jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Haralampos N. Miras | Cole Mathis | Weimin Xuan | De‐Liang Long | Robert Pow | Leroy Cronin
Source: Proceedings of the National Academy of Sciences (article, 2020-05-05)
Field: Materials Science — Materials Chemistry

Spontaneous formation of autocatalytic sets with self-replicating inorganic metal oxide clusters Here we show how a simple inorganic salt can spontaneously form autocatalytic sets of replicating inorganic molecules that work via molecular recognition based on the {PMo<sub>12</sub>} ≡ [PMo<sub>12</sub>O<sub>40</sub>]<sup>3-</sup> Keggin ion, and {Mo<sub>36</sub>} ≡ [H<sub>3</sub>Mo<sub>57</sub>M<sub>6</sub>(NO)<sub>6</sub>O<sub>183</sub>(H<sub>2</sub>O)<sub>18</sub>]<sup>22-</sup> cluster. These small clusters are able to catalyze their own formation via an autocatalytic network, which subsequently template the assembly of gigantic molybdenum-blue wheel {Mo<sub>154</sub>} ≡ [Mo<sub>154</sub>O<sub>462</sub>H<sub>14</sub>(H<sub>2</sub>O)<sub>70</sub>]<sup>14-</sup>, {Mo<sub>132</sub>} ≡ [Mo<sup>VI</sup><sub>72</sub>Mo<sup>V</sup><sub>60</sub>O<sub>372</sub>(CH<sub>3</sub>COO)<sub>30</sub>(H<sub>2</sub>O)<sub>72</sub>]<sup>42-</sup> ball-shaped species containing 154 and 132 molybdenum atoms, and a {PMo<sub>12</sub>}⊂{Mo<sub>124</sub>Ce<sub>4</sub>} ≡ [H<sub>16</sub>Mo<sup>VI</sup><sub>100</sub>Mo<sup>V</sup><sub>24</sub>Ce<sub>4</sub>O<sub>376</sub>(H<sub>2</sub>O)<sub>56</sub> (PMo<sup>VI</sup><sub>10</sub>Mo<sup>V</sup><sub>2</sub>O<sub>40</sub>)(C<sub>6</sub>H<sub>12</sub>N<sub>2</sub>O<sub>4</sub>S<sub>2</sub>)<sub>4</sub>]<sup>5-</sup> nanostructure. Kinetic investigations revealed key traits of autocatalytic systems including molecular recognition and kinetic saturation. A stochastic model confirms the presence of an autocatalytic network involving molecular recognition and assembly processes, where the larger clusters are the only products stabilized by the cycle, isolated due to a critical transition in the network. Autocatalysis ; Molybdenum ; Crystallography ; Chemistry ; Molecule ; Metal ; Cluster (spacecraft) ; Stereochemistry ; Inorganic chemistry ; Catalysis ; Organic chemistry Polyoxometalates: Synthesis and Applications ; Nanocluster Synthesis and Applications ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Fei Jia | Hendrik V. Schröder | Liu‐Pan Yang | Carolina von Eßen | Sebastian Sobottka | Biprajit Sarkar | Kari Rissanen | Wei Jiang | Christoph A. Schalley
Source: Journal of the American Chemical Society (article, 2020-02-04)
Field: Chemistry — Organic Chemistry

Redox-Responsive Host–Guest Chemistry of a Flexible Cage with Naphthalene Walls "Naphthocage", a naphthalene-based organic cage, reveals very strong binding (up to 10<sup>10</sup> M<sup>-1</sup>) to aromatic (di)cationic guests, i.e., the tetrathiafulvalene mono- and dication and methyl viologen. Intercalation of the guests between two naphthalene walls is mediated by C-H···O, C-H···π, and cation···π interactions. The guests can be switched into and out of the cage by redox processes with high binding selectivity. Oxidation of the flexible cage itself in the absence of a guest leads to a stable radical cation with the oxidized naphthalene intercalated between and stabilized by the other two. Encapsulated guest cations are released from the cavity upon cage oxidation, paving the way to future applications in redox-controlled guest release or novel stimuli-responsive materials. Chemistry ; Cage ; Naphthalene ; Redox ; Host (biology) ; Host–guest chemistry ; Nanotechnology ; Organic chemistry ; Supramolecular chemistry ; Molecule ; Ecology Supramolecular Chemistry and Complexes ; Molecular Sensors and Ion Detection ; Neuroscience and Neuropharmacology Research
Authors: Xinming Wang | Shuming Zeng | Zhuchi Wang | Jun Ni
Source: The Journal of Physical Chemistry C (article, 2020-03-30)
Field: Materials Science — Materials Chemistry

Identification of Crystalline Materials with Ultra-Low Thermal Conductivity Based on Machine Learning Study We have developed machine learning (ML) models to predict the thermal conductivity (κ). By analyzing the impact of different choice of descriptors and various algorithms, we find that the XGBoost algorithm based on the descriptors of crystal structural and compositional information can accurately predict the thermal conductivity with an average mean absolute error of 2.13 W m–1 K–1. To understand and interpret the model, we have analyzed the feature importance of the ML model. Among the top five important features, we find that the thermal conductivity is clearly partitioned when projected to the avg(ΔHatomic)–Vpc and Vpa–Vpc dual-descriptor spaces, which exhibit simple heuristic rules useful for searching and designing materials with favorable thermal conductivity. We employ the learned models on the materials in the entire Inorganic Crystallographic Structure Database (ICSD) and find that the heavy elements like Cs, Au, Hg, Tl, and Pb are helpful to reduce κ. Using the prediction results on the ICSD, we have screened the materials with low κ like BiTe2Tl and Cl2CsI and validated them using first-principles calculations, which could indicate prospective thermoelectric materials. Thermal conductivity ; Materials science ; Heuristic ; Artificial intelligence ; Feature (linguistics) ; Computer science ; Identification (biology) ; Thermoelectric materials ; Machine learning ; Thermal ; Thermoelectric effect ; Algorithm ; Pattern recognition (psychology) ; Data mining ; Thermodynamics ; Physics ; Composite material Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; X-ray Diffraction in Crystallography
Authors: Marco Bragato | Guido Falk von Rudorff | O. Anatole von Lilienfeld
Source: Chemical Science (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Data enhanced Hammett-equation: reaction barriers in chemical space It is intriguing how the Hammett equation enables control of chemical reactivity throughout chemical space by separating the effect of substituents from chemical process variables, such as reaction mechanism, solvent, or temperature. We generalize Hammett's original approach to predict potential energies of activation in non aromatic molecular scaffolds with multiple substituents. We use global regression to optimize Hammett parameters <i>ρ</i> and <i>σ</i> in two experimental datasets (rate constants for benzylbromides reacting with thiols and ammonium salt decomposition), as well as in a synthetic dataset consisting of computational activation energies of ∼2400 S<sub>N</sub>2 reactions, with various nucleophiles and leaving groups (-H, -F, -Cl, -Br) and functional groups (-H, -NO<sub>2</sub>, -CN, -NH<sub>3</sub>, -CH<sub>3</sub>). Individual substituents contribute additively to molecular <i>σ</i> with a unique regression term, which quantifies the inductive effect. The position dependence of substituents can be modeled by a distance decaying factor for S<sub>N</sub>2. Use of the Hammett equation as a base-line model for Δ-machine learning models of the activation energy in chemical space results in substantially improved learning curves reaching low prediction errors for small training sets. Hammett equation ; Space (punctuation) ; Chemistry ; Computational chemistry ; Chemical equation ; Organic chemistry ; Physics ; Computer science ; Classical mechanics ; Kinetics ; Reaction rate constant Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Dean H. Barrett | Aderemi B. Haruna
Source: Current Opinion in Electrochemistry (article, 2020-02-18)
Field: Materials Science — Materials Chemistry

Artificial intelligence and machine learning for targeted energy storage solutions Computer science ; Field (mathematics) ; Characterization (materials science) ; Artificial intelligence ; Machine learning ; Interpretation (philosophy) ; Artificial neural network ; Nanotechnology ; Materials science ; Mathematics Machine Learning in Materials Science ; Advancements in Battery Materials ; Catalysis and Oxidation Reactions
Authors: Seo-Yul Kim | Seung-Ik Kim | Youn‐Sang Bae
Source: The Journal of Physical Chemistry C (article, 2020-08-13)
Field: Chemistry — Inorganic Chemistry

Machine-Learning-Based Prediction of Methane Adsorption Isotherms at Varied Temperatures for Experimental Adsorbents Metal–organic frameworks (MOFs) are crystalline materials and one of the optimal materials for large-scale grand canonical Monte Carlo (GCMC) simulations. Recently, there have been trials for applying machine learning (ML) to the results of large-scale GCMC simulations to predict gas adsorption on MOFs. However, the functions of the developed algorithms are not different from those of GCMC simulations, in that they provide a prediction of adsorption properties based on the coordination structures. In this study, we propose a novel Monte Carlo-Machine Learning (MC-ML) strategy, which combines ML with GCMC to provide the function that is distinct from that of GCMC. To verify the concept of the strategy, we designed an algorithm to predict methane isotherms at a range of temperatures from a methane isotherm at a temperature of 298 K. GCMC simulations functioned as a data-producing tool for ML, which yielded adsorption properties of 4951 structures in the CoRE-MOF database. The ML was applied to the GCMC results using experimentally measurable properties as features. Finally, the algorithm developed from ML was evaluated using experimental methane adsorption data for defective MOFs, MOFs with open metal sites, and non-MOF materials, which revealed the merits of the MC-ML strategy in comparison with typical GCMC. Adsorption ; Methane ; Thermodynamics ; Materials science ; Monte Carlo method ; Chemistry ; Physical chemistry ; Physics ; Organic chemistry ; Mathematics Metal-Organic Frameworks: Synthesis and Applications ; Catalytic Processes in Materials Science ; Carbon dioxide utilization in catalysis
Authors: Anastasios G. Papadiamantis | Jaak Jänes | Evangelos Voyiatzis | Lauri Sikk | Jaanus Burk | Peeter Burk | Andreas Tsoumanis | My Kieu Ha | Tae Hyun Yoon | Eugenia Valsami‐Jones | Iseult Lynch | Georgia Melagraki | Kaido Tämm | Antreas Afantitis
Source: Nanomaterials (article, 2020-10-13)
Field: Materials Science — Materials Chemistry

Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform A literature curated dataset containing 24 distinct metal oxide (Me<sub>x</sub>O<sub>y</sub>) nanoparticles (NPs), including 15 physicochemical, structural and assay-related descriptors, was enriched with 62 atomistic computational descriptors and exploited to produce a robust and validated in silico model for prediction of NP cytotoxicity. The model can be used to predict the cytotoxicity (cell viability) of Me<sub>x</sub>O<sub>y</sub> NPs based on the colorimetric lactate dehydrogenase (LDH) assay and the luminometric adenosine triphosphate (ATP) assay, both of which quantify irreversible cell membrane damage. Out of the 77 total descriptors used, 7 were identified as being significant for induction of cytotoxicity by Me<sub>x</sub>O<sub>y</sub> NPs. These were NP core size, hydrodynamic size, assay type, exposure dose, the energy of the Me<sub>x</sub>O<sub>y</sub> conduction band (<i>E</i><sub>C</sub>), the coordination number of the metal atoms on the NP surface (Avg. C.N. Me atoms surface) and the average force vector surface normal component of all metal atoms (v⟂ Me atoms surface). The significance and effect of these descriptors is discussed to demonstrate their direct correlation with cytotoxicity. The produced model has been made publicly available by the Horizon 2020 (H2020) NanoSolveIT project and will be added to the project's Integrated Approach to Testing and Assessment (IATA). Cytotoxicity ; Nanoparticle ; Analytics ; Metal ; Oxide ; Materials science ; Nanotechnology ; Chemistry ; Computer science ; Data science ; Metallurgy ; Biochemistry Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Computational Drug Discovery Methods
Authors: Kun‐Hua Tu | H. H. HUANG | Sangho Lee | Wonmoo Lee | Zehao Sun | Alfredo Alexander‐Katz | Caroline A. Ross
Source: Advanced Materials (article, 2020-11-18)
Field: Materials Science — Materials Chemistry

Machine Learning Predictions of Block Copolymer Self‐Assembly Directed self-assembly of block copolymers is a key enabler for nanofabrication of devices with sub-10 nm feature sizes, allowing patterning far below the resolution limit of conventional photolithography. Among all the process steps involved in block copolymer self-assembly, solvent annealing plays a dominant role in determining the film morphology and pattern quality, yet the interplay of the multiple parameters during solvent annealing, including the initial thickness, swelling, time, and solvent ratio, makes it difficult to predict and control the resultant self-assembled pattern. Here, machine learning tools are applied to analyze the solvent annealing process and predict the effect of process parameters on morphology and defectivity. Two neural networks are constructed and trained, yielding accurate prediction of the final morphology in agreement with experimental data. A ridge regression model is constructed to identify the critical parameters that determine the quality of line/space patterns. These results illustrate the potential of machine learning to inform nanomanufacturing processes. Nanomanufacturing ; Materials science ; Copolymer ; Nanolithography ; Simulated annealing ; Lithography ; Photolithography ; Annealing (glass) ; Nanotechnology ; Dewetting ; Computer science ; Artificial intelligence ; Biological system ; Polymer ; Machine learning ; Thin film ; Composite material ; Optoelectronics ; Fabrication Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Advancements in Photolithography Techniques
Authors: Mojgan Sheikhpour | Maryam Naghinejad | Alibakhsh Kasaeian | Armaghan Lohrasbi | Seyed Sadegh Shahraeini | Shahab Zomorodbakhsh
Source: International Journal of Nanomedicine (review, 2020-09-01)
Field: Materials Science — Materials Chemistry

&lt;p&gt;The Applications of Carbon Nanotubes in the Diagnosis and Treatment of Lung Cancer: A Critical Review&lt;/p&gt; The importance of timely diagnosis and the complete treatment of lung cancer for many people with this deadly disease daily increases due to its high mortality. Diagnosis and treatment with helping the nanoparticles are useful, although they have reasonable harms. This article points out that the side effects of using carbon nanotube (CNT) in this disease treatment process such as inflammation, fibrosis, and carcinogenesis are very problematic. Toxicity can reduce to some extent using the techniques such as functionalizing to proper dimensions as a longer length, more width, and greater curvature. The targeted CNT sensors can be connected to various modified vapors. In this regard, with helping this method, screening makes non-invasive diagnosis possible. Researchers have also found that nanoparticles such as CNTs could be used as carriers to direct drug delivery, especially with chemotherapy drugs. Most of these carriers were multi-wall carbon nanotubes (MWCNT) used for cancerous cell targeting. The results of laboratory and animal researches in the field of diagnosis and treatment became very desirable and hopeful. The collection of researches summarized has highlighted the requirement for a detailed assessment which includes CNT dose, duration, method of induction, etc., to achieve the most controlled conditions for animal and human studies. In the discussion section, 4 contradictory issues are discussed which are invited researchers to do more research to get clearer results. Carbon nanotube ; Nanotechnology ; Cancer ; Drug delivery ; Disease ; Medicine ; Lung cancer ; Carcinogenesis ; Materials science ; Internal medicine Carbon Nanotubes in Composites ; Advanced Chemical Sensor Technologies ; Polydiacetylene-based materials and applications
Authors: Jiangsan Zhao | Dmitry Kechasov | Boris Rewald | Gernot Bodner | Michel Verheul | Nicholas Clarke | Jihong Liu Clarke
Source: Remote Sensing (article, 2020-10-07)
Field: Chemistry — Analytical Chemistry

Deep Learning in Hyperspectral Image Reconstruction from Single RGB images—A Case Study on Tomato Quality Parameters Hyperspectral imaging has many applications. However, the high device costs and low hyperspectral image resolution are major obstacles limiting its wider application in agriculture and other fields. Hyperspectral image reconstruction from a single RGB image fully addresses these two problems. The robust HSCNN-R model with mean relative absolute error loss function and evaluated by the Mean Relative Absolute Error metric was selected through permutation tests from models with combinations of loss functions and evaluation metrics, using tomato as a case study. Hyperspectral images were subsequently reconstructed from single tomato RGB images taken by a smartphone camera. The reconstructed images were used to predict tomato quality properties such as the ratio of soluble solid content to total titratable acidity and normalized anthocyanin index. Both predicted parameters showed very good agreement with corresponding “ground truth” values and high significance in an F test. This study showed the suitability of hyperspectral image reconstruction from single RGB images for fruit quality control purposes, underpinning the potential of the technology—recovering hyperspectral properties in high resolution—for real-world, real time monitoring applications in agriculture any beyond. Hyperspectral imaging ; Artificial intelligence ; RGB color model ; Ground truth ; Metric (unit) ; Computer vision ; Mathematics ; Computer science ; Image quality ; Iterative reconstruction ; Image resolution ; Remote sensing ; Pattern recognition (psychology) ; Image (mathematics) ; Geography ; Engineering Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Rijuta Ganesh Saratale | Si‐Kyung Cho | Gajanan Ghodake | Han-Seung Shin | Ganesh Dattatraya Saratale | Yooheon Park | Hee‐Seok Lee | Ram Naresh Bharagava | Dong‐Su Kim
Source: Polymers (article, 2020-07-29)
Field: Materials Science — Biomaterials

Utilization of Noxious Weed Water Hyacinth Biomass as a Potential Feedstock for Biopolymers Production: A Novel Approach This study aims to utilize a noxious weed water hyacinth biomass (WH) for polyhydroxybutyrate (PHB) production. Alkaline and peracetic acid pretreatment was employed for the hydrolysis of WH and consequently enzymatic saccharification to produce fermentable sugars for PHB production. The pretreatment competence was determined using various operational parameters. By applying ambient conditions, alkaline pretreatment gave higher lignin removal of 65.0%, with 80.8% hydrolysis yield, and on enzyme hydrolysis (40 FPU/g of dry WH), produced total reducing sugar of about 523 mg/g of WH. The resulted WH enzymatic hydolysates were evaluated for the production of PHB by <i>Ralstonia eutropha</i> (ATCC 17699). The WH hydrolysates cultivation was compared to synthetic hydrolysates that contain a similar carbon composition in terms of bacterial growth and PHB synthesis. The effects of various supplements to enhance PHB production were estimated. Supplementation of corn steep liquor (CSL) as a cheap nitrogen source with WH hydrolysates favored a higher amount of PHB synthesis (73%), PHB titer of 7.30 g/L and PHB yield of 0.429 g/g of reducing sugar. Finally, using standard analytical tools, the physical and thermal characteristics of the extracted PHB were evaluated. The findings revealed WH was a promising and technically feasible option for transforming biomass into sustainable biopolymer conversion on a large scale. Chemistry ; Polyhydroxybutyrate ; Hydrolysis ; Hydrolysate ; Food science ; Biomass (ecology) ; Raw material ; Enzymatic hydrolysis ; Xylose ; Cupriavidus necator ; Reducing sugar ; Lignocellulosic biomass ; Sugar ; Pulp and paper industry ; Fermentation ; Polyhydroxyalkanoates ; Organic chemistry ; Agronomy ; Bacteria ; Biology biodegradable polymer synthesis and properties ; Biofuel production and bioconversion ; Catalysis for Biomass Conversion
Authors: Nhut-Thanh Tran | Masayuki Fukuzawa
Source: Sensors (article, 2020-10-17)
Field: Chemistry — Analytical Chemistry

A Portable Spectrometric System for Quantitative Prediction of the Soluble Solids Content of Apples with a Pre-calibrated Multispectral Sensor Chipset A portable spectrometric system for nondestructive assessment of the soluble solids content (SSC) of fruits for practical applications has been proposed and its performance has been examined by an experiment on quantitative prediction of the SSC of apples. Although the spectroscopic technique is a powerful tool for predicting the internal qualities of fruits, its practical applications are limited due to its high cost and complexity. In the proposed system, the spectra of apples were collected by a simple optical setup with a cheap pre-calibrated multispectral chipset. An optimal multiple linear regression model with five wavebands at 900, 760, 730, 680, and 535 nm revealed the best performance with the coefficient of determination of prediction and the root mean square error of prediction of 0.861 and 0.403 °Brix, respectively, which was comparable to that of the previous studies using dispersive spectrometers. Compared with previously reported systems using discrete filters or light emitting diodes, the proposed system was superior in terms of manufacturability and reproducibility. The experimental results confirmed that the proposed system had a considerable potential for practical, cost-effective applications of the SSC prediction, not only for apples but also for other fruits. Chipset ; Multispectral image ; Spectrometer ; Computer science ; Design for manufacturability ; Biological system ; Process engineering ; Engineering ; Optics ; Artificial intelligence ; Physics ; Mechanical engineering Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Matej Kanduč | Won Kyu Kim | Rafael Roa | Joachim Dzubiella
Source: ACS Nano (article, 2020-12-31)
Field: Materials Science — Materials Chemistry

How the Shape and Chemistry of Molecular Penetrants Control Responsive Hydrogel Permeability The permeability of hydrogels for the selective transport of molecular penetrants (drugs, toxins, reactants, <i>etc</i>.) is a central property in the design of soft functional materials, for instance in biomedical, pharmaceutical, and nanocatalysis applications. However, the permeation of dense and hydrated polymer membranes is a complex multifaceted molecular-level phenomenon, and our understanding of the underlying physicochemical principles is still very limited. Here, we uncover the molecular principles of permeability and selectivity in hydrogel permeation. We combine the solution-diffusion model for permeability with comprehensive atomistic simulations of molecules of various shapes and polarities in a responsive hydrogel in different hydration states. We find in particular that dense collapsed states are extremely selective, owing to a delicate balance between the partitioning and diffusivity of the penetrants. These properties are sensitively tuned by the penetrant size, shape, and chemistry, leading to vast cancellation effects, which nontrivially contribute to the permeability. The gained insights enable us to formulate semiempirical rules to quantify and extrapolate the permeability categorized by classes of molecules. They can be used as approximate guiding ("rule-of-thumb") principles to optimize penetrant or membrane physicochemical properties for a desired permeability and membrane functionality. Penetrant (biochemical) ; Permeation ; Permeability (electromagnetism) ; Membrane ; Polymer ; Molecular dynamics ; Nanotechnology ; Materials science ; Chemistry ; Self-healing hydrogels ; Molecule ; Chemical physics ; Computational chemistry ; Polymer chemistry ; Organic chemistry Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization ; Membrane Separation and Gas Transport