jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Maryam Khosravinezhad | Masoumeh Kazemi
Source: Preprints.org (preprint, 2021-05-25)
Field: Materials Science — Materials Chemistry

Study on Kinetics of Thermal Degradation of Polymethyl Methacrylate/Cyclic Olefin Copolymer Blend In this investigation, polymethyl methacrylate (PMMA) was mixed with cyclic olefin copolymer (COC) because of its hardness, strength, and transparency properties. The results of thermal analysis through TGA and DTG showed that the thermal properties of the alloy are improved using 40% cyclic olefin copolymer. Kinetics of thermal degradation (pyrolysis) of polymers have been studied and analyzed and thermal pyrolysis of polymethyl methacrylate and cyclic olefin copolymer thermoplastic polymer was conducted. The computation of kinetic analysis is measured along with the different methods used to study the kinetics. The activation energy (E) of degradation of studied materials was estimated using Ozawa Flynn and Wall (OFW), Starink and Kissinger&amp;rsquo;s methods, and evaluation of three kinetic parameters taken appropriate kinetic model in terms of percent change for both types of polymers have been proposed, and finally, simulated curves were compared with the experimental curves. Both mechanisms of degradation for COC and PMMA under nitrogen atmosphere will reflect intramolecular transfer and random scission of the main chain. Materials science ; Copolymer ; Polymer chemistry ; Kinetics ; Methacrylate ; Activation energy ; Polymer ; Olefin fiber ; Pyrolysis ; Thermoplastic ; Degradation (telecommunications) ; Thermal stability ; Chemical engineering ; Composite material ; Chemistry ; Organic chemistry Thermal and Kinetic Analysis ; Fiber-reinforced polymer composites ; Inorganic and Organometallic Chemistry
Authors: Daniel S. King | Laura Gagliardi | Donald G. Truhlar
Source:  (preprint, 2021-06-30)
Field: Materials Science — Materials Chemistry

Machine-Learned Energy Functionals for Strongly Correlated Systems We introduce multiconfiguration data-driven functional theory (MC-DDFT) as a new approach to multiconfiguration nonclassical functional theory (MC-NCFT), in which the classical energy of a multiconfigurational wave function is combined with a machine-learned functional for the nonclassical exchange-correlation energy. We also present results obtained by a related approach, multiconfiguration energy-correcting functional theory (MC-ECFT), in which the total energy of a wave function method (e.g. CASSCF or NEVPT2) is corrected with a machine-learned functional. On a dataset of carbene singlet-triplet energy splittings, we demonstrate that these new multiconfiguration data-driven functional methods (MC-DDFMs) are able to achieve near-benchmark performance on systems not used for training while being less active-space dependent than multiconfiguration pair-density functional theory using currently available translated functionals. Density functional theory ; Wave function ; Singlet state ; Orbital-free density functional theory ; Energy (signal processing) ; Energy functional ; Hybrid functional ; Function (biology) ; Space (punctuation) ; Physics ; Benchmark (surveying) ; Statistical physics ; Quantum mechanics ; Computer science ; Excited state Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; Advanced NMR Techniques and Applications
Authors: Eduardo A. Barros de Moraes | Jorge L. Suzuki | Mohsen Zayernouri
Source: Computational Materials Science (preprint, 2021-06-17)
Field: Materials Science — Materials Chemistry

Atomistic-to-meso multi-scale data-driven graph surrogate modeling of dislocation glide Statistical physics ; Computation ; Molecular dynamics ; Dislocation ; Jump ; Granularity ; Jump process ; Graph ; Computer science ; Materials science ; Algorithm ; Physics ; Theoretical computer science Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Wenrong Chen | Elijah N. McCool | Liangliang Sun | Yong Zang | Xia Ning | Xiaowen Liu
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-11-03)
Field: Chemistry — Spectroscopy

Evaluation of machine learning models for proteoform retention and migration time prediction in top-down mass spectrometry Abstract Reversed-phase liquid chromatography (RPLC) and capillary zone electrophoresis (CZE) are two popular proteoform separation methods in mass spectrometry (MS)-based top-down proteomics. The prediction of proteoform retention time in RPLC and migration time in CZE provides additional information that can increase the accuracy of proteoform identification and quantification. Whereas existing methods for retention and migration time prediction are mainly focused on peptides in bottom-up MS, there is still a lack of methods for the problem in top-down MS. We systematically evaluated 6 models for proteoform retention and/or migration time prediction in top-down MS and showed that the Prosit model achieved a high accuracy ( R 2 &gt; 0.91) for proteoform retention time prediction and that the Prosit model and a fully connected neural network model obtained a high accuracy ( R 2 &gt; 0.94) for proteoform migration time prediction. Chemistry ; Retention time ; Mass spectrometry ; Proteomics ; Capillary electrophoresis ; Top-down proteomics ; Task (project management) ; Computer science ; Chromatography ; Tandem mass spectrometry ; Protein mass spectrometry ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Ercan Çınar | Eyüp Başaran | Ömer Erdoğan | Reşit Çakmak | Mehmet Boğa | Özge Çevik
Source: Research Square (preprint, 2021-05-24)
Field: Chemistry — Organic Chemistry

Synthesis and biological evaluation of some pyrazolone based Schiff base derivatives as enzymes inhibitors, antioxidant, and anticancer agents Abstract Drugs with sufficient efficacy for use in the therapy of Alzheimer and other diseases caused by oxidative stress have not yet been produced until today despite the great efforts. Therefore, many people all over the world are experiencing serious health problems due to these diseases nowadays. In the current study, a series of pyrazolone based Schiff base derivatives (2a-e) (except 2a ) as target molecules were successfully synthesized for the first time, and then structurally illuminated by using FT-IR, 1 H NMR and 13 C NMR. Their inhibition activities on acetylcholinesterase (AChE), butyrylcholinesterase (BChE), and tyrosinase enzymes were extensively tested, respectively. Furthermore, the synthesized molecules were also investigated for antioxidant and anticancer activities. The potential in vitro cytotoxic activities of the title molecules were examined on HeLa cancer and 3T3 mouse normal fibroblast cell lines using MTT assay. Our results showed that 1b (IC 50 : 9.497 mM) and 2a (IC 50 : 30.49 mM) significantly decreased the proliferation of HeLa cells. On the other hand, the apoptotic effect of 1b and 2a were investigated with acridine orange/propidium iodide double staining. The apoptotic cell ratios of the molecules treated with 1b and 2a were determined as 60 and 64%, respectively. While 2b was found to be a very active molecule in antioxidant activities assays in ABTS cation radical scavenging (IC 50 :17.95±0.47 mM) and CUPRAC (A 0.5 :48.73±0.52 mM) activities, 2c had a very active molecule in AChE, BChE and tyrosinase inhibitory activities with 82.79±1.03, 91.39±1.06 and 92.60±1.80 inhibition%, respectively. Also, the target molecules (2a-e) showed better antioxidant and enzyme inhibitory activities than those of ester derivatives (1a-e). HeLa ; Chemistry ; Antioxidant ; MTT assay ; ABTS ; Acetylcholinesterase ; Biochemistry ; Butyrylcholinesterase ; Enzyme ; Apoptosis ; In vitro ; Pharmacology ; Aché ; DPPH ; Biology Synthesis and biological activity ; Cholinesterase and Neurodegenerative Diseases ; Computational Drug Discovery Methods
Authors: Ezgi Gulcay-Ozcan | Paul Iacomi | Youngsang Ko | Jong‐San Chang | Guillaume Rioland | Sabine Devautour‐Vinot | Guillaume Maurin
Source: ChemRxiv (preprint, 2021-03-19)
Field: Chemistry — Inorganic Chemistry

Breaking the Upper Bound of Siloxane Uptake: Metal-Organic Frameworks as an Adsorbent Platform Biogas, regarded as a promising renewable energy source, still needs to be upgraded. This calls for the removal of the most prominent contaminants, among others the octamethylcyclotetrasiloxane (D4) molecule. Herein, high throughput computational screening in tandem with synthesis and adsorption testing revealed the hydrophobic Zr-MOF PCN-777 as an optimal D4 adsorbent with record gravimetric (1.8 g/g) and volumetric (0.49 g/cm3) uptakes, alongside with a reversible and fast adsorption/desorption process, good cyclability and easy regeneration. This MOF was demonstrated to encompass an ideal combination of mesoporous cages and chemical functionality to enable an optimal packing of the siloxane molecules and their efficient removal while maintaining the process highly reversible thanks to moderately high host/guest interactions. This work highlights the efficacy of an integrated workflow for accelerating adsorbent selection for a desired application, spanning the entire pipeline from method validation to computational screening, synthesis and adsorption testing towards the identification of the optimal adsorbents.&lt;br&gt; Adsorption ; Siloxane ; Octamethylcyclotetrasiloxane ; Chemical engineering ; Mesoporous material ; Materials science ; Metal-organic framework ; Desorption ; Nanotechnology ; Chemistry ; Process engineering ; Organic chemistry ; Catalysis Metal-Organic Frameworks: Synthesis and Applications ; Catalytic Processes in Materials Science ; Carbon dioxide utilization in catalysis
Authors: Faten Eshrati Yeganeh | Amir Eshrati Yeganeh | Bahareh Farasati Far | Iman Akbarzadeh | Sameer Quazi | Muhammad Safdar
Source: Research Square (preprint, 2021-12-03)
Field: Materials Science — Biomaterials

In-Vitro Cytotoxicity and Anticancer Drug Release Behavior of Methionine - Coated Magnetite Nanoparticles As Carriers. Abstract An innovative and customized drug delivery system for in vitro cancer treatment has been developed successfully by a simple one-step method. A CoFe 2 O 4 @Methionine core-shell nanoparticle was prepared by the reflux assay, in which amino acid on the surface makes the ferrite biocompatible, enhances the chemical stability of the compound, and increases the drug loading capacity. The synthesized nanoparticles were evaluated using SEM, TEM, FTIR, and VSM, while XRD and TGA analysis verified the presence of a coating amino acid on the surface of CoFe2O4. The appearance of a new peak for C≡N in the FTIR spectrum validates the synthesis of a letrozole-loaded carrier. Both uncoated CoFe2O4 and methionine-coated CoFe2O4 nanoparticles behave super-paramagnetically at room temperature, with saturation values of 46 emu/g and 16.8 emu/g, respectively. SEM and TEM were used to characterize the morphology and size of samples, revealing that the average particle size was around 28–29 nm. The loading of Letrozole and the effect of pH (5, 7.4) on the release behavior of the carrier were studied. The result of the drug release in pH (5) was about 88% higher than pH (7.4). Also, the preparation has been evaluated for determining its cytotoxicity using MCF-7, MDA-MB-231, and MCF10A cell lines as an in vitro model, and the results of in vitro experiments showed that CoFe 2 O 4 @Methionine could significantly reduce cancer in the cell model. These results demonstrate that core-shell nanoparticles were prepared that are biocompatible and have potential use as drug delivery. Nanoparticle ; Nuclear chemistry ; Fourier transform infrared spectroscopy ; Chemistry ; In vitro ; Cytotoxicity ; Particle size ; Drug delivery ; Materials science ; Chemical engineering ; Nanotechnology ; Biochemistry ; Physical chemistry Nanoparticle-Based Drug Delivery
Authors: Lingjun Wu | Wei Wang | Haitao Zhao | Libo Gao | Jibao Lu | Rong Sun
Source: Frontiers in Materials (article, 2021-11-24)
Field: Materials Science — Materials Chemistry

A Molecular Dynamics Simulation Study: The Inkjet Printing of Graphene Inks on Polyimide Substrates Inkjet printing-based 2D materials for flexible electronics have aroused much interest due to their highly low-cost customization and manufacturing resolution. However, there is a lack of investigation and essential understanding of the surface adhesion affected by the printing parameters at the atomic scale. Herein, we conducted a systematic molecular dynamics simulation investigating the inkjet printing of graphitic inks on polyimide substrates under various conditions. Simulations under different temperatures, inkjet velocities, and mechanical loadings such as pressure and deformation are performed. The results show that the best adhesion is achieved in the plasma-modified polyimide/graphene-oxide (mPI/GO) interfacial system (the interaction energy (E in ) between mPI and GO is ca. 1.2 times than with graphene). The adhesion strength decreases with increasing temperature, and higher inkjet velocities lead to both larger impact force as well as interfacial fluctuation, while the latter may result in greater interfacial instability. When loaded with pressure, the adhesion strength reaches a threshold without further improvement as continuing compacting of polymer slabs can hardly be achieved. The detachment of the interfaces was also explored and mPI/GO shows better resistance against delamination. Hopefully, our simulation study paves the way for future inkjet printing-based manufacturing of graphene-based flexible electronics. Graphene ; Polyimide ; Materials science ; Inkwell ; Adhesion ; Nanotechnology ; Delamination (geology) ; Molecular dynamics ; Flexible electronics ; Oxide ; Composite material ; Inkjet printing ; Adhesive ; Electronics ; Polymer ; Layer (electronics) ; Electrical engineering ; Chemistry Graphene research and applications ; Advanced Sensor and Energy Harvesting Materials ; Nanomaterials and Printing Technologies
Authors: Yuko Ikeda | Michihiro Okuyama | Yukihito Nakazawa | Tomohiro Oshiyama | Kimito Funatsu
Source: Journal of Computer Chemistry Japan -International Edition (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Materials Informatics Approach to Predictive Models for Elastic Modulus of Polypropylene Composites Reinforced by Fillers and Additives Advanced processes are useful when developing polymer composites because there are an enormous number of possible combinations of fillers and additives to realize polymers with desired properties. Materials informatics is a data-driven approach to find novel materials or a suitable combination of materials from material data sheets. Here, we used materials informatics to construct a predictive model for the elastic modulus of polypropylene composites. To apply materials informatics to existing experimental data, we described explanatory variables by a combination of 0 and 1 representing polypropylene, or by the content ratio of filler and additive, without using materials property data. We constructed a predictive model for the elastic modulus of polypropylene composites using a partial least square regression model with dummy variables. To validate the predictive model, comparisons were made between measured and predicted elastic moduli for eight new polypropylene composites. The residual was less than 300 MPa for the range 1,000–3,000 MPa. We improved the accuracy of the prediction for composites with high filler content ratio by applying a nonlinear support vector regression model. The predictive model is therefore useful for identifying suitable combinations of polypropylene, filler and additive to achieve a desired elastic modulus. Polypropylene ; Materials science ; Composite material ; Filler (materials) ; Elastic modulus ; Materials informatics ; Health informatics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Susi Lehtola | Antti J. Karttunen
Source:  (preprint, 2021-08-12)
Field: Chemistry — Physical and Theoretical Chemistry

Free and Open Source Software for Computational Chemistry Education Long in the making, computational chemistry for the masses [J. Chem. Educ. 1996, 73, 104] is finally here. We point out the existence of a variety of free and open source software (FOSS) packages for computational chemistry that offer a wide range of functionality all the way from approximate semiempirical calculations with tight-binding density functional theory to sophisticated ab initio wave function methods such as coupled-cluster theory, both for molecular and for solid-state systems. By their very definition, FOSS packages allow usage for whatever purpose by anyone, meaning they can also be used in industrial applications without limitation. Also, FOSS software has no limitations to redistribution in source or binary form, allowing their easy distribution and installation by third parties. Many FOSS scientific software packages are available as part of popular Linux distributions, and other package managers such as pip and conda. Combined with the remarkable increase in the power of personal devices—which rival that of the fastest supercomputers in the world of the 1990s—a decentralized model for teaching computational chemistry is now possible, enabling students to perform reasonable modeling on their own computing devices, in the bring your own device (BYOD) scheme. In addition to the programs’ use for various applications, open access to the programs’ source code also enables comprehensive teaching strategies, as actual algorithms’ implementations can be used in teaching. We discuss the availability and use of various FOSS quantum chemistry packages and demonstrate what kinds of calculations are feasible with these programs, assuming only extremely modest computational resources. Our examples confirm that FOSS software enables decentralized approaches to computational chemistry education within the BYOD scheme, affording a democratization of the science of computational chemistry as well. Computer science ; Software ; Computational science ; Source code ; Theoretical computer science ; Implementation ; Coupled cluster ; Software engineering ; Programming language ; Physics ; Quantum mechanics ; Molecule Various Chemistry Research Topics ; Scientific Computing and Data Management ; Distributed and Parallel Computing Systems
Authors: Adam F. Sapnik | Irene Bechis | Alice M. Bumstead | Timothy Johnson | Philip A. Chater | David A. Keen | Kim E. Jelfs | Thomas D. Bennett
Source: ChemRxiv (preprint, 2021-11-17)
Field: Chemistry — Inorganic Chemistry

Multivariate Analysis of Disorder in Metal–Organic Frameworks The rational design of disordered frameworks is an appealing route to target functional materials. However, intentional realisation of such materials relies on our ability to readily characterise and quantify structural disorder. Here, we use multivariate analysis of pair distribution functions to fingerprint and quantify the disorder within a series of compositionally identical metal–organic frameworks, possessing different crystalline, disordered, and amorphous structures. We find this approach can provide powerful insight into the kinetics and mechanism of structural collapse that links these materials. Our methodology is also extended to a very different system, namely the melting of a zeolitic imidazolate framework, to demonstrate the potential generality of this approach across many areas of disordered structural chemistry. Generality ; Multivariate statistics ; Metal-organic framework ; Amorphous solid ; Zeolitic imidazolate framework ; Computer science ; Nanotechnology ; Materials science ; Biological system ; Chemistry ; Machine learning ; Crystallography ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Shuo Zhou | Xun Xu | Nan Ma | F. Jung | Andreas Lendlein
Source: MRS Advances (article, 2021-10-13)
Field: Chemistry — Spectroscopy

Prediction of the epichlorohydrin derived cytotoxic substances from the eluent of poly(glycerol glycidyl ether) films Abstract Glycerol-based epoxy networks have great potential for surface functionalization, providing anti-microbial and protein repellant function. However, the synthesis of glycerol glycidyl ether (GGE) monomer often requires excessive epichlorohydrin (ECH). ECH derived organochloride containing byproducts from monomer production maybe present in the eluent of the polymer networks prepared by cationic ring-opening polymerization. Here, the cytotoxicity analysis revealed cell damages in contact with the polyGGE eluent. The occurrence of organochlorides, which was predicted based on the data from high-performance liquid chromatography/electrospray ionization mass spectrometry, as confirmed by a constant chloride level in GGE and polyGGE, and by a specific peak of C–Cl in infrared spectra of GGE. The resulting polyGGE was densely crosslinked, which possibly contribute to the trapping of organochlorides. These results provide a valuable information for exploring the toxins leaching from polyGGE and propose a feasible strategy for minimizing the cytotoxicity via reducing their crosslink density. Graphic abstract The eluent of poly(glycerol glycidyl ether) (polyGGE) films impaired the viability and metabolic activity of L-929 cells due to the organochloride byproducts or epichlorohydrin precursors originating from the GGE monomer, which was predicted based on the data from high-performance liquid chromatography/electrospray ionization mass spectrometry (HPLC–ESI–MS) and confirmed by chloride content analysis and attenuated total reflection fourier transform infrared (ATR-FT-IR) spectroscopy. Epichlorohydrin ; Column chromatography ; Monomer ; Materials science ; Electrospray ionization ; Polymerization ; Gel permeation chromatography ; Mass spectrometry ; Chromatography ; Organic chemistry ; Polymer chemistry ; Polymer ; Chemistry Mass Spectrometry Techniques and Applications ; Microplastics and Plastic Pollution ; biodegradable polymer synthesis and properties
Authors: Xiaoyan Song | Kai Guo | Hao Lu | Dong Liu | Fawei Tang
Source: Journal of Materials Informatics (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Modelling of phase stability: integrating computational materials science and materials informatics Journal of Materials Informatics (JMI) is an international peer-reviewed, open access journal. The journal provides a platform for presentation, publication, and exchange of materials informatics research, seeking to break down barriers among materials science and engineering, data science and engineering, and artificial intelligence. The journal also provides a venue for presenting innovative approaches to overcome the difficulties in unraveling the complexity of data associated with numerous factors including noise, uncertainty, and small amount, to achieve the quantum jump from data to knowledge. Materials informatics ; Informatics ; Stability (learning theory) ; Data science ; Computer science ; Big data ; Health informatics ; Management science ; Artificial intelligence ; Engineering informatics ; Systems engineering ; Machine learning ; Engineering ; Data mining Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques