jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shiyue Yang | Graeme M. Day
Source: ChemRxiv (preprint, 2021-11-17)
Field: Chemistry — Physical and Theoretical Chemistry

Global analysis of crystal energy landscapes: applying the threshold algorithm to molecular crystal structures We describe the implementation of the Monte Carlo threshold algorithm for molecular crystals as a method to provide an estimate of the energy barriers separating crystal structures. By sampling the local energy minima accessible from multiple starting structures, the simulations yield a global picture of the crystal energy landscapes. This provides valuable information on the depth of the energy minima associated with crystal structures and adds to the information available from crystal structure prediction methods that are used for anticipating polymorphism. We present results from applying the threshold algorithm to four polymorphic organic molecular crystals, examine the influence of applying space group symmetry constraints during the simulations, and discuss the relationship between the structure of the energy landscape and the intermolecular interactions present in the crystals. Maxima and minima ; Energy landscape ; Crystal structure prediction ; Crystal (programming language) ; Monte Carlo method ; Energy (signal processing) ; Crystal structure ; Algorithm ; Statistical physics ; Intermolecular force ; Polymorphism (computer science) ; Space (punctuation) ; Chemical physics ; Computer science ; Materials science ; Physics ; Crystallography ; Molecule ; Chemistry ; Mathematics ; Statistics ; Quantum mechanics ; Thermodynamics Crystallography and molecular interactions ; Crystallization and Solubility Studies ; Machine Learning in Materials Science
Authors: Thanh Thuy Tran | Van‐Dat Doan | Xuan Hung Nguyen | Uyên Cường Trương
Source: Journal of Science and Technology - IUH (article, 2021-08-25)
Field: Chemistry — Analytical Chemistry

SOLID PHASE EXTRACTION BASED ON Mn3O4-TIO2 NANOCOMPOSITE FOR THE DETERMINATION OF MOLYBDENUM IN WATER SAMPLES The Mn3O4-TiO2 nanocomposite was applied as a new solid phase adsorbent for preconcentration of Molybdenum before determination by an UV-Vis spectrophotometer. The solid-phase extraction conditions as pH, the mass of sorbent, contact time, stirring speed, and the elution condition were investigated. Under optimized conditions, the adsorption capacity of adsorbent was 20.69 mg/g, and the elution condition was 0.2M NaOH solution. Molybdenum ions were determined based on its catalytic effect on the oxidation of 1-amino-2- naphthol-4-sulfonic acid (ANSA) with H2O2. The linear calibration graph was in the range of 0.2–2.5 µg/L (r2 = 0.995) with a detection limit of 0.0502 µg/L. The relative standard deviation for five measurements of 2 µg/L of Mo (VI) was 3,37%. The method was applied to the determination of Molybdenum in water samples. Molybdenum ; Sorbent ; Solid phase extraction ; Adsorption ; Detection limit ; Elution ; Nanocomposite ; Extraction (chemistry) ; Calibration curve ; Materials science ; Sulfonic acid ; Molybdenum disulfide ; Chemistry ; Chromatography ; Nuclear chemistry ; Inorganic chemistry ; Organic chemistry ; Metallurgy Analytical chemistry methods development ; Electrochemical Analysis and Applications ; Water Quality Monitoring and Analysis
Authors: Vahid Nozari | Courtney Calahoo | Joshua M. Tuffnell | David A. Keen | Thomas D. Bennett | Lothar Wondraczek
Source: ChemRxiv (preprint, 2021-01-28)
Field: Chemistry — Inorganic Chemistry

Ionic Liquid Facilitated Melting of the Metal-Organic Framework ZIF-8 &lt;div&gt;Hybrid glasses from melt-quenched metal-organic frameworks (MOFs) have been emerging as a new class of materials, which combine the functional properties of crystalline MOFs with the processability of glasses. However, only a handful of the vast variety of crystalline MOFs have been identified as being meltable. Porosity and metal-linker interaction strength have both been identified as crucial parameters in the trade-off between thermal decomposition of the organic linker and, more desirably, melting. For example, the inability of the prototypical zeolitic imidazolate framework (ZIF) ZIF-8 to melt, is ascribed to the instability of the organic linker upon dissociation from the metal center. Here, we demonstrate that the incorporation of an ionic liquid (IL) into the porous interior of ZIF-8 provides a means to reduce its melting temperature to below its thermal decomposition temperature (Tm &lt; Td). Experimental evidence shows that the Tm of ZIF-8 obtained by IL infiltration is around 381 °C, and that the glass forming ability (Tg/Tm) of such melts is above 0.9, i.e. higher than those previously reported for other meltable MOFs. Our structural studies show that the prevention of decomposition, and successful melting, is due to the IL interactions stabilizing the rapidly dissociating ZIF-8 linkers upon heating. This understanding may act as a general guide for extending the range of meltable MOF materials and, hence, the chemical and structural variety of MOF-derived glasses.&lt;/div&gt; Zeolitic imidazolate framework ; Materials science ; Porosity ; Metal-organic framework ; Ionic liquid ; Thermal decomposition ; Chemical engineering ; Ionic bonding ; Imidazolate ; Nanotechnology ; Chemistry ; Composite material ; Ion ; Organic chemistry ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Pigment Synthesis and Properties ; Thermal and Kinetic Analysis
Authors: Hongwei Chen | Changchang Lv | L. Jay Guo | Ming Ma | Xiangfeng Li | Zhengyi Lan | Jun Huo | Hao Dong | Xiangdong Zhu | Qiang Zhu | Yuming Gu | Ziteng Liu | Jianjun Liu | Hangrong Chen | Xuefeng Guo | Jing Ma
Source: ChemRxiv (preprint, 2021-11-23)
Field: Materials Science — Biomaterials

Surface Stability and Morphology of Calcium Phosphate Tuned by pH Values and Lactic Acid Additives: Theoretical and Experimental Study The ubiquitous mineralization of calcium phosphate (CaP) facilitates biological organisms to produce hierarchically structured minerals. The coordination number and strength of Ca2+ ions with phosphate species, oxygen-containing additives, and solvent molecules played a crucial role in tuning nucleation processes and surface stability of CaP under the simulated body fluid (SBF) or aqueous solutions upon the addition of oligomeric lactic acid (LACn, n=1, 8) and changing pH values. As revealed by ab initio molecular dynamics (AIMD), density functional theory (DFT), and molecular dynamics (MD) simulations as well as high-throughput experimentation (HTE), the binding of LAC molecules with Ca2+ ions and phosphate species could stabilize both pre-nucleation clusters and brushite (DCPD, CaHPO4·2H2O) surface through intermolecular electrostatic and hydrogen bonding interactions. When the concentration of Ca2+ ions ([Ca2+]) is very low, the amount of the formed precipitation decreased with the addition of LAC based on UV-Vis spectroscopic analysis due to the reduced chance for the LAC capped Ca2+ ions to coordinate with phosphates and the increased solubility in acid solution. With the increasing [Ca2+] concentration, the kinetically stable DCPD precipitation was obtained with high Ca2+ coordination number and low surface energy. Morphologies of DCPD precipitation are in plate, needle, or rod, depending on the initial pH values that tuned by adding NH3·H2O, HCl, or CH3COOH. The prepared samples at pH ≈ 7.4 with different Ca/P ratios exhibited negative zeta potential values, which were correlated with the surface electrostatic potential distributions and potential biological applications. Chemistry ; Nucleation ; Aqueous solution ; Precipitation ; Molecule ; Zeta potential ; Molecular dynamics ; Phosphate ; Brushite ; Inorganic chemistry ; Density functional theory ; Solubility ; Ion ; Calcium ; Physical chemistry ; Computational chemistry ; Materials science ; Organic chemistry ; Nanotechnology ; Nanoparticle Calcium Carbonate Crystallization and Inhibition
Authors: Debanshu Banerjee | Taylor D. Sparks
Source: ChemRxiv (preprint, 2021-08-10)
Field: Materials Science — Materials Chemistry

Comparing Transfer Learning to Feature Optimization in Microstructure Classification Human analysis of research data is slow and inefficient. In recent years machine learning tools have advanced our capability to perform tasks normally carried out by humans, such as image segmentation and classification. In this work, we seek to further improve binary classification models for high throughput identification of different microstructural morphologies. We utilize a dataset with limited observations (133 dendritic structures, 444 non-dendritic) and employ data augmentation via rotation and translation to enhance the dataset six-fold. Then, transfer learning is carried out using pre-trained networks VGG16, InceptionV3, and Xception achieving only moderate F1 scores (0.801 to 0.822). We hypothesize that feature engineering could yield better results than transfer learning alone. To test this, we employ a new nature-inspired feature optimization algorithm, the Binary Red Deer Algorithm (BRDA), to carry out binary classification and observe F1 scores above 0.93. Transfer of learning ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Feature (linguistics) ; Binary classification ; Binary number ; Segmentation ; Feature extraction ; Machine learning ; Support vector machine ; Mathematics Machine Learning in Materials Science ; Mineral Processing and Grinding ; X-ray Diffraction in Crystallography
Authors: Mohammad R. Momeni | Dil K. Limbu | Sara Abdelhamid | Shaina Pearson | Farnaz A. Shakib
Source: ChemRxiv (preprint, 2021-12-07)
Field: Chemistry — Inorganic Chemistry

Atomistic molecular dynamics insights on water local structure and dynamics on different surfaces of zeolitic-imidazolate frameworks Most of chemistry in nanoporous materials with small pore sizes and windows is known to occur on the surface which is in immediate contact with substrate/solvent, rather than inside pores and channels. Here, we report the results of our comprehensive atomistic molecular dynamics simulations on deciphering the intermolecular hydrogen bond network of water on outer surface of a nanoparticle model of ZIF-8 vs. inner surfaces of its pristine crystalline bulk model. Using a finite ~5.1 nm nanoparticle model with edges containing under--coordinated Zn2+ metal sites we show that water exposed to the surface of the nanoparticle exhibits both interfacial and bulk-like characters. Furthermore, we illustrate that as water content increases larger droplets are formed with water molecules starting to diffuse into the nanopores. While the confined water in the crystalline bulk simulations is pushed to the vacant pores due to hydrophobic inner surfaces, the outer surface water molecules form chemical bonds with under--coordinated Zn2+ metal sites which act as nucleation sites for the water droplets to form and hence making the surface hydrophilic. By adapting a similar mechanism to the dangling linker defect formation mechanism, we probe the tendency of the outer surface of ZIF-8 nanoparticles to water attack and hydrolysis. Results presented in this work are useful in designing more robust materials for applications in humid environments. Chemical physics ; Nucleation ; Molecular dynamics ; Nanoporous ; Nanoparticle ; Materials science ; Hydrogen bond ; Molecule ; Nanotechnology ; Intermolecular force ; Dangling bond ; Chemical engineering ; Adsorption ; Chemistry ; Computational chemistry ; Physical chemistry ; Silicon ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Enhanced Oil Recovery Techniques ; Mesoporous Materials and Catalysis
Authors: Debanshu Banerjee | Taylor D. Sparks
Source:  (preprint, 2021-08-23)
Field: Materials Science — Materials Chemistry

Comparing Transfer Learning to Feature Optimization in Microstructure Classification Human analysis of research data is slow and inefficient. In recent years machine learning tools have advanced our capability to perform tasks normally carried out by humans, such as image segmentation and classification. In this work, we seek to further improve binary classification models for high throughput identification of different microstructural morphologies. We utilize a dataset with limited observations (133 dendritic structures, 444 non-dendritic) and employ data augmentation via rotation and translation to enhance the dataset six-fold. Then, transfer learning is carried out using pre-trained networks VGG16, InceptionV3, and Xception achieving only moderate F1 scores (0.801 to 0.822). We hypothesize that feature engineering could yield better results than transfer learning alone. To test this, we employ a new nature-inspired feature optimization algorithm, the Binary Red Deer Algorithm (BRDA), to carry out binary classification and observe F1 scores above 0.93. Transfer of learning ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Binary classification ; Feature (linguistics) ; Binary number ; Segmentation ; Machine learning ; Support vector machine ; Mathematics Machine Learning in Materials Science ; Mineral Processing and Grinding ; X-ray Diffraction in Crystallography
Authors: Laurien I. Roest | Sabrya E. van Heijst | Louis Maduro | Juan Rojo | Sonia Conesa‐Boj
Source: Ultramicroscopy (preprint, 2021-01-11)
Field: Materials Science — Materials Chemistry

Charting the low-loss region in electron energy loss spectroscopy with machine learning Exploiting the information provided by electron energy-loss spectroscopy (EELS) requires reliable access to the low-loss region where the zero-loss peak (ZLP) often overwhelms the contributions associated to inelastic scatterings off the specimen. Here we deploy machine learning techniques developed in particle physics to realise a model-independent, multidimensional determination of the ZLP with a faithful uncertainty estimate. This novel method is then applied to subtract the ZLP for EEL spectra acquired in flower-like WS<sub>2</sub> nanostructures characterised by a 2H/3R mixed polytypism. From the resulting subtracted spectra we determine the nature and value of the bandgap of polytypic WS<sub>2</sub>, finding E<sub>BG</sub>=1.6<sub>-0.2</sub><sup>+0.3</sup>eV with a clear preference for an indirect bandgap. Further, we demonstrate how this method enables us to robustly identify excitonic transitions down to very small energy losses. Our approach has been implemented and made available in an open source Python package dubbed EELSfitter. Python (programming language) ; Electron energy loss spectroscopy ; Spectroscopy ; Electron ; Band gap ; Spectral line ; Computational physics ; Physics ; Computer science ; Atomic physics ; Materials science ; Condensed matter physics ; Nuclear physics ; Quantum mechanics Machine Learning in Materials Science ; 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films
Authors: Stavros K. Kariofillis | Shutian Jiang | A. Zuranski | Shivaani S. Gandhi | Jesus Martinez Alvarado | Abigail G. Doyle
Source: ChemRxiv (preprint, 2021-06-24)
Field: Chemistry — Organic Chemistry

Using Data Science to Guide Aryl Bromide Substrate Scope Analysis in a Ni/Photoredox-Catalyzed Cross-Coupling with Acetals as Alcohol-Derived Radical Sources Ni/photoredox catalysis has emerged as a powerful platform for C(sp2)–C(sp3) bond formation. In principle, these reactions enable access to the same product scaffolds, but it can be hard to discern which method to employ because non-standardized sets of aryl bromides are used in scope evaluation. Herein we report a Ni/photoredox-catalyzed alkylation of aryl halides where benzaldehyde di(alkyl) acetals serve as alcohol-derived radical sources. We describe the integration of data science techniques, including DFT featurization, dimensionality reduction, and hierarchical clustering, to delineate a diverse and succinct collection of aryl bromides that is representative of the chemical space of the substrate class. By superimposing the scope examples from published Ni/photoredox methods on this chemical space, we identify areas of sparse coverage and high/low yields, enabling comparisons between prior art and this method. We demonstrate that the systematically-selected scope of aryl bromides can be used to quantify population-wide reactivity trends with supervised ML. Photoredox catalysis ; Aryl ; Scope (computer science) ; Combinatorial chemistry ; Substrate (aquarium) ; Alkylation ; Population ; Chemistry ; Catalysis ; Benzaldehyde ; Computer science ; Alcohol ; Coupling reaction ; Alkyl ; Organic chemistry Radical Photochemical Reactions ; Catalytic C–H Functionalization Methods ; Advanced Photocatalysis Techniques
Authors: Jingjing Xia | Yué Huang | Qianqian Li | Yanmei Xiong | Shungeng Min
Source: Research Square (preprint, 2021-03-30)
Field: Chemistry — Analytical Chemistry

Convolutional neural network for identification of plastic solid waste using near infrared spectroscopy Abstract Plastic waste is a relevant challenge for waste management sector and further technological means has to be urgently researched. Near infrared spectroscopy (NIR), as a non-destructive, cost-effective and mature technology, is currently operational in some waste-sorting facilities. However, NIR remains challenging to discriminate different black plastics because black targets have low reflectance in the its spectral region. Therefore, this study aimed to address the problem in black plastic classification. Seven kinds of plastics: High Impact Polystyrene (HIPS), Acrylonitrile Butadiene Styrene (ABS), High Density Polyethylene (HDPE), Polyethylene terephthalate (PET), Polyamide 66 (PA66), Polycarbonate (PC) and Polypropylene (PP), were prepared for analysis, in which black plastic accounted for half of the total samples (84/159). Some methods such as partial least squares discrimination analysis (PLS-DA), soft independent modelling of class analogy (SIMCA), linear discriminant analysis (LDA) and convolutional neural network (CNN) were used to classify the plastics. Coupling with proper preprocessing methods, PLS-DA and LDA could provide an accuracy of 57.00%, and SIMCA of 69.98%. Meanwhile, the NIR spectra fed into the CNN model as one-dimensional data, the accuracy of CNN model could reach 98.00%. Compared to the SIMCA, PLS-DA and LDA, the accuracy of CNN provided an average improvement of 28-41%. Results demonstrated that NIR spectra combined with CNN gave an excellent accuracy which was potential to solve the bottleneck problem of black plastic discrimination. Linear discriminant analysis ; Artificial intelligence ; Pattern recognition (psychology) ; Convolutional neural network ; Materials science ; High-density polyethylene ; Partial least squares regression ; Acrylonitrile butadiene styrene ; Polypropylene ; Microplastics ; Near-infrared spectroscopy ; Computer science ; Polyethylene ; Composite material ; Machine learning ; Chemistry ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Microplastics and Plastic Pollution
Authors: Kamila Janzakova | Mahdi Ghazal | Ankush Kumar | Yannick Coffinier | Sébastien Pecqueur | Fabien Alibart
Source: Advanced Science (preprint, 2021-10-29)
Field: Materials Science — Polymers and Plastics

Dendritic Organic Electrochemical Transistors Grown by Electropolymerization for 3D Neuromorphic Engineering One of the major limitations of standard top-down technologies used in today's neuromorphic engineering is their inability to map the 3D nature of biological brains. Here, it is shown how bipolar electropolymerization can be used to engineer 3D networks of PEDOT:PSS dendritic fibers. By controlling the growth conditions of the electropolymerized material, it is investigated how dendritic fibers can reproduce structural plasticity by creating structures of controllable shape. Gradual topologies evolution is demonstrated in a multielectrode configuration. A detailed electrical characterization of the PEDOT:PSS dendrites is conducted through DC and impedance spectroscopy measurements and it is shown how organic electrochemical transistors (OECT) can be realized with these structures. These measurements reveal that quasi-static and transient response of OECTs can be adjusted by controlling dendrites' morphologies. The unique properties of organic dendrites are used to demonstrate short-term, long-term, and structural plasticity, which are essential features required for future neuromorphic hardware development. Neuromorphic engineering ; PEDOT:PSS ; Materials science ; Dielectric spectroscopy ; Transistor ; Nanotechnology ; Electrode ; Electrochemistry ; Optoelectronics ; Computer science ; Voltage ; Artificial neural network ; Electrical engineering ; Artificial intelligence ; Chemistry ; Engineering ; Layer (electronics) Conducting polymers and applications ; Advanced Memory and Neural Computing ; Advanced Sensor and Energy Harvesting Materials
Authors: Pier Alexandre Champagne
Source:  (preprint, 2021-09-01)
Field: Chemistry — Organic Chemistry

Identifying the true origins of selectivity in chiral phosphoric acid catalyzed N-acyl-azetidine desymmetrizations The first catalytic intermolecular desymmetrization of azetidines was reported by Sun and coworkers in 2015 using a BINOL-derived phosphoric acid catalyst (J. Am. Chem. Soc. 2015, 137, 5895-5898). To uncover the mechanism of the reaction and the origins of the high enantioselectivity, Density Functional Theory (DFT) calculations were performed at the B97D3 / 6-311+G(2d,2p) / SMD(toluene) // B97D3 / 6-31G(d,p) / CPCM(toluene) level of theory. Comparison of four possible activation modes confirms that this reaction proceeds through the bifunctional activation of the azetidine nitrogen and the thione tautomer of the 2-mercaptobenzothiazole nucleophile. Upon thorough conformational sampling of the enantiodetermining transition structures (TSs), a free energy difference of 2.0 kcal mol-1 is obtained, accurately reproducing the experimentally measured 88% e.e. at 80 °C. This energy difference is due to both decreased distortion and increased non-covalent interactions in the pro-(S) TS. To uncover the true origins of selectivity, the TSs optimized with the full catalyst were compared to those optimized with a model catalyst through steric maps. It is found that the arrangements displayed by the substrates are controlled by strict primary orbital interaction requirements at the transition complex, and their ability to fit into the catalyst pocket drives the selectivity. A general model of selectivity for phosphoric acid-catalyzed azetidine desymmetrizations is proposed, which is based on the preference of the nucleophile and benzoyl group to occupy empty quadrants of the chiral catalyst pocket. Chemistry ; Nucleophile ; Selectivity ; Transition state ; Catalysis ; Azetidine ; Steric effects ; Desymmetrization ; Bifunctional ; Density functional theory ; Computational chemistry ; Stereochemistry ; Enantioselective synthesis ; Organic chemistry Synthesis and Catalytic Reactions ; Asymmetric Hydrogenation and Catalysis ; Asymmetric Synthesis and Catalysis