jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mona Koder Hamid | Linda K. Månsson | Viktoriia Meklesh | Per Persson | Marie Skepö
Source: Frontiers in Molecular Biosciences (article, 2022-10-26)
Field: Materials Science — Surfaces, Coatings and Films

Molecular dynamics simulations of the adsorption of an intrinsically disordered protein: Force field and water model evaluation in comparison with experiments This study investigates possible structural changes of an intrinsically disordered protein (IDP) when it adsorbs to a solid surface. Experiments on IDPs primarily result in ensemble averages due to their high dynamics. Therefore, molecular dynamics (MD) simulations are crucial for obtaining more detailed information on the atomistic and molecular levels. An evaluation of seven different force field and water model combinations have been applied: (A) CHARMM36IDPSFF + CHARMM-modified TIP3P, (B) CHARMM36IDPSFF + TIP4P-D, (C) CHARMM36m + CHARMM-modified TIP3P, (D) AMBER99SB-ILDN + TIP3P, (E) AMBER99SB-ILDN + TIP4P-D, (F) AMBERff03ws + TIP4P/2005, and (G) AMBER99SB-disp + disp-water. The results have been qualitatively compared with those of small-angle X-ray scattering, synchrotron radiation circular dichroism spectroscopy, and attenuated total reflectance Fourier transform infrared spectroscopy. The model IDP corresponds to the first 33 amino acids of the N-terminal of the magnesium transporter A (MgtA) and is denoted as KEIF. With a net charge of +3, KEIF is found to adsorb to the anionic synthetic clay mineral Laponite<sup>®</sup> due to the increase in entropy from the concomitant release of counterions from the surface. The experimental results show that the peptide is largely disordered with a random coil conformation, whereas the helical content (α- and/or 3<sub>10</sub>-helices) increased upon adsorption. MD simulations corroborate these findings and further reveal an increase in polyproline II helices and an extension of the peptide conformation in the adsorbed state. In addition, the simulations provided atomistic resolution of the adsorbed ensemble of structures, where the arginine residues had a high propensity to form hydrogen bonds with the surface. Simulations B, E, and G showed significantly better agreement with experiments than the other simulations. Particularly noteworthy is the discovery that B and E with TIP4P-D water had superior performance to their corresponding simulations A and D with TIP3P-type water. Thus, this study shows the importance of the water model when simulating IDPs and has also provided an insight into the structural changes of surface-active IDPs induced by adsorption, which may play an important role in their function. Chemistry ; Molecular dynamics ; Polyproline helix ; Force field (fiction) ; Water model ; Adsorption ; Random coil ; Chemical physics ; Crystallography ; Small-angle X-ray scattering ; Circular dichroism ; Peptide ; Scattering ; Computational chemistry ; Physical chemistry ; Physics Polymer Surface Interaction Studies ; Proteins in Food Systems ; Hydrogels: synthesis, properties, applications
Authors: Michelle Åhlén | Francoise M. Amombo Noa | Lars Öhrström | Daniel Hedbom | Maria Strømme | Ocean Cheung
Source: Microporous and Mesoporous Materials (article, 2022-08-11)
Field: Chemistry — Inorganic Chemistry

Pore size effect of 1,3,6,8-tetrakis(4-carboxyphenyl)pyrene-based metal-organic frameworks for enhanced SF6 adsorption with high selectivity Anthropogenic greenhouse gas emission poses as serious threat to our environment and it is therefore of utmost importance that efficient systems are developed to mitigate these issues. SF6, in particular, has attracted more attention in recent years due to its global warming potential which severely exceeds that of CO2. In this study we present the SF6 sorption properties of four highly porous 1,3,6,8-tetrakis(4-carboxyphenyl)pyrene-based (TBAPy4−) metal-organic frameworks containing either ytterbium(III), thulium(III), cerium(III), or hafnium(IV). These MOFs can be synthesized with high crystallinity in as little as 5 h and were found to have good SF6 uptakes due to their suitable pore size. The SF6 uptake of the Yb-TBAPy MOF reached 2.33 mmol g−1 with high Henry's law SF6-over-N2 selectivity of ∼80 at 1 bar and 293 K. The TBAPy-MOFs were also found to have good chemical stability and good cyclic SF6 sorption stability with fast SF6 uptake. These TBAPy-MOFs possesses many of the properties desired for an efficient SF6 sorbent and may be suitable sorbents for further development, including sorbent processing for industrial applications. Sorbent ; Sorption ; Selectivity ; Crystallinity ; Adsorption ; Pyrene ; Metal-organic framework ; Materials science ; Ytterbium ; Chemical engineering ; Metal ; Chemistry ; Inorganic chemistry ; Organic chemistry ; Doping ; Catalysis Metal-Organic Frameworks: Synthesis and Applications ; Inorganic Fluorides and Related Compounds ; Membrane Separation and Gas Transport
Authors: Abdulrahman Masoud Alotaibi | Aznan Fazli Ismail
Source: International Journal of Environmental Research and Public Health (article, 2022-10-23)
Field: Chemistry — Inorganic Chemistry

Modification of Clinoptilolite as a Robust Adsorbent for Highly-Efficient Removal of Thorium (IV) from Aqueous Solutions The natural zeolite has been modified with sulphate and phosphate. The adsorption of thorium from the aqueous solutions by using the natural and modified zeolites has been investigated via a batch method. The adsorbent samples were characterized by X-ray Diffraction (XRD), N<sub>2</sub> adsorption-desorption (BET), Fourier transform infrared (FTIR), field emission scanning electron microscopy (FESEM), and energy dispersive X-ray spectroscopy (EDX). Modification of natural zeolite with sulphate and phosphate was found to increase its adsorption capacity of thorium but reduced its specific surface area (S<sub>BET</sub>). The adsorption experiments were expressed by Langmuir, Freundlich and Dubinin-Radushkevitch (D-R) isotherm models and the results of adsorption demonstrated that the adsorption of thorium onto the natural and modified zeolites correlated better with the Langmuir isotherm model than with the Freundlich isotherm model. The maximum adsorption capacity (Qo) was determined using the Langmuir isotherm model at 25 °C and was found to be 17.27, 13.83, and 10.21 mg/g for phosphate-modified zeolite, sulfate-modified zeolite, and natural zeolite, respectively. The findings of this study indicate that phosphate-modified zeolite can be utilized as an effective and low-cost adsorbent material for the removal of thorium from aqueous solutions. Zeolite ; Adsorption ; Freundlich equation ; Clinoptilolite ; Aqueous solution ; Langmuir adsorption model ; Fourier transform infrared spectroscopy ; Langmuir ; Thorium ; Nuclear chemistry ; Chemistry ; Desorption ; Phosphate ; Inorganic chemistry ; Materials science ; Chemical engineering ; Organic chemistry ; Uranium ; Catalysis ; Metallurgy Radioactive element chemistry and processing ; Chemical Synthesis and Characterization ; Clay minerals and soil interactions
Authors: Bernardo Patella | Tarun Narayan | Benjamin O’Sullivan | Robert J. Daly | Claudio Zanca | Pierre Lovera | Rosalinda Inguanta | Alan O’Riordan
Source: Electrochimica Acta (article, 2022-12-03)
Field: Chemistry — Electrochemistry

Simultaneous detection of copper and mercury in water samples using in-situ pH control with electrochemical stripping techniques Electrode ; Copper ; Stripping (fiber) ; Electrochemistry ; Analytical Chemistry (journal) ; Mercury (programming language) ; Chemistry ; Detection limit ; Electrolysis ; In situ ; Anodic stripping voltammetry ; Metal ions in aqueous solution ; Metal ; Inorganic chemistry ; Materials science ; Electrolyte ; Chromatography Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; Electrochemical sensors and biosensors
Authors: Hu Li | Kexue Han | Jinhua Shang | Weihai Cai | Minghao Pan | Donghui Xu | Can Du | Rui Zuo
Source: Sustainability (article, 2022-06-24)
Field: Chemistry — Inorganic Chemistry

Comparison of Adsorption Capacity and Removal Efficiency of Strontium by Six Typical Adsorption Materials The rapid development and application of nuclear technology have been accompanied by the production of large amounts of radioactive wastes, of which Sr is a typical nuclide. In this study, six typical materials with strong adsorption properties, namely activated carbon, kaolin, montmorillonite, bentonite, zeolite, and attapulgite, were selected. Their adsorption mechanisms were investigated by analyzing their adsorption isotherms, adsorption kinetics, micromorphologies, element contents, specific surface areas, crystal structures, and functional groups. The results showed that the adsorption efficiency of Sr by the six adsorbents can be ranked as zeolite, bentonite, attapulgite, montmorillonite, activated carbon, and kaolin, among which the maximum adsorption capacity of zeolite was 4.07 mg/g. Based on the adsorption kinetic and thermodynamic fitting results, the adsorption of Sr by zeolites, bentonite and attapulgite is consistent with Langmuir model, the pseudo-first-order and pseudo-second-order model, and the adsorption process of Sr (II) by montmorillonite, activated carbon and kaolinite is consistent with the Freundlich model and corresponds to non-uniform adsorption. The main mechanisms of the six materials are physical adsorption, ion exchange and complexation. In summary, zeolite, bentonite, and attapulgite, especially zeolite, are highly effective for the treatment of radioactive wastewater containing strontium and have great application value in the treatment of radioactive wastes. Adsorption ; Zeolite ; Bentonite ; Montmorillonite ; Strontium ; Kaolinite ; Freundlich equation ; Activated carbon ; Langmuir adsorption model ; Chemistry ; Langmuir ; Ion exchange ; Chemical engineering ; Radioactive waste ; Materials science ; Inorganic chemistry ; Nuclear chemistry ; Mineralogy ; Ion ; Organic chemistry ; Catalysis Radioactive element chemistry and processing ; Chemical Synthesis and Characterization ; Adsorption and biosorption for pollutant removal
Authors: Ji Li | Xikou He | Bin Xu | Zhengxin Tang | Caishun Fang | Gang Yang
Source: Materials (article, 2022-04-13)
Field: Materials Science — Materials Chemistry

Effect of Silicon on Dynamic/Static Corrosion Resistance of T91 in Lead–Bismuth Eutectic at 550 °C The 9-12% Cr ferritic-martensitic heat-resistant steel is the main candidate structural material for the Lead-cooled Faster Reactor. The lower Gibbs free energy change of Si oxide can promote the formation of a stable oxide layer, which can improve the corrosion resistance of the material. Therefore, it is of great significance to study the effect of silicon (Si) on the corrosion resistance of T91 steel in lead-bismuth eutectic (LBE). The corrosion resistance of T91 steel with Si contents of 0.5 wt.%, 1.3 wt.%, and 2.0 wt.%, both in dynamic and static LBE at 550 °C, was investigated. The microstructure was analyzed by X-ray diffraction (XRD), scanning electron microscope (SEM) and transmission electron microscope (TEM), while the oxide films were characterized by electron probe microanalysis (EPMA). Results show that the addition of Si is conducive to improving the corrosion resistance of T91 steel in LBE. T91 steel with high Si content has a thinner and more stable oxide film. The change of Si content can change the contact angle between the steel and LBE, and the contact angle is the largest when the Si content is 1.3 wt.%. The Si-rich oxide layer is usually located in the inner oxide layer, which promotes the formation of a Cr oxide layer located in the internal oxidation zone (IOZ). Si will not enter the precipitated phase, but only change the ferrite content. The oxidation model of T91 steel containing Si in LBE was also proposed. Materials science ; Eutectic system ; Corrosion ; Metallurgy ; Oxide ; Electron microprobe ; Microstructure ; Scanning electron microscope ; Silicon ; Austenite ; Composite material Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Fusion materials and technologies
Authors: Theddeus T. Akano | Chinemerem C. James
Source: Beni-Suef University Journal of Basic and Applied Sciences (article, 2022-12-12)
Field: Chemistry — Analytical Chemistry

An assessment of ensemble learning approaches and single-based machine learning algorithms for the characterization of undersaturated oil viscosity Abstract Background Prediction of accurate crude oil viscosity when pressure volume temperature (PVT) experimental results are not readily available has been a major challenge to the petroleum industry. This is due to the substantial impact an inaccurate prediction will have on production planning, reservoir management, enhanced oil recovery processes and choice of design facilities such as tubing, pipeline and pump sizes. In a bid to attain improved accuracy in predictions, recent research has focused on applying various machine learning algorithms and intelligent mechanisms. In this work, an extensive comparative analysis between single-based machine learning techniques such as artificial neural network, support vector machine, decision tree and linear regression, and ensemble learning techniques such as bagging, boosting and voting was performed. The prediction performance of the models was assessed by using five evaluation measures, namely mean absolute error, relative squared error, mean squared error, root mean squared error and root mean squared log error. Results The ensemble methods offered generally higher prediction accuracies than single-based machine learning techniques. In addition, weak single-based learners of the dataset used in this study (for example, SVM) were transformed into strong ensemble learners with better prediction performance when used as based learners in the ensemble method, while other strong single-based learners were discovered to have had significantly improved prediction performance. Conclusion The ensemble methods have great prospects of enhancing the overall predictive accuracy of single-based learners in the domain of reservoir fluid PVT properties (such as undersaturated oil viscosity) prediction. Machine learning ; Mean squared error ; Ensemble learning ; Artificial intelligence ; Computer science ; Artificial neural network ; Support vector machine ; Decision tree ; Random forest ; Boosting (machine learning) ; Pipeline (software) ; Mean squared prediction error ; Gradient boosting ; Algorithm ; Mathematics ; Statistics Petroleum Processing and Analysis ; Reservoir Engineering and Simulation Methods ; Oil and Gas Production Techniques
Authors: Samira Munkaila | Rabin Dahal | Manzili Kokayi | Tatyana Jackson | Bishnu Prasad Bastakoti
Source: The Chemical Record (review, 2022-07-11)
Field: Materials Science — Materials Chemistry

Hollow Structured Transition Metal Phosphates and Their Applications Hollow nanostructures of transition metal phosphate are of immense interest in the existing and evolving areas of technology, due to their high surface area, presence of hollow void, and easy tuning of compositions and dimensions. Emerging synthesis methods such as template-free methods, hard-templating, and soft-templating are discussed in this review. Applications of these hollow metal phosphates dominate in energy storage and conversions, with specific advantages as supercapacitor materials. Other applications, including drug delivery, water splitting, catalysis, and adsorption, are reviewed. Finally, additional perspectives on the progress of these nanostructures, and their existing challenges related to the current synthesis routes are covered. Therefore, with the strategic modifications of the unique properties of these hollow metal phosphates, broader application requirements are fulfilled. Nanotechnology ; Materials science ; Transition metal ; Nanostructure ; Supercapacitor ; Catalysis ; Chemistry ; Organic chemistry Polyoxometalates: Synthesis and Applications ; MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques
Authors: Edoardo Donadoni | Paulo Siani | Giulia Frigerio | Cristiana Di Valentin
Source: Nanoscale (article, 2022-01-01)
Field: Materials Science — Biomaterials

Multi-scale modeling of folic acid-functionalized TiO<sub>2</sub>nanoparticles for active targeting of tumor cells Strategies based on the active targeting of tumor cells are emerging as smart and efficient nanomedical procedures. Folic acid (FA) is a vitamin and a well-established tumor targeting agent because of its strong affinity for the folate receptor (FR), which is an overexpressed protein on the cell membranes of the tumor cells. FA can be successfully anchored to several nanocarriers, including inorganic nanoparticles (NPs) based on transition metal oxides. Among them, TiO<sub>2</sub> is extremely interesting because of its excellent photoabsorption and photocatalytic properties, which can be exploited in photodynamic therapy. However, it is not yet clear in which respects direct anchoring of FA to the NP or the use of spacers, based on polyethylene glycol (PEG) chains, are different and whether one approach is better than the other. In this work, we combine Quantum Mechanics (QM) and classical Molecular Dynamics (MD) to design and optimize the FA functionalization on bare and PEGylated TiO<sub>2</sub> models and to study the dynamical behavior of the resulting nanoconjugates in a pure water environment and in physiological conditions. We observe that they are chemically stable, even under the effect of increasing temperature (up to 500 K). Using the results from long MD simulations (100 ns) and from free energy calculations, we determine how the density of FA molecules on the TiO<sub>2</sub> NP and the presence of PEG spacers impact on the actual exposure of the ligands, especially by affecting the extent of FA-FA intermolecular interactions, which are detrimental for the targeting ability of FA towards the folate receptor. This analysis provides a solid and rational basis for experimentalists to define the optimal FA density and the more appropriate mode of anchoring to the carrier, according to the final purpose of the nanoconjugate. Folic acid ; Nanoparticle ; Nanotechnology ; Materials science ; Combinatorial chemistry ; Chemistry ; Medicine Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; Nanoparticles: synthesis and applications
Authors: Anand Narayanan Krishnamoorthy | Christian Wölke | Diddo Diddens | M. Maiti | Youssef Mabrouk | Peng Yan | Mariano Grünebaum | Martin Winter | Andreas Heuer | Isidora Cekić-Lasković
Source: Chemistry - Methods (article, 2022-06-10)
Field: Materials Science — Materials Chemistry

Data‐Driven Analysis of High‐Throughput Experiments on Liquid Battery Electrolyte Formulations: Unraveling the Impact of Composition on Conductivity** Abstract A specially designed high‐throughput experimentation facility, used for the highly effective exploration of electrolyte formulations in composition space for diverse battery chemistries and targeted applications, is presented. It follows a high‐throughput formulation‐characterization‐optimization chain based on a set of previously established electrolyte‐related requirements. Here, the facility is used to acquire large dataset of ionic conductivities of non‐aqueous battery electrolytes in the conducting salt‐solvent/co‐solvent‐additive composition space. The measured temperature dependence is mapped on three generalized Arrhenius parameters, including deviations from simple activated dynamics. This reduced dataset is thereafter analyzed by a scalable data‐driven workflow, based on linear and Gaussian process regression, providing detailed information about the compositional dependence of the conductivity. Complete insensitivity to the addition of electrolyte additives for otherwise constant molar composition is observed. Quantitative dependencies, for example, on the temperature‐dependent conducting salt content for optimum conductivity are provided and discussed in light of physical properties such as viscosity and ion association effects. Electrolyte ; Conductivity ; Molar conductivity ; Battery (electricity) ; Arrhenius equation ; Ionic conductivity ; Solvent ; Materials science ; Viscosity ; Chemistry ; Thermodynamics ; Chemical engineering ; Ion ; Electrode ; Organic chemistry ; Activation energy ; Physical chemistry ; Physics Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advanced Battery Materials and Technologies
Authors: Daniele Ongari | Leopold Talirz | Kevin Maik Jablonka | Daniel W. Siderius | Berend Smit
Source: Journal of Chemical & Engineering Data (article, 2022-02-17)
Field: Chemistry — Inorganic Chemistry

Data-Driven Matching of Experimental Crystal Structures and Gas Adsorption Isotherms of Metal–Organic Frameworks Porous metal–organic frameworks are a class of materials with great promise in gas separation and gas storage applications. Due to the high dimensional space of materials science and engineering, computational screening techniques have long been an important part of the scientific toolbox. However, a broad validation of molecular simulations in these materials is impeded by the lack of a connection between databases of gas adsorption experiments and databases of the atomic crystal structure of corresponding materials. This work aims to connect the gas adsorption isotherms of metal–organic frameworks collected in the NIST/ARPA-E Database of Novel and Emerging Adsorbent Materials to the corresponding crystal structures in the Cambridge Structural Database. With tens of thousands of isotherms and crystal structures reported to date, an automatic approach is needed to establish this link, which we describe in this paper. As a first application and consistency check, we compare the pore volume measured from low-temperature argon or nitrogen isotherms to the geometrical pore volume computed from the crystal structure. Overall, 545 argon or nitrogen isotherms could be matched to a corresponding crystal structure. We find that the pore volume computed via the two complementary methods shows acceptable agreement only in about 35% of these cases. We provide the subset of isotherms measured on these materials as a seed for a future and more complete reference data set for computational studies. Argon ; Adsorption ; Chemistry ; Volume (thermodynamics) ; Metal-organic framework ; Crystal (programming language) ; NIST ; Toolbox ; Sorption ; Crystal structure ; Thermodynamics ; Physical chemistry ; Organic chemistry ; Computer science ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Covalent Organic Framework Applications