization in ferroelectric domain walls and vortices Ferroelectric domain wall (DW) based nano-electronics is an emerging new field of research. It is only recently with advancements in electron and atomic force microscopy instrumentation that the complex nature of these 2D entities can be probed. In this Research Update, the advances in aberration corrected scanning transmission electron microscopy applied to ferroelectric topological defects are summarized. We discuss sub-atomic imaging and diffraction techniques used to observe changes in polarization, chemical composition, charge density, and strain at DWs and vortices. We further highlight the current achievements in mapping the 3D nature of ferroelectric polar skyrmions and in situ biasing. This Review will focus on both the fundamental physics of DW and polar vortex formation and their dynamics. Finally, we discuss how electron spectroscopy can be used to relate the quantified structural distortions of polar topological entities to changes in their oxidation state and band structure. Ferroelectricity ; Scanning transmission electron microscopy ; Polarization (electrochemistry) ; Vortex ; Materials science ; Condensed matter physics ; Skyrmion ; Polar ; Biasing ; Domain wall (magnetism) ; Nanotechnology ; Transmission electron microscopy ; Optics ; Physics ; Optoelectronics ; Magnetic field ; Voltage ; Magnetization ; Chemistry Ferroelectric and Piezoelectric Materials ; Electronic and Structural Properties of Oxides ; Acoustic Wave Resonator Technologies
Authors: Pablo Albelda Aparisi | Elena Fortes Sanchez | Laura Contat Rodrigo | Rafael Masot | Nicolás Laguarda-Miró
Source: IEEE Sensors Journal (article, 2021-03-12)
Field: Chemistry — Analytical Chemistry

A Rapid Electrochemical Impedance Spectroscopy and Sensor-Based Method for Monitoring Freeze-Damage in Tangerines This study focuses on the analysis and early detection of freeze-damage in tangerines using a specific double-needle sensor and Electrochemical Impedance Spectroscopy (EIS). Freeze damage may appear in citrus fruits both in the field and in postharvest processes resulting in quality loss and a difficult commercialization of the fruit. EIS has been used to test a set of homogeneous tangerine samples both fresh and later frozen to analyze electrochemical and biological differences. A double-needle electrode associated to a specifically designed electronic device and software has been designed and used to send an AC electric sinusoidal signal 1 V in amplitude and frequency range [100Hz to 1MHz] to the analyzed samples and then receive the electrochemical impedance response. EIS measurements lead to distinct values of both impedance module and phase of fresh and frozen samples over a wide frequency range. Statistical treatment of the received data set by Principal Components Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) shows a clear classification of the samples depending on the experienced freeze phenomenon, with high sensitivity (1.00), specificity (≥ 0.95) and confidence level (95%). Later Artificial Neural Networks (ANN) analysis based on 20-3-1 architecture has allowed to create a mathematical prediction model able to correctly classify 100% of the analyzed samples (CCR =100% for training, validation and test phases, and overall classification), being fast, easy, robust and reliable, and an interesting alternative method to the traditional laboratory analyses. Dielectric spectroscopy ; Electrical impedance ; Materials science ; Partial least squares regression ; Linear discriminant analysis ; Biological system ; Analytical Chemistry (journal) ; Frequency domain ; Electrode ; Computer science ; Artificial intelligence ; Engineering ; Chemistry ; Electrochemistry ; Chromatography ; Machine learning ; Electrical engineering Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Food Drying and Modeling
Authors: Atılım Güneş Baydin | K. Cranmer | Pablo De Castro Manzano | C. Delaere | Denis Derkach | J. Donini | T. Dorigo | A. Giammanco | J. Kieseler | Lukas Layer | Gilles Louppe | F. Ratnikov | Giles C. Strong | M. Tosi | A. Ustyuzhanin | P. Vischia | Hevjin Yarar
Source: Nuclear Physics News (article, 2021-01-02)
Field: Materials Science — Materials Chemistry

Toward Machine Learning Optimization of Experimental Design The effective design of instruments that rely on the interaction of radiation with matter for their operation is a complex task. A full optimization of the many parameters involved may still be sought by leveraging recent progress in computer science. Key to such a goal is the definition of a utility function that models the true goals of the instrument. Such a function must account for the interplay between physical processes that are intrinsically stochastic in nature and the vast space of possible choices for the physical characteristics of the instrument. The construction of a differentiable model of all the ingredients of the information-extraction procedures, including data collection, detector response, pattern recognition, and all existing constraints, then allows the automatic exploration of the vast space of design choices and the search for their best combination.\n\nIn this document we succinctly describe the research program of the MODE Collaboration (an acronym for Machine-learning Optimized Design of Experiments), which aims at developing tools based on deep learning techniques to achieve end-to-end optimization of the design of instruments via a fully differentiable pipeline capable of exploring the Pareto-optimal frontier of the utility function. The goal of MODE is to demonstrate those techniques on small-scale applications such as muon tomography or hadron therapy, to then gradually adapt them to the more ambitious task of exploring innovative solutions to the design of detectors for future particle collider experiments. Computer science ; Task (project management) ; Artificial intelligence ; Human–computer interaction ; Machine learning ; Systems engineering ; Engineering Machine Learning in Materials Science ; Particle Detector Development and Performance ; Fault Detection and Control Systems
Authors: Anbing Zheng | Huihua Yang | Xipeng Pan | Lihui Yin | Yanchun Feng
Source: Sensors (article, 2021-02-05)
Field: Chemistry — Analytical Chemistry

Identification of Multi-Class Drugs Based on Near Infrared Spectroscopy and Bidirectional Generative Adversarial Networks Drug detection and identification technology are of great significance in drug supervision and management. To determine the exact source of drugs, it is often necessary to directly identify multiple varieties of drugs produced by multiple manufacturers. Near-infrared spectroscopy (NIR) combined with chemometrics is generally used in these cases. However, existing NIR classification modeling methods have great limitations in dealing with a large number of categories and spectra, especially under the premise of insufficient samples, unbalanced samples, and sensitive identification error cost. Therefore, this paper proposes a NIR multi-classification modeling method based on a modified Bidirectional Generative Adversarial Networks (Bi-GAN). It makes full utilization of the powerful feature extraction ability and good sample generation quality of Bi-GAN and uses the generated samples with obvious features, an equal number between classes, and a sufficient number within classes to replace the unbalanced and insufficient real samples in the courses of spectral classification. 1721 samples of four kinds of drugs produced by 29 manufacturers were used as experimental materials, and the results demonstrate that this method is superior to other comparative methods in drug NIR classification scenarios, and the optimal accuracy rate is even more than 99% under ideal conditions. Computer science ; Chemometrics ; Identification (biology) ; Artificial intelligence ; Pattern recognition (psychology) ; Class (philosophy) ; Feature (linguistics) ; Machine learning ; Generative grammar ; Premise ; Data mining Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Image Processing Techniques and Applications