jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Martin Z. Bazant
Source: Faraday Discussions (article, 2023-01-01)
Field: Chemistry — Electrochemistry

Unified quantum theory of electrochemical kinetics by coupled ion–electron transfer A general theory of coupled ion-electron transfer (CIET) is presented, which unifies Marcus kinetics of electron transfer (ET) with Butler-Volmer kinetics of ion transfer (IT). In the limit of large reorganization energy, the theory predicts normal Marcus kinetics of "electron-coupled ion transfer" (ECIT). In the limit of large ion transfer energies, the theory predicts Butler-Volmer kinetics of "ion-coupled electron transfer" (ICET), where the charge transfer coefficient and exchange current are connected to microscopic properties of the electrode/electrolyte interface. In the ICET regime, the reductive and oxidative branches of Tafel's law are predicted to hold over a wide range of overpotentials, bounded by the ion-transfer energies for oxidation and reduction, respectively. The probability distribution of transferring electron energies in CIET smoothly interpolates between a shifted Gaussian distribution for ECIT (as in the Gerischer-Marcus theory of ET) to an asymmetric, fat-tailed Meixner distribution centered at the Fermi level for ICET. The latter may help interpret asymmetric line shapes in x-ray photo-electron spectroscopy (XPS) and Auger electron spectroscopy (AES) for metal surfaces in terms of shake-up relaxation of the ionized atom and its image polaron by ICET. In the limit of large overpotentials, the theory predicts a transition to inverted Marcus ECIT, leading to a universal reaction-limited current for metal electrodes, dominated by barrierless quantum transitions. Uniformly valid, closed-form asymptotic approximations are derived that smoothly transition between the limiting rate expressions for ICET and ECIT for metal electrodes, using simple but accurate mathematical functions. The theory is applied to lithium intercalation in lithium iron phosphate (LFP) and found to provide a consistent description of the observed current dependence on overpotential, temperature and concentration. CIET theory thus provides a critical bridge between quantum electrochemistry and electrochemical engineering, which may find many other applications and extensions. Electron transfer ; Kinetics ; Ion ; Electrochemistry ; Electron ; Quantum ; Chemical physics ; Chemistry ; Materials science ; Physics ; Electrode ; Physical chemistry ; Quantum mechanics Electrochemical Analysis and Applications ; Photochemistry and Electron Transfer Studies ; Molecular Junctions and Nanostructures
Authors: Tiwa Yimyai | Daniel Crespy | Abdon Pena‐Francesch
Source: Advanced Functional Materials (article, 2023-03-06)
Field: Chemistry — Organic Chemistry

Self‐Healing Photochromic Elastomer Composites for Wearable UV‐Sensors Abstract Photochromic materials have recently received strong interest for the development of wearable ultraviolet (UV) detection technologies because they do not require electronic components, resulting in systems and devices that change color upon irradiation. However, their implementation in wearable technology has lightweight, compliance, and durability (especially under mechanical stress) requirements that are limited by the materials’ properties. Here, a self‐healing photochromic elastomer composite (photoPUSH) consisting of phosphomolybdic acid (PMA) in a self‐healing polyurethane dynamic network with reversible disulfide bonds (PUSH) is presented. The unique properties of the dynamic polymer matrix enable multiple complementary functions in the UV‐sensing composite: i) photochromism via electron donor groups without requiring additional dopants, ii) stretchability and durability via elastomeric properties, iii) healing of extreme mechanical damage via dynamic bonds, and iv) multimaterial integration via adhesive properties. PhotoPUSH composites exhibit excellent durability, tunable sensing range, and no loss of performance under mechanical stress and severe damage, as well as in underwater environments (waterproof). Leveraging these properties, soft, portable, multimaterial photoPUSH‐based UV‐sensing devices are developed for applications in environmental monitoring, packaging, and healthcare wearable technology (including skin‐mounted, textile‐mounted, and wristband devices) in challenging environments and tunable to different skin types. Materials science ; Elastomer ; Durability ; Self-healing ; Composite number ; Composite material ; Photochromism ; Adhesive ; Nanocomposite ; Dopant ; Self-healing hydrogels ; Nanotechnology ; Optoelectronics ; Doping ; Polymer chemistry Polydiacetylene-based materials and applications ; Photochromic and Fluorescence Chemistry ; Polymer composites and self-healing
Authors: Jason Y. C. Lim | Leonard Goh | Ken‐ichi Otake | Shermin S. Goh | Xian Jun Loh | Susumu Kitagawa
Source: Biomaterials Science (review, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Biomedically-relevant metal organic framework-hydrogel composites Metal organic frameworks (MOFs) are incredibly versatile three-dimensional porous materials with a wide range of applications that arise from their well-defined coordination structures, high surface areas and porosities, as well as ease of structural tunability due to diverse compositions achievable. In recent years, following advances in synthetic strategies, development of water-stable MOFs and surface functionalisation techniques, these porous materials have found increasing biomedical applications. In particular, the combination of MOFs with polymeric hydrogels creates a class of new composite materials that marries the high water content, tissue mimicry and biocompatibility of hydrogels with the inherent structural tunability of MOFs in various biomedical contexts. Additionally, the MOF-hydrogel composites can transcend each individual component such as by providing added stimuli-responsiveness, enhancing mechanical properties and improving the release profile of loaded drugs. In this review, we discuss the recent key advances in the design and applications of MOF-hydrogel composite materials. Following a summary of their synthetic methodologies and characterisation, we discuss the state-of-the-art in MOF-hydrogels for biomedical use - cases including drug delivery, sensing, wound treatment and biocatalysis. Through these examples, we aim to demonstrate the immense potential of MOF-hydrogel composites for biomedical applications, whilst inspiring further innovations in this exciting field. Composite material ; Metal ; Self-healing hydrogels ; Metal-organic framework ; Materials science ; Chemistry ; Polymer chemistry ; Metallurgy ; Organic chemistry ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Nanoplatforms for cancer theranostics ; Advanced Nanomaterials in Catalysis
Authors: Bowen Zheng | Felipe Lopes Oliveira | Rodrigo Neumann Barros Ferreira | M. Steiner | Hendrik F. Hamann | Grace X. Gu | Binquan Luan
Source: ACS Nano (article, 2023-03-08)
Field: Chemistry — Inorganic Chemistry

Quantum Informed Machine-Learning Potentials for Molecular Dynamics Simulations of CO<sub>2</sub>’s Chemisorption and Diffusion in Mg-MOF-74 Among various porous solids for gas separation and purification, metal-organic frameworks (MOFs) are promising materials that potentially combine high CO<sub>2</sub> uptake and CO<sub>2</sub>/N<sub>2</sub> selectivity. So far, within the hundreds of thousands of MOF structures known today, it remains a challenge to computationally identify the best suited species. First principle-based simulations of CO<sub>2</sub> adsorption in MOFs would provide the necessary accuracy; however, they are impractical due to the high computational cost. Classical force field-based simulations would be computationally feasible; however, they do not provide sufficient accuracy. Thus, the entropy contribution that requires both accurate force fields and sufficiently long computing time for sampling is difficult to obtain in simulations. Here, we report quantum-informed machine-learning force fields (QMLFFs) for atomistic simulations of CO<sub>2</sub> in MOFs. We demonstrate that the method has a much higher computational efficiency (∼1000×) than the first-principle one while maintaining the quantum-level accuracy. As a proof of concept, we show that the QMLFF-based molecular dynamics simulations of CO<sub>2</sub> in Mg-MOF-74 can predict the binding free energy landscape and the diffusion coefficient close to experimental values. The combination of machine learning and atomistic simulation helps achieve more accurate and efficient <i>in silico</i> evaluations of the chemisorption and diffusion of gas molecules in MOFs. Molecular dynamics ; Chemisorption ; Force field (fiction) ; Quantum ; Statistical physics ; Computer science ; Diffusion ; Molecule ; Materials science ; Chemical physics ; Nanotechnology ; Adsorption ; Computational chemistry ; Chemistry ; Physics ; Artificial intelligence ; Physical chemistry ; Thermodynamics ; Quantum mechanics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Yaoguang Zhai | Alessandro Caruso | Sigbjørn Løland Bore | Zhishang Luo | Francesco Paesani
Source: The Journal of Chemical Physics (article, 2023-02-02)
Field: Materials Science — Materials Chemistry

A “short blanket” dilemma for a state-of-the-art neural network potential for water: Reproducing experimental properties or the physics of the underlying many-body interactions? Deep neural network (DNN) potentials have recently gained popularity in computer simulations of a wide range of molecular systems, from liquids to materials. In this study, we explore the possibility of combining the computational efficiency of the DeePMD framework and the demonstrated accuracy of the MB-pol data-driven, many-body potential to train a DNN potential for large-scale simulations of water across its phase diagram. We find that the DNN potential is able to reliably reproduce the MB-pol results for liquid water, but provides a less accurate description of the vapor-liquid equilibrium properties. This shortcoming is traced back to the inability of the DNN potential to correctly represent many-body interactions. An attempt to explicitly include information about many-body effects results in a new DNN potential that exhibits the opposite performance, being able to correctly reproduce the MB-pol vapor-liquid equilibrium properties, but losing accuracy in the description of the liquid properties. These results suggest that DeePMD-based DNN potentials are not able to correctly "learn" and, consequently, represent many-body interactions, which implies that DNN potentials may have limited ability to predict the properties for state points that are not explicitly included in the training process. The computational efficiency of the DeePMD framework can still be exploited to train DNN potentials on data-driven many-body potentials, which can thus enable large-scale, "chemically accurate" simulations of various molecular systems, with the caveat that the target state points must have been adequately sampled by the reference data-driven many-body potential in order to guarantee a faithful representation of the associated properties. Computer science ; Artificial neural network ; Process (computing) ; Range (aeronautics) ; Scale (ratio) ; State (computer science) ; Liquid water ; Artificial intelligence ; Statistical physics ; Physics ; Algorithm ; Aerospace engineering ; Thermodynamics ; Engineering Machine Learning in Materials Science ; Protein Structure and Dynamics ; Quantum, superfluid, helium dynamics
Authors: Arthur Abinader Vasconcelos | Thomas Len | Alex de Oliveira | Ana Alice Farias da Costa | Allan Rodrigo da Silva Souza | Carlos Emmerson Ferreira da Costa | Rafael Luque | Geraldo Narciso da Rocha Filho | Renata Coelho Rodrigues Noronha | Luís Adriano Santos do Nascimento
Source: Applied Sciences (article, 2023-02-01)
Field: Chemistry — Inorganic Chemistry

Zeolites: A Theoretical and Practical Approach with Uses in (Bio)Chemical Processes This review provides a state-of-the-art summary of distributed zeolite technology, as well as identifying strategies to further promote the absorption of these materials in various areas of study. Zeolites are materials that can be synthesized or found in natural rock deposits a with a basic composition consisting in Al, Si, and O. Zeolite’s consideration as a future material is due to many facile synthesis methods to obtain different structures with variations in pore size, surface area, pore volume and physical properties. These methods are developed using the control of relevant synthesis parameters that influences structure formation, such as crystallization temperature, time of aging and/or crystallization, stoichiometric relationships between components of synthesis gel, pH of the medium, and in some cases the type of structure-directing agent. Each method will lead to geometric changes in the framework formation, making possible the formation of typical chemical bonds that are the fingerprint of any zeolitic structure (O-Si-O and Al-O-Si), forming typical acid sites that give specificity in zeolite and allows it to act as a nanoreactor. The specificity is a characteristic that in some cases depends on selectivity, a fundamental property derived of the porosity, mostly in processes that occur inside the zeolite. In processes outside the structure, the surface area is the main factor influencing this property. Moreover, there are many natural sources with adequate chemical composition to be used as precursors. Some of these sources are waste, minimizing the deposition of potential hazardous materials that can be recalcitrant pollutants depending on the environment. Besides its uses as a catalyst, zeolite serves as a support for many bioprocesses; therefore, this review aims to explain relevant aspects in chemical nature, physical properties, main methods of synthesis, main precursors used for synthesis, and relevant applications of zeolites in chemical catalysis and biological processes. Zeolite ; Crystallization ; Materials science ; Porosity ; Chemical engineering ; Nanoreactor ; Nanotechnology ; Mineralogy ; Chemistry ; Catalysis ; Organic chemistry ; Composite material ; Nanoparticle Zeolite Catalysis and Synthesis ; Mesoporous Materials and Catalysis ; Catalysis and Hydrodesulfurization Studies
Authors: Adedapo O. Adeola | Michelle Pains Duarte | Rafik Naccache
Source: Frontiers in Carbon (article, 2023-07-05)
Field: Chemistry — Organic Chemistry

Microwave-assisted synthesis of carbon-based nanomaterials from biobased resources for water treatment applications: emerging trends and prospects Carbon-based nanomaterials have drawn significant interest as desirable nanomaterials and composites for the adsorptive removal of various classes of pollutants from water owing to their versatile physicochemical properties. The underlying sorption mechanisms serve as the bedrock for the development of carbonaceous adsorbents for various target pollutants. Microwave-assisted synthesis can be regarded as a recent and well-advanced technique for the development of carbon-based nanomaterials, and the use of biobased materials/wastes/residues conforms with the concept of green and sustainable chemistry. For advancements in carbon-based functional nanomaterials and their industrial/field applications, it is essential to fully comprehend the sorption performance and the selective/non-selective interaction processes between the contaminants and sorbents. In this regard, research on the development of carbon-based nanomaterials for the adsorption of chemical contaminants, both organic and inorganic, in water has made considerable strides as discussed in this review. However, there are still several fundamental hurdles associated with microwave-assisted chemical synthesis and commercial/industrial scale-up applications in nano-remediation. The challenges, benefits, and prospects for further research and development of carbon-based nanomaterials/nanocomposites for the purification of water are also discussed. Nanomaterials ; Adsorption ; Carbon fibers ; Nanotechnology ; Environmental remediation ; Sorption ; Pollutant ; Materials science ; Environmental science ; Chemistry ; Contamination ; Organic chemistry Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal ; Graphene research and applications
Authors: Nurul Umairah M. Nizam | ‪Marlia M. Hanafiah‬ | Ebrahim Mahmoudi | Abdul Wahab Mohammad
Source: Scientific Reports (article, 2023-08-07)
Field: Materials Science — Materials Chemistry

Synthesis of highly fluorescent carbon quantum dots from rubber seed shells for the adsorption and photocatalytic degradation of dyes The potentials of biomass-based carbon quantum dot (CQD) as an adsorbent for batch adsorption of dyes and its photocatalytic degradation capacity for dyes which are congo red (CR) and methylene blue (MB) have been conducted in this study. The CQDs properties, performance, behaviour, and photoluminescence characteristics were assessed using batch adsorption experiments which were carried out under operating conditions including, temperature, pH and dosage. The morphological analysis revealed that CQDs are highly porous, uniform, closely aligned and multi-layered. The presence of hydroxyl, carboxyl and carbonyl functional groups indicated the significance of the oxygenated functional groups. Spectral analysis of photoluminescence for CQDs confirmed their photoluminescent quality by exhibiting high excitation intensity and possessing greenish-blue fluorescence under UV radiation. The removal percentage of the dyes adsorbed for both CR and MB dyes was 77% and 75%. Langmuir isotherm and pseudo-second-order models closely fitted the adsorption results. Thermodynamics analysis indicated that the adsorption process was exothermic and spontaneous, with excellent reusability and stability. The degradation efficiency of CQDs on both dyes was more than 90% under sunlight irradiation and obeyed the first-order kinetic model. These results demonstrated CQDs to be an excellent adsorbent and outstanding photocatalyst for organic dye degradation. Adsorption ; Photoluminescence ; Degradation (telecommunications) ; Photocatalysis ; Photochemistry ; Methylene blue ; Fluorescence ; Congo red ; Carbon fibers ; Langmuir adsorption model ; Chemical engineering ; Materials science ; Quantum yield ; Chemistry ; Catalysis ; Organic chemistry ; Composite material Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: Wenli Sun | Mohamad Hesam Shahrajabian | Spyridon Α. Petropoulos | Nazanin Shahrajabian
Source: Plants (review, 2023-06-28)
Field: Materials Science — Biomaterials

Developing Sustainable Agriculture Systems in Medicinal and Aromatic Plant Production by Using Chitosan and Chitin-Based Biostimulants Chitosan is illustrated in research as a stimulant of plant tolerance and resistance that promotes natural defense mechanisms against biotic and abiotic stressors, and its use may lessen the amount of agrochemicals utilized in agriculture. Recent literature reports indicate the high efficacy of soil or foliar usage of chitin and chitosan in the promotion of plant growth and the induction of secondary metabolites biosynthesis in various species, such as <i>Artemisia annua</i>, <i>Curcuma longa</i>, <i>Dracocephalum kotschyi</i>, <i>Catharanthus roseus</i>, <i>Fragaria</i> × <i>ananassa</i>, <i>Ginkgo biloba</i>, <i>Iberis amara</i>, <i>Isatis tinctoria</i>, <i>Melissa officinalis</i>, <i>Mentha piperita</i>, <i>Ocimum basilicum</i>, <i>Origanum vulgare</i> ssp. <i>Hirtum</i>, <i>Psammosilene tunicoides</i>, <i>Salvia officinalis</i>, <i>Satureja isophylla</i>, <i>Stevia rebaudiana</i>, and <i>Sylibum marianum</i>, among others. This work focuses on the outstanding scientific contributions to the field of the production and quality of aromatic and medicinal plants, based on the different functions of chitosan and chitin in sustainable crop production. The application of chitosan can lead to increased medicinal plant production and protects plants against harmful microorganisms. The effectiveness of chitin and chitosan is also due to the low concentration required, low cost, and environmental safety. On the basis of showing such considerable characteristics, there is increasing attention on the application of chitin and chitosan biopolymers in horticulture and agriculture productions. Salvia officinalis ; Elicitor ; Foeniculum ; Basilicum ; Chitin ; Origanum ; Ocimum ; Biology ; Melissa officinalis ; Chitosan ; Botany ; Chemistry ; Essential oil ; Officinalis Nanocomposite Films for Food Packaging ; Plant Growth Enhancement Techniques ; Allelopathy and phytotoxic interactions
Authors: Tara K. Bartolec | Xabier Vázquez-Campos | Alexander Norman | Clement Luong | Marcus Johnson | Richard J. Payne | Marc R. Wilkins | Joel P. Mackay | Jason K. K. Low
Source: Proceedings of the National Academy of Sciences (article, 2023-04-18)
Field: Materials Science — Materials Chemistry

Cross-linking mass spectrometry discovers, evaluates, and corroborates structures and protein–protein interactions in the human cell Significant recent advances in structural biology, particularly in the field of cryoelectron microscopy, have dramatically expanded our ability to create structural models of proteins and protein complexes. However, many proteins remain refractory to these approaches because of their low abundance, low stability, or-in the case of complexes-simply not having yet been analyzed. Here, we demonstrate the power of using cross-linking mass spectrometry (XL-MS) for the high-throughput experimental assessment of the structures of proteins and protein complexes. This included those produced by high-resolution but in vitro experimental data, as well as in silico predictions based on amino acid sequence alone. We present the largest XL-MS dataset to date, describing 28,910 unique residue pairs captured across 4,084 unique human proteins and 2,110 unique protein-protein interactions. We show that models of proteins and their complexes predicted by AlphaFold2, and inspired and corroborated by the XL-MS data, offer opportunities to deeply mine the structural proteome and interactome and reveal mechanisms underlying protein structure and function. Mass spectrometry ; Chemistry ; Protein–protein interaction ; Computational biology ; Biochemistry ; Biology ; Chromatography Enzyme Structure and Function ; Mass Spectrometry Techniques and Applications ; Protein Structure and Dynamics