jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nicholas Rosa | Marko Ristić | Luke Thorburn | Gabriel Abrahams | B. Marshall | Christopher J. Watkins | A. M. C. Kruger | Alex Khassapov | Janet Newman
Source: Crystals (article, 2020-02-07)
Field: Materials Science — Materials Chemistry

Tools to Ease the Choice and Design of Protein Crystallisation Experiments The process of macromolecular crystallisation almost always begins by setting up crystallisation trials using commercial or other premade screens, followed by cycles of optimisation where the crystallisation cocktails are focused towards a particular small region of chemical space. The screening process is relatively straightforward, but still requires an understanding of the plethora of commercially available screens. Optimisation is complicated by requiring both the design and preparation of the appropriate secondary screens. Software has been developed in the C3 lab to aid the process of choosing initial screens, to analyse the results of the initial trials, and to design and describe how to prepare optimisation screens. Process (computing) ; Computer science ; Crystallization ; Software ; Usability ; Process engineering ; Biochemical engineering ; Nanotechnology ; Software engineering ; Engineering ; Materials science ; Human–computer interaction ; Chemical engineering ; Programming language Enzyme Structure and Function ; Protein Structure and Dynamics ; Advanced Proteomics Techniques and Applications
Authors: Vadym Ptashnyk | Ihor Bordun | Volodymyr Pohrebennyk | Sabina Ziembowicz | Małgorzata Kida | Piotr Koszelnik
Source: Journal of Ecological Engineering (article, 2020-08-26)
Field: Chemistry — Electrochemistry

Aspects of Electrochemically Activated Water Solutions Practical Use This article analyzes the issue of developing ecologically safe technologies for obtaining the electrochemically activated water solutions for ensuring the rational use of water resources and reducing the volume of discharges of harmful substances into the environment. The necessity of controlling the depth of electrochemical activation for obtaining ecologically safe industrial water solutions and reducing the energy consumption of this process was substantiated. The ecological advantages of electrochemically activated solutions application were presented as compared to the chemical solutions in the technologies of activated carbon surfaces modification to increase the supercapacitors electrode specific capacitance, preparation of the gypsum plaster mixing water to improve the physicochemical properties of gypsum stone, as well as efficiency of use of disinfectant based on the electrochemically activated water solutions solutions, approbated in the sugar production industry. It was experimentally confirmed that the depth of electrochemical activation significantly affects the efficiency of practical use of activated water solutions. The proposed theoretical models allow prediction of the activation modes for solving specific problems. Activated sludge ; Environmental science ; Water treatment ; Waste management ; Chemistry ; Environmental engineering ; Engineering ; Sewage treatment Electrochemical Analysis and Applications ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Hyun Kil Shin
Source: RSC Advances (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Electron configuration-based neural network model to predict physicochemical properties of inorganic compounds Registration, evaluation, and authorization of chemicals (REACH), the regulation of chemicals in use, imposes the characterization and report of the physicochemical properties of compounds. To cope with the financial burden of the experiments, the use of computational models is permitted for prediction of properties. Although a number of physicochemical property prediction models have been developed, their applicability domain is limited to organic molecules since most available data are concerned with organic molecules, and most of the molecular descriptors are restricted to organic molecule calculations. Prediction models developed for inorganic compounds were intended to predict endpoints relevant to novel material design. Therefore, no models were available for predicting endpoints of inorganic compounds that are significant to regulatory perspectives. In this study, boiling point, water solubility, melting point, and pyrolysis point prediction models were developed for inorganic compounds based on their composition. The electron configuration of each element in the molecule was used as a descriptor in this study. The dataset covered a wide range of endpoints and diverse elements in their structure. The performance of the models was measured using <i>R</i> <sup>2</sup>, mean absolute error, and Spearman's correlation coefficient, and indicated good prediction accuracy of continuous endpoints and prioritization of inorganic compounds. Artificial neural network ; Chemistry ; Biological system ; Chemical engineering ; Nanotechnology ; Materials science ; Computer science ; Engineering ; Artificial intelligence ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Amir Reza Sadrolhosseini | Ganesan Krishnan | Suhaidi Shafie | Suraya Abdul Rashid | Sulaiman Wadi Harun
Source: Molecules (article, 2020-12-09)
Field: Materials Science — Materials Chemistry

Optical and Photoacoustic Properties of Laser-Ablated Silver Nanoparticles in a Carbon Dots Solution This study used the carbon dots solution for the laser ablation technique to fabricate silver nanoparticles. The ablation time range was from 5 min to 20 min. Analytical methods, including Fourier transform infrared spectroscopy (FTIR), UV-visible spectroscopy, transmission electron microscopy, and Raman spectroscopy were used to categorize the prepared samples. The UV-visible and z-scan techniques provided optical parameters such as linear and nonlinear refractive indices in the range of 1.56759 to 1.81288 and 7.3769 × 10<sup>-10</sup> cm<sup>2</sup> W<sup>-1</sup> to 9.5269 × 10<sup>-10</sup> cm<sup>2</sup> W<sup>-1</sup> and the nonlinear susceptibility was measured in the range of 5.46 × 10<sup>-8</sup> to 6.97 × 10<sup>-8</sup> esu. The thermal effusivity of prepared samples, which were measured using the photoacoustic technique, were in the range of 0.0941 W s<sup>1/2</sup> cm<sup>-2</sup> K<sup>-1</sup> to 0.8491 W s<sup>1/2</sup> cm<sup>-2</sup> K<sup>-1</sup>. The interaction of the prepared sample with fluoride was investigated using a Raman spectrometer. Consequently, the intensity of the Raman signal decreased with the increasing concentration of fluoride, and the detection limit is about 0.1 ppm. Raman spectroscopy ; Analytical Chemistry (journal) ; Fourier transform infrared spectroscopy ; Materials science ; Spectroscopy ; Silver nanoparticle ; Laser ablation ; Photoacoustic spectroscopy ; Detection limit ; Nanoparticle ; Laser ; Fourier transform spectroscopy ; Optics ; Chemistry ; Nanotechnology ; Chromatography Carbon and Quantum Dots Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Laser-Ablation Synthesis of Nanoparticles
Authors: Vinayak Agarwal | Shriya Agarwal | Ramneek Kaur | Pranav Pancham | Harleen Kaur | Siddhi Bhardwaj | Manisha Singh
Source: Journal of Biomaterials and Nanobiotechnology (article, 2020-01-01)
Field: Materials Science — Biomaterials

&amp;lt;i&amp;gt;In-Silico&amp;lt;/i&amp;gt; Validation and Development of Chlorogenic Acid (CGA) Loaded Polymeric Nanoparticle for Targeting Neurodegenerative Disorders Background: Recent decades witnessed a significant growth in terms of phytocompounds based therapeutics, extensively explored for almost all types of existing disorders. They have also been widely investigated in Neurodegenerative disorders (NDDs) and Chlorogenic acid (CGA), a polyphenolic compound having potential anti-inflammatory and anti-oxidative properties, emerged as a promising compound in ameliorating NDDs. Owing to its poor stability, bioavailability and release kinetics, CGA needed a suitable nanocarrier based pharmaceutical design for targeting NDDs. Objective: The current study is aimed at the in-silico validation of CGA as an effective therapeutic agent targeting various NDDs followed by the fabrication of polymeric nanoparticles-based carrier system to overcome its pharmacological limitations and improve its stability. Methods: A successful in-silico validation using molecular docking techniques along with synthesis of CGA loaded polymeric nanoparticles (CGA-NPs) by ionic gelation method was performed. The statistical optimisation of the developed CGA-NPs was done by Box Behnken method and then the optimized formulation of CGA-NPs was characterised using particle size analysis (PSA), Transmission electron microscopy (TEM), Fourier Transform Infrared spectroscopy (FTIR) along with in-vitro release kinetics analysis. Results & Conclusion: The results attained exhibited average particle size of 101.9 ± 1.5 nm, Polydispersibility (PDI) score of 0.065 and a ZP of −17.4 mV. On a similar note, TEM results showed a size range of CGA-NPs between 90 - 110 nm with a spherical shape of NPs. Also, the data from in-vitro release kinetics showed a sustained release of CGA from the NPs following the first-order kinetics suggesting the appropriate designing of nanoformulation. Nanocarriers ; In silico ; Chlorogenic acid ; Kinetics ; Nanoparticle ; Fourier transform infrared spectroscopy ; Bioavailability ; Materials science ; Nanotechnology ; Chemistry ; Pharmacology ; Biophysics ; Combinatorial chemistry ; Biochemistry ; Chemical engineering ; Chromatography ; Medicine ; Biology Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems ; Curcumin's Biomedical Applications
Authors: Mohamed Berber | N. Bouzouira | H. Abid | A. Boudali | H. Moujri
Source: Revista Mexicana de Física (article, 2020-09-01)
Field: Chemistry — Inorganic Chemistry

Spinelectronic investigation of the quaternary vanadium fluoride Rb2NaVF6: Ab-initio method In this study, we have investigated the structural, electronic, and magnetic properties of the Rb2NaVF6 compound. We have performed our calculations by the use of first-principle methods based on spin-polarized density functional theory, where the electronic exchange-correlation potential is treated by the generalized gradient approximation GGA- PBEsol coupled with the improved TB-mBJ approach. The calculated structural parameters of Rb2NaVF6 are in good agreement with the available experimental data. Rb2NaVF6 exhibits a half-metallic ferromagnetic feature with a spin polarization of 100 % at the Fermi level and a direct large half-metallic gap of 3.582 eV. The total magnetic moments are 2 μB. This material is half-metallic ferromagnets, and it can be potential candidates for spintronics applications at a higher temperature. Spintronics ; Condensed matter physics ; Ferromagnetism ; Materials science ; Density functional theory ; Spin polarization ; Ab initio ; Magnetic moment ; Ab initio quantum chemistry methods ; Fermi level ; Electronic structure ; Vanadium ; Polarization (electrochemistry) ; Computational chemistry ; Physics ; Chemistry ; Physical chemistry ; Quantum mechanics ; Molecule ; Electron Inorganic Fluorides and Related Compounds ; Superconducting Materials and Applications ; Physics of Superconductivity and Magnetism
Authors: Belén Hernández | Fernando Pflüger | Sergei G. Kruglik | Mahmoud Ghomi
Source: Journal of Molecular Graphics and Modelling (article, 2020-10-30)
Field: Materials Science — Biomaterials

Multiconformational analysis of tripeptides upon consideration of implicit and explicit hydration effects Intramolecular force ; Polyproline helix ; Tripeptide ; Chemistry ; Intermolecular force ; Molecular dynamics ; Molecule ; Computational chemistry ; Density functional theory ; Helix (gastropod) ; Crystallography ; Chemical physics ; Stereochemistry ; Peptide ; Organic chemistry Supramolecular Self-Assembly in Materials ; Chemical Synthesis and Analysis ; DNA and Nucleic Acid Chemistry
Authors: Gangshan Wu | Qiyou Jiang | Yuhao Bai | Chongfeng Tian | Wei Pan | Xinxin Jin | Baohua Zhang
Source: IEEE Access (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

Nitrogen Status Assessment for Multiple Cultivars of Strawberries Using Portable FT-NIR Spectrometers Combined With Cultivar Recognition and Multivariate Analysis The nondestructive assessment of nitrogen status in strawberry was performed to analyze its growth and provide guidance for precise management of N fertilizer using Fourier transform near-infrared reflectance (FT-NIR) spectroscopy with leaf spectral reflectance. The leaf soil plant analysis development (SPAD) value is considered as an indicator that indirectly reflects the nitrogen status in strawberry. However, the variation of cultivars leads to differences in the cell structure and light scattering and/or reflection effects of strawberry leaves during strawberry N status assessment, which means, leaf SPAD threshold value and the specific N demand will vary with different cultivars. As a result, accurate measurement of SPAD values and N status in strawberries with multiple cultivars is still challenging. In this study, the individual-cultivar, hybrid-cultivar, and multi-cultivar models were developed for the determination of SPAD values, and the performance of the models in lessening the impact of cultivar variation was studied and compared. The individual-cultivar model was constructed on the basis of a single cultivar of strawberry leaf. The hybrid-cultivar model was developed by merging the spectrum reflectance data and SPAD values of all studied leaf samples, and a multi-cultivar model was built in combination with cultivar identification, individual-cultivar models, and model search strategy. The results indicated that the multi-cultivar model was superior to the other two models for SPAD value estimation of strawberry leaves from different cultivars, with the overall <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Rp</i> and RMSEP values of 0.966 and 0.468, respectively. We demonstrate that the leaf N content of strawberry is profoundly affected by cultivar variation, and establishing a multi-cultivar model is useful in monitoring the nitrogen status and guiding N fertilization recommendations for different strawberry cultivars. Cultivar ; Mathematics ; Horticulture ; Agronomy ; Biology Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Leaf Properties and Growth Measurement
Authors: Rebecca L. Pinals | Darwin Yang | Daniel J. Rosenberg | Tanya Chaudhary | Andrew R. Crothers | Anthony T. Iavarone | Michal Hammel | Markita P. Landry
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-01-14)
Field: Chemistry — Spectroscopy

Protein Corona Composition and Dynamics on Carbon Nanotubes in Blood Plasma and Cerebrospinal Fluid Abstract When a nanoparticle enters a biological environment, the surface is rapidly coated with proteins to form a “protein corona”. Presence of the protein corona surrounding the nanoparticle has significant implications for applying nanotechnologies within biological systems, affecting outcomes such as biodistribution and toxicity. Herein, we measure protein corona formation on single-stranded DNA wrapped single-walled carbon nanotubes (ssDNA-SWCNTs), a high-aspect ratio nanoparticle ideal for sensing and delivery applications, and polystyrene nanoparticles, a model nanoparticle system. The protein corona of each nanoparticle is studied in human blood plasma and cerebrospinal fluid. We characterize corona composition by proteomic mass spectrometry to determine abundant and differentially enriched vs. depleted corona proteins. High-binding corona proteins on ssDNA-SWCNTs include proteins involved in lipid binding and transport (clusterin and apolipoprotein A-I), complement activation (complement C3), and blood coagulation (fibrinogen). Of note, albumin is the most common blood protein (55% w/v), yet exhibits low-binding affinity towards ssDNA-SWCNTs, displaying 1300-fold lower bound concentration relative to native plasma. We investigate the role of electrostatic and entropic interactions driving selective protein corona formation, and find that hydrophobic interactions drive inner corona formation, while shielding of electrostatic interactions allows for outer corona formation. Lastly, we study real-time binding of proteins on ssDNA-SWCNTs and find relative agreement between proteins that are enriched and bind strongly, such as fibrinogen, and proteins that are depleted and bind marginally, such as albumin. Interestingly, certain proteins express contrary behavior in single-protein experiments than within the whole biofluid, highlighting the importance of cooperative mechanisms driving selective corona adsorption on the SWCNT surface. Knowledge of the protein corona composition, dynamics, and structure informs translation of engineered nanoparticles from in vitro design to effective in vivo application. Biophysics ; Chemistry ; Blood proteins ; Carbon nanotube ; Clusterin ; Nanoparticle ; Corona (planetary geology) ; Albumin ; Nanotoxicology ; Fibrinogen ; Plasma protein binding ; Nanotechnology ; Biochemistry ; Materials science ; Biology Advanced Proteomics Techniques and Applications ; Hemoglobin structure and function ; Nanoparticle-Based Drug Delivery
Authors: Qunxiong Zhu | Lu Liu
Source: Processes (article, 2020-06-25)
Field: Chemistry — Physical and Theoretical Chemistry

Ranking Factors of Infant Formula Milk Powder Using Improved Entropy Weight Based on HDT Method and Its Application of Food Safety Food safety is about everyone’s health. Through risk assessment and early warning of food safety, food-related safety issues can be identified as early as possible and take timely precautions. However, the detection data of food safety are complex and non-linear, so it is necessary to find the relationship and hierarchical representation of factors affecting food safety. This paper presents an improved entropy weight based on Hasse diagram technology (HDT) method to analyze the influencing factors of food safety. The entropy weight method was used to calculate the weight of each factor index, and the relationship matrix was obtained. Then, the data of infant milk powder in China were analyzed hierarchically by the HDT method. Thus, we can obtain the multi-level structure that affects food safety. It provides an effective basis for early warning of food safety, can help government regulators to strengthen management, and urge enterprises to produce food safely. Food safety ; Hasse diagram ; Warning system ; Food safety risk analysis ; Ranking (information retrieval) ; Infant formula ; Entropy (arrow of time) ; Mathematics ; Computer science ; Environmental health ; Risk analysis (engineering) ; Business ; Food science ; Data mining ; Medicine ; Chemistry ; Information retrieval History and advancements in chemistry ; Computational Drug Discovery Methods
Authors: Martin Amezcua | David L. Mobley
Source: ChemRxiv (preprint, 2020-08-07)
Field: Materials Science — Materials Chemistry

SAMPL7 Challenge Overview: Assessing the Reliability of Polarizable and Non-Polarizable Methods for Host-Guest Binding Free Energy Calculations The SAMPL challenges focus on testing and driving progress of computational methods to help guide pharmaceutical drug discovery. However, assessment of methods for predicting binding affinities is often hampered by computational challenges such as conformational sampling, protonation state uncertainties, variation in test sets selected, and even lack of high quality experimental data. SAMPL blind challenges have thus frequently included a component focusing on host-guest binding, which removes some of these challenges while still focusing on molecular recognition. Here, we report on the results of the SAMPL7 blind prediction challenge for host-guest affinity prediction. In this study, we focused on three different host-guest categories -- a familiar deep cavity cavitand series which has been featured in several prior challenges (where we examine binding of a series of guests to two hosts), a new series of cyclodextrin derivatives which are monofunctionalized around the rim to add amino acid-like functionality (where we examine binding of a two guests to a series of hosts), and binding of a series of guests to a new acyclic TrimerTrip host which is related to previous cucurbituril hosts. Many predictions used methods based on molecular simulations, and overall success was mixed, though several methods stood out. As in SAMPL6, we find that one strategy for achieving reasonable accuracy here was to make empirical corrections to binding predictions based on previous data for host categories which have been studied well before, though this can be of limited value when new systems are included. Additionally, we found that methods using the AMOEBA polarizable force field had considerable success for the two host categories in which they participated. The new TrimerTrip system was also found to introduce some sampling problems, because multiple conformations may be relevant to binding and interconvert only slowly. Overall, results in this challenge tentatively suggest that further investigation of polarizable force fields for these challenges may be warranted. Computer science ; Host (biology) ; Cavitand ; Machine learning ; Artificial intelligence ; Computational chemistry ; Computational biology ; Chemistry ; Biology ; Supramolecular chemistry ; Molecule Machine Learning in Materials Science ; Chemical Synthesis and Analysis ; Protein Structure and Dynamics
Authors: Yusuke Kita | Junji Sugiyama
Source: Holzforschung (article, 2020-11-13)
Field: Chemistry — Organic Chemistry

Wood identification of two anatomically similar Cupressaceae species based on two-dimensional microfibril angle mapping Abstract Identifying two anatomically similar species of Cupressaceae, Chamaecyparis obtusa and Thujopsis spp., is important to better understand the culture of wood use in Japan. However, the conventional method, which involves observing their cross-field pitting, cannot identify them in many cases. This study solves the above problem by introducing an anatomical criterion based on the micro fibril angle (MFA). MFA values were obtained through two-dimensional MFA images using the uniaxial optical anisotropy of cellulose microfibrils. A combination of the preprocessed MFA images and a convolutional neural network (CNN) yielded an accuracy nearly of 90% in classifying these species in cases of present and old wood specimens. Our feature extraction and classification techniques provide a new way for describing the anatomical features of wood and identifying featureless softwoods. Using the model interpretation-related methodologies of the CNN, distinct features of the two wood species were partly explained by MFA anisotropy in the S 2 wall induced by the existence of pits. Cupressaceae ; Microfibril ; Chamaecyparis ; Softwood ; Anisotropy ; Lignin ; Convolutional neural network ; Preprocessor ; Botany ; Cellulose ; Pattern recognition (psychology) ; Materials science ; Biology ; Artificial intelligence ; Computer science ; Optics ; Physics Wood and Agarwood Research ; Remote Sensing and LiDAR Applications ; Industrial Vision Systems and Defect Detection