jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: John R. F. B. Connolly | J. A. Muñoz | Cris Lapthorn | David Higton | Johannes P.C. Vissers | Alison Webb | Claire Beaumont | Gordon J. Dear
Source: Journal of the American Society for Mass Spectrometry (article, 2021-07-23)
Field: Chemistry — Spectroscopy

Investigation into Small Molecule Isomeric Glucuronide Metabolite Differentiation Using <i>In Silico</i> and Experimental Collision Cross-Section Values Identifying isomeric metabolites remains a challenging and time-consuming process with both sensitivity and unambiguous structural assignment typically only achieved through the combined use of LC-MS and NMR. Ion mobility mass spectrometry (IMMS) has the potential to produce timely and accurate data using a single technique to identify drug metabolites, including isomers, without the requirement for in-depth interpretation (<i>cf.</i> MS/MS data) using an automated computational pipeline by comparison of experimental collision cross-section (CCS) values with predicted CCS values. An ion mobility enabled Q-Tof mass spectrometer was used to determine the CCS values of 28 (14 isomeric pairs of) small molecule glucuronide metabolites, which were then compared to two different <i>in silico</i> models; a quantum mechanics (QM) and a machine learning (ML) approach to test these approaches. The difference between CCS values within isomer pairs was also assessed to evaluate if the difference was large enough for unambiguous structural identification through <i>in silico</i> prediction. A good correlation was found between both the QM- and ML-based models and experimentally determined CCS values. The predicted CCS values were found to be similar between ML and QM <i>in silico</i> methods, with the QM model more accurately describing the difference in CCS values between isomer pairs. Of the 14 isomeric pairs, only one (naringenin glucuronides) gave a sufficient difference in CCS values for the QM model to distinguish between the isomers with some level of confidence, with the ML model unable to confidently distinguish the studied isomer pairs. An evaluation of analyte structures was also undertaken to explore any trends or anomalies within the data set. Chemistry ; Ion-mobility spectrometry ; In silico ; Mass spectrometry ; Analyte ; Metabolomics ; Metabolite ; Molecule ; Biological system ; Computational chemistry ; Chromatography ; Analytical Chemistry (journal) Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications
Authors: Jessica Wahlers | Jèssica Margalef | Eric Hansen | Armita Bayesteh | Paul Helquist | Montserrat Diéguez | Òscar Pàmies | Olaf Wiest | Per‐Ola Norrby
Source: Nature Communications (article, 2021-11-18)
Field: Chemistry — Organic Chemistry

Proofreading experimentally assigned stereochemistry through Q2MM predictions in Pd-catalyzed allylic aminations The palladium-catalyzed enantioselective allylic substitution by carbon or nitrogen nucleophiles is a key transformation that is particularly useful for the synthesis of bioactive compounds. Unfortunately, the selection of a suitable ligand/substrate combination often requires significant screening effort. Here, we show that a transition state force field (TSFF) derived by the quantum-guided molecular mechanics (Q2MM) method can be used to rapidly screen ligand/substrate combinations. Testing of this method on 77 literature reactions revealed several cases where the computationally predicted major enantiomer differed from the one reported. Interestingly, experimental follow-up led to a reassignment of the experimentally observed configuration. This result demonstrates the power of mechanistically based methods to predict and, where necessary, correct the stereochemical outcome. Nucleophile ; Allylic rearrangement ; Ligand (biochemistry) ; Enantioselective synthesis ; Substrate (aquarium) ; Catalysis ; Enantiomer ; Combinatorial chemistry ; Stereochemistry ; Chemistry ; Organic chemistry ; Biology ; Ecology Asymmetric Synthesis and Catalysis ; Asymmetric Hydrogenation and Catalysis ; Chemical Synthesis and Analysis
Authors: James Barker | Laura‐Sophie Berg | Jan Hamaekers | Astrid Maaß
Source: Batteries & Supercaps (article, 2021-05-31)
Field: Materials Science — Materials Chemistry

Rapid Prescreening of Organic Compounds for Redox Flow Batteries: A Graph Convolutional Network for Predicting Reaction Enthalpies from SMILES Abstract Identifying interesting redox‐active couples from the vastness of organic chemical space requires rapid screening techniques. A good initial indicator for couples worthy of further investigation is the heat of reaction Δ H °. Traditional methods of calculating this quantity, both experimental and computational, are prohibitively costly at large scale. Instead, we apply a graph convolutional network to estimate the heats of reaction of arbitrary redox couples orders of magnitude faster than conventional computational methods. Our graph takes only SMILES strings as input, rather than full three‐dimensional geometries. A network trained on a dataset of atomization enthalpies for approximately 45,000 hydrogenation reactions, applied to a separate test set of 235 compounds and benchmarked against experimental heats of reaction, produces promisingly accurate results, and we anticipate that this methodology can be extended to other RFB‐relevant reactions. However, lower predictivity for compounds in regions of chemical space not covered by the training dataset reinforces the pivotal importance of the particular chemistries presented to a model during training. Redox ; Chemical space ; Graph ; Computer science ; Reaction conditions ; Space (punctuation) ; Test set ; Set (abstract data type) ; Chemistry ; Biological system ; Theoretical computer science ; Artificial intelligence ; Organic chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Joice Jaqueline Kaschuk | Maryam Borghei | Katariina Solin | Anurodh Tripathi | Alexey Khakalo | Fábio A. S. Leite | Aida Branco | Miriam C. Amores de Sousa | Elisabete Frollini | Orlando J. Rojas
Source: ACS Applied Polymer Materials (article, 2021-03-16)
Field: Materials Science — Polymers and Plastics

Cross-Linked and Surface-Modified Cellulose Acetate as a Cover Layer for Paper-Based Electrochromic Devices We studied the surface and microstructure of cellulose acetate (CA) films to tailor their barrier and mechanical properties for application in electrochromic devices (ECDs). Cross-linking of CA was carried out with pyromellitic dianhydride to enhance the properties relative to unmodified CA: solvent resistance (by 43% in acetone and 37% in DMSO), strength (by 91% for tensile at break), and barrier (by 65% to oxygen and 92% to water vapor). Surface modification via tetraethyl orthosilicate and octyltrichlorosilane endowed the films with hydrophobicity, stiffness, and further enhanced solvent resistance. A detailed comparison of structural, chemical, surface, and thermal properties was performed by using X-ray diffraction, dynamic mechanical analyses, Fourier-transform infrared spectroscopy, and atomic force microscopy. Coplanar ECDs were synthesized by incorporating a hydrogel electrolyte comprising TEMPO-oxidized cellulose nanofibrils and an ionic liquid. When applied as the top layer in the ECDs, cross-linked and hydrophobized CA films extended the functionality of the assembled displays. The results indicate excellent prospects for CA films in achieving environmental-friendly ECDs that can replace poly(ethylene terephthalate)-based counterparts. Materials science ; Tetraethyl orthosilicate ; Chemical engineering ; Electrochromic devices ; Cellulose ; Fourier transform infrared spectroscopy ; Pyromellitic dianhydride ; Electrochromism ; Solvent ; Surface modification ; Ionic liquid ; Polymer chemistry ; Polyimide ; Layer (electronics) ; Composite material ; Organic chemistry ; Nanotechnology ; Chemistry Transition Metal Oxide Nanomaterials ; Advanced Cellulose Research Studies ; Advanced Sensor and Energy Harvesting Materials
Authors: Kamila Startek | Anna Szczurek | L.T. Tran | Justyna Krzak | Alicja Bachmatiuk | Anna Łukowiak
Source: Coatings (article, 2021-05-14)
Field: Materials Science — Materials Chemistry

Structural and Functional Properties of Fluorinated Silica Hybrid Barrier Layers on Flexible Polymeric Foil The reported work was focused on sol–gel-derived organically modified and fluorinated silica coatings deposited on elastic polymeric foil. The structure and topography of the coatings were tested by infrared spectroscopy and microscopic studies. The functional properties were determined using thermal analysis, surface analysis, and oxygen permeability tests. The barrier feature of the investigated materials against oxygen was correlated with the properties of the coatings. The hybrid (organic–inorganic) structure of the coatings was proven, demonstrating the presence of a silica network modified with alkyl and fluoroalkyl groups since precursors with the isooctyl group or different lengths of the fluoroalkyl chains were used for the syntheses. The coatings were free of defects and had a smooth surface except for the sample containing the longest fluoroalkyl chain (perfluorododecyl group), which showed a wrinkle-like surface. The hydrophobic character of the coatings increased, whereas the oxygen permeation coefficient values decreased (reaching a fourfold lower coefficient in comparison to the bare substrate) with a higher content of the fluorinated carbon atoms in the structure. The results were enriched by the information from the thermomechanical analysis as well as nanoindentation and scratch tests giving values of the glass transition temperature, thermal expansion coefficient, coatings adhesion, and hardness of the investigated systems. Materials science ; Thermal expansion ; Alkyl ; FOIL method ; Oxygen permeability ; Glass transition ; Composite material ; Nanoindentation ; Permeation ; Adhesion ; Contact angle ; Substrate (aquarium) ; Oxygen ; Surface modification ; Chemical engineering ; Organic chemistry ; Polymer ; Membrane ; Chemistry Silicone and Siloxane Chemistry ; Surface Modification and Superhydrophobicity ; Synthesis and properties of polymers
Authors: Huan‐Tsung Chang
Source: Frontiers in Analytical Science (article, 2021-07-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Grand Challenges in Analytical Science FIELD GRAND CHALLENGE article Front. Anal. Sci., 08 July 2021 https://doi.org/10.3389/frans.2021.725070 Grand Challenges ; Field (mathematics) ; Front (military) ; Grand strategy ; Political science ; Physics ; Meteorology ; Law ; Mathematics Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Advanced biosensing and bioanalysis techniques
Authors: Deepa Prabhu | Sathyashankara Sharma | P R Prabhu | Jilna Jomy | Ramakrishna Vikas Sadanand
Source: Journal of the Iranian Chemical Society (article, 2021-08-19)
Field: Materials Science — Materials Chemistry

Analysis of the inhibiting action of pectin on corrosion of AISI1040 dual-phase steel with ferrite–martensite and ferrite–bainite structure: a comparison in 0.5 M sulphuric acid Abstract The adsorption of pectin and corrosion inhibition of dual-phase AISI1040 steel with ferrite–martensite and ferrite–bainite structure in 0.5 M sulphuric acid (H 2 SO 4 ) solution have been investigated using the weightloss method. This work investigates the adsorption mechanism and quantum chemical calculations of pectin. For a specific set of parameters such as immersion time and concentration of inhibitor, the maximum inhibition efficiency of 83.36% is observed. The inhibition efficiency increased with pectin concentration and decreased with immersion time at 30 ℃. The results from the statistical analysis show that the concentration of inhibitor is having the highest influence with a 43.87% contribution on the inhibition efficiency. The adsorption study revealed that the Langmuir adsorption isotherm gave the best-fit results out of all the isotherms studied. Theoretical studies based on density functional theory supported experimental observations. From the results, it was also observed that lower weight loss and better inhibition efficiency are achieved in the case of ferrite–bainite when compared to the ferrite–martensite structure. Surface characterization confirmed corrosion and inhibition on the surface of the metal as the surface became uneven when exposed to a corrosive medium and smooth when immersed in the inhibited solution. Bainite ; Corrosion ; Adsorption ; Martensite ; Ferrite (magnet) ; Materials science ; Langmuir adsorption model ; Dual-phase steel ; Pectin ; Metallurgy ; Corrosion inhibitor ; Chemical engineering ; Nuclear chemistry ; Chemistry ; Composite material ; Microstructure ; Organic chemistry ; Biochemistry Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Anele Mpupa | Mehmet Dinc | Boris Mizaikoff | Philiswa N. Nomngongo
Source: Processes (article, 2021-01-20)
Field: Chemistry — Analytical Chemistry

Exploration of a Molecularly Imprinted Polymer (MIPs) as an Adsorbent for the Enrichment of Trenbolone in Water The presence of endocrine disruptors in surface waters can have negative implications on wildlife and humans both directly and indirectly. A molecularly imprinted polymer (MIP) was explored for its potential to enhance the UV-Vis determination of trenbolone in water using solid-phase extraction (SPE). The synthesized MIP was studied using Fourier transform infrared spectra (FTIR) and scanning electron microscopy (SEM). Using the MIP resulted in a preconcentration and enrichment factor of 14 and 8, respectively. Trenbolone binding on the MIP was shown to follow a Langmuir adsorption and had a maximum adsorption capacity of 27.5 mg g−1. Interference studies showed that the MIP selectivity was not compromised by interferences in the sample. The MIP could be recycled three times before significant loss in analyte recovery. Molecularly imprinted polymer ; Adsorption ; Solid phase extraction ; Fourier transform infrared spectroscopy ; Chemistry ; Analyte ; Chromatography ; Molecular imprinting ; Selective adsorption ; Polymer ; Extraction (chemistry) ; Selectivity ; Chemical engineering ; Organic chemistry Analytical chemistry methods development ; Analytical Chemistry and Chromatography ; Pharmaceutical and Antibiotic Environmental Impacts
Authors: M.R. Kuppusamy | T. Ramanathan
Source: Materiali in tehnologije (article, 2021-02-12)
Field: Materials Science — Materials Chemistry

Experimental analysis of the thermal-barrier coating for an Al2O3-TiO2 ceramic coated CI engine operating on calophyllum inophyllum oil This work focuses on the thermal barrier coating (TBC) with a thickness of 300 m for the combustion chamber of a single-cylinder four-stroke diesel engine. The piston crown, and inlet and exhaust-valve head are coated using the air-plasma-spray coating technique. Records found in the available literature revealed that the use of aluminum oxide titanium dioxide (Al2O3-TiO2) ceramic-powder coating and punnagam oil methyl ester (POME) is the most preferred method for improving emission levels. Punnagam oil methyl ester (POME) was prepared with transesterification in order to blend the same substances with biodiesel proportions of B25, B50, B75, B100 and conventional diesel. The proposed test report relies on a single-cylinder four-stroke diesel engine without any modification. This research is aimed at reducing the emission levels and attaining a greener and cleaner system by using a renewable fuel. The test was carried out to assess the engine performance, viz., the brake power, brake thermal efficiency, volumetric efficiency, brake specific fuel consumption and air-fuel ratio, assess the emission levels when combining POME and the Al2O3-TiO2 coated engine and compare them with those of conventional diesel. The amounts of pollutants such as unburned hydrocarbon (HC), carbon monoxide (CO), oxide of nitrogen (NOx) and the emitted smoke opacity were compared to those produced by conventional diesel. The result revealed that POME and the Al2O3-TiO2 ceramic improved the performance of the engine so that the pollution levels are much better than those produced by conventional diesel. Keywords: punnagam oil methyl ester, thermal-barrier coating, air-plasma-spray coating technique, aluminum oxide titanium dioxide, ceramic powder material, performance, engine emission V ~lanku sta se avtorja osredoto~ila na opis za{~itne plasti, ki deluje kot termi~na bariera debeline 300 m v eno-valjnem {tiri-taktnem motorju. Povr{ine oz Materials science ; Thermal barrier coating ; Ceramic ; Coating ; Composite material Catalytic Processes in Materials Science ; High-Temperature Coating Behaviors ; Biodiesel Production and Applications
Authors: Melike Çalışkan | Sema Akay | Berkant Kayan | Talat Baran | Dimitrios Kalderis
Source: Molecules (article, 2021-11-13)
Field: Chemistry — Organic Chemistry

Preparation and Application of a Hydrochar-Based Palladium Nanocatalyst for the Reduction of Nitroarenes In the present study, a novel heterogeneous catalyst was successfully fabricated through the decoration of palladium nanoparticles on the surface of designed Fe<sub>3</sub>O<sub>4</sub>-coffee waste composite (Pd-Fe<sub>3</sub>O<sub>4</sub>-CWH) for the catalytic reduction of nitroarenes. Various characterization techniques such as XRD, FE-SEM and EDS were used to establish its nano-sized chemical structure. It was determined that Pd-Fe<sub>3</sub>O<sub>4</sub>-CWH is a useful nanocatalyst, which can efficiently reduce various nitroarenes, including 4-nitrobenzoic acid (4-NBA), 4-nitroaniline (4-NA), 4-nitro-o-phenylenediamine (4-NPD), 2-nitroaniline (2-NA) and 3-nitroanisole (3-NAS), using NaBH<sub>4</sub> in aqueous media and ambient conditions. Catalytic reactions were monitored with the help of high-performance liquid chromatography. Additionally, Pd-Fe<sub>3</sub>O<sub>4</sub>-CWH was proved to be a reusable catalyst by maintaining its catalytic activity through six successive runs. Moreover, the nanocatalyst displayed a superior catalytic performance compared to other catalysts by providing a shorter reaction time to complete the reduction in nitroarenes. Catalysis ; Palladium ; Nitroaniline ; Aqueous solution ; Materials science ; Nitro ; Composite number ; Nanoparticle ; Chemical engineering ; Nuclear chemistry ; Chemistry ; Organic chemistry ; Nanotechnology ; Composite material Nanomaterials for catalytic reactions
Authors: Iveren Blessing Iorliam | Barnabas A. Ikyo | Aamo Iorliam | Emmanuel Odeh Okube | Dekera Kenneth Kwaghtyo | Yahaya Isah Shehu
Source: Journal of Data Analysis and Information Processing (article, 2021-01-01)
Field: Chemistry — Analytical Chemistry

Application of Machine Learning Techniques for Okra Shelf Life Prediction The ability of machine learning techniques to make accurate predications is increasing. The aim of this work is to apply machine learning techniques such as Support Vector Machine, Na&#239ve Bayes, Decision Tree, Logistic Regression, and K-Nearest Neighbour algorithms to predict the shelf life of Okra. Predicting the shelf life of Okra is important because Okra becomes harmful for human consumption if consumed after its shelf life. Okra parameters such as weight loss, firmness, Titrable Acid, Total Soluble Solids, Vitamin C/Ascorbic acid content, and PH were used as inputs into these machine learning techniques. Support Vector Machine, Na&#239ve Bayes and Decision Tree each accurately predicted the shelf life of Okra with accuracies of 100%. However, the Logistic Regression and K-Nearest Neighbour achieved 88.89% and 88.33% accuracies, respectively. These results showed that machine learning techniques especially Support Vector Machine, Na&#239ve Bayes and Decision Tree can be effectively applied for the prediction of Okra shelf life. Machine learning ; Decision tree ; Naive Bayes classifier ; Support vector machine ; Shelf life ; Artificial intelligence ; Logistic regression ; Ascorbic acid ; Computer science ; Mathematics ; Chemistry ; Food science Spectroscopy and Chemometric Analyses ; Food Science and Nutritional Studies ; Agricultural Practices and Plant Genetics
Authors: Niraj Thapa | Meenal Chaudhari | Anthony A. Iannetta | Clarence White | Kaushik Roy | Robert H. Newman | Leslie M. Hicks | Dukka B. KC
Source: Scientific Reports (article, 2021-06-15)
Field: Chemistry — Spectroscopy

A deep learning based approach for prediction of Chlamydomonas reinhardtii phosphorylation sites Protein phosphorylation, which is one of the most important post-translational modifications (PTMs), is involved in regulating myriad cellular processes. Herein, we present a novel deep learning based approach for organism-specific protein phosphorylation site prediction in Chlamydomonas reinhardtii, a model algal phototroph. An ensemble model combining convolutional neural networks and long short-term memory (LSTM) achieves the best performance in predicting phosphorylation sites in C. reinhardtii. Deemed Chlamy-EnPhosSite, the measured best AUC and MCC are 0.90 and 0.64 respectively for a combined dataset of serine (S) and threonine (T) in independent testing higher than those measures for other predictors. When applied to the entire C. reinhardtii proteome (totaling 1,809,304 S and T sites), Chlamy-EnPhosSite yielded 499,411 phosphorylated sites with a cut-off value of 0.5 and 237,949 phosphorylated sites with a cut-off value of 0.7. These predictions were compared to an experimental dataset of phosphosites identified by liquid chromatography-tandem mass spectrometry (LC-MS/MS) in a blinded study and approximately 89.69% of 2,663 C. reinhardtii S and T phosphorylation sites were successfully predicted by Chlamy-EnPhosSite at a probability cut-off of 0.5 and 76.83% of sites were successfully identified at a more stringent 0.7 cut-off. Interestingly, Chlamy-EnPhosSite also successfully predicted experimentally confirmed phosphorylation sites in a protein sequence (e.g., RPS6 S245) which did not appear in the training dataset, highlighting prediction accuracy and the power of leveraging predictions to identify biologically relevant PTM sites. These results demonstrate that our method represents a robust and complementary technique for high-throughput phosphorylation site prediction in C. reinhardtii. It has potential to serve as a useful tool to the community. Chlamy-EnPhosSite will contribute to the understanding of how protein phosphorylation influences various biological processes in this important model microalga. Chlamydomonas reinhardtii ; Phosphorylation ; Threonine ; Proteome ; Protein phosphorylation ; Computational biology ; Serine ; UniProt ; Biology ; Chemistry ; Computer science ; Biochemistry ; Protein kinase A ; Gene Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Machine Learning in Bioinformatics