jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Linyang Li | Jialei Li | Yawei Yu | Yuxuan Song | Jia Li | Xiaobiao Liu | F. M. Peeters | Xin Chen | Guodong Liu
Source: arXiv (Cornell University) (preprint, 2024-01-07)
Field: Materials Science — Materials Chemistry

Rectangular carbon nitrides C4N monolayers with a zigzag buckled structure: Quasi-one-dimensional Dirac nodal lines and topological flat edge states Due to the flexibility of C and N atoms in forming different types of bonds, the prediction of new two-dimensional (2D) carbon nitrides is a hot topic in the field of carbon-based materials. Using first-principles calculations, we propose two C4N monolayers with a zigzag buckled (ZB) structure. The ZB C4N monolayers contain raised-C (raised-N) atoms with sp3 hybridization, different from the traditional 2D graphene-like carbon nitride materials with sp2 hybridization. Interestingly, the band structures of the ZB C4N monolayers exhibit quasi-one-dimensional (quasi-1D) Dirac nodal line that results from the corresponding quasi-1D structure of the zigzag carbon chains, which is essentially different from the more common ring-shaped nodal line. The quasi-1D Dirac nodal line exhibits the following features: (i) gapless Dirac points, (ii) varying Fermi velocity, and (iii) slightly curved band along the high-symmetry path. All these features are successfully explained by our proposed tight-binding model that includes interactions up to the third nearest-neighbor. The Fermi velocity of the 2D system can reach 105 m/s, which is promising for applications in high-speed electronic devices. The topological flat band structure determined by the Zak phase and band inversion of the corresponding 1D system is edge-dependent, which is corresponding to the Su-Schrieffer-Heeger model, providing to rich physical phenomena. Zigzag ; Monolayer ; Dirac (video compression format) ; Fermi level ; Condensed matter physics ; Graphene ; Gapless playback ; Physics ; Electronic band structure ; Topology (electrical circuits) ; Geometry ; Materials science ; Quantum mechanics ; Mathematics ; Nanotechnology ; Electron ; Combinatorics Graphene research and applications ; 2D Materials and Applications ; Boron and Carbon Nanomaterials Research
Authors: Ismail O. Amodu | Faith A. Olaojotule | Miracle N. Ogbogu | Oluwatobi A. Olaiya | Innocent Benjamin | Adedapo S. Adeyinka | Hitler Louis
Source: RSC Advances (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Adsorption and sensor performance of transition metal-decorated zirconium-doped silicon carbide nanotubes for NO<sub>2</sub> gas application: a computational insight Owing to the fact that the detection limit of already existing sensor-devices is below 100% efficiency, the use of 3D nanomaterials as detectors and sensors for various pollutants has attracted interest from researchers in this field. Therefore, the sensing potentials of bare and the impact of Cu-group transition metal (Cu, Ag, Au)-functionalized silicon carbide nanotube (SiCNT) nanostructured surfaces were examined towards the efficient detection of NO<sub>2</sub> gas in the atmosphere. All computational calculations were carried out using the density functional theory (DFT) electronic structure method at the B3LYP-D3(BJ)/def2svp level of theory. The mechanistic results showed that the Cu-functionalized silicon carbide nanotube surface possesses the greatest adsorption energies of -3.780 and -2.925 eV, corresponding to the adsorption at the o-site and n-site, respectively. Furthermore, the lowest energy gap of 2.095 eV for the Cu-functionalized surface indicates that adsorption at the o-site is the most stable. The stability of both adsorption sites on the Cu-functionalized surface was attributed to the small ellipticity (<i>ε</i>) values obtained. Sensor mechanisms confirmed that among the surfaces, the Cu-functionalized surface exhibited the best sensing properties, including sensitivity, conductivity, and enhanced adsorption capacity. Hence, the Cu-functionalized SiCNT can be considered a promising choice as a gas sensor material. Adsorption ; Materials science ; Density functional theory ; Silicon carbide ; Nanomaterials ; Transition metal ; Carbon nanotube ; Doping ; Zirconium ; Nanotechnology ; Chemical engineering ; Physical chemistry ; Computational chemistry ; Composite material ; Chemistry ; Catalysis ; Optoelectronics ; Organic chemistry Boron and Carbon Nanomaterials Research ; Gas Sensing Nanomaterials and Sensors ; ZnO doping and properties
Authors: Laura Wimberger | Gervase Ng | Cyrille Boyer
Source: ChemRxiv (preprint, 2024-02-22)
Field: Chemistry — Organic Chemistry

Light-Driven Polymer Recycling to Monomers and Small Molecules Only a small proportion of global plastic waste is recycled, of which most is mechanically recycled into lower quality materials. The alternative, chemical recycling, enables renewed production of pristine materials, but generally comes at a high energy cost, particularly for processes like pyrolysis. This review focuses on light-driven approaches for chemically recycling and upcycling plastic waste, with emphasis on reduced energy consumption and selective transformations not achievable with heat-driven methods. We focus on challenging to recycle backbone structures, mainly C‒C, which lack functional groups i.e. esters or amides, that facilitate chemical recycling e.g. by solvolysis. We discuss the use of light, either in conjunction with heat to drive depolymerization to monomers or via photocatalysis to transform polymers into valuable small molecules. The structural prerequisites for these approaches are outlined, highlighting their advantages as well as limitations. We conclude with an outlook, addressing key challenges, opportunities, and provide guidelines for future photocatalyst development. Depolymerization ; Monomer ; Polymer ; Pyrolysis ; Nanotechnology ; Solvolysis ; Photocatalysis ; Materials science ; Biochemical engineering ; Process engineering ; Waste management ; Chemistry ; Organic chemistry ; Polymer chemistry ; Engineering ; Catalysis ; Hydrolysis Advanced Polymer Synthesis and Characterization ; Photopolymerization techniques and applications ; Microplastics and Plastic Pollution
Authors: A. Hajeera Aseen | V Gowthami | P Jyolsna
Source: Journal of Integrated Science and Technology (article, 2024-12-20)
Field: Materials Science — Materials Chemistry

Green synthesis of NiO/ZnO nanocomposite using Vitex negundo leaf extract for antibacterial and anti-inflammatory activities The eco-friendly synthesis of Nickel Oxide/Zinc Oxide (NiO/ZnO) nanocomposite nanoparticles (NPs) using Vitex negundo leaf extract offers a sustainable and green approach to developing nanomaterials for biomedical applications. Three different molar ratios were synthesized: F1 (equal proportion of NiO and ZnO), F2 (high zinc, low nickel), and F3 (high nickel, low zinc). XRD analysis confirmed excellent crystallinity, with ZnO particle sizes of 14.35 nm (F1), 15.91 nm (F2), and 22.97 nm (F3), while NiO sizes ranged from 14.11 nm to 15.75 nm. UV-Vis spectroscopy revealed bandgap energies of 2.7 eV (F1), 2.75 eV (F2), and 2.55 eV (F3), highlighting compositional effects. SEM analysis demonstrated sheets morphology with minimal agglomeration, and EDX confirmed elemental composition. The F1 ratio exhibited superior antibacterial performance, showing a 1.5 mm inhibition zone against Staphylococcus aureus and Pseudomonas aeruginosa. Anti-inflammatory studies revealed concentration-dependent inhibition, with F1 achieving the highest inhibition (46.2% at 500 µg), though lower than diclofenac (70.3%). This study highlights the successful synthesis of NiO/ZnO nanocomposites, particularly F1, with significant antibacterial and anti-inflammatory potential, emphasizing the role of eco-friendly methods in advancing biomedical research. Zinc ; Crystallinity ; Nuclear chemistry ; Nanocomposite ; Materials science ; Antibacterial activity ; Nickel oxide ; Vitex negundo ; Nanoparticle ; Non-blocking I/O ; Particle size ; Nickel ; Chemical engineering ; Nanotechnology ; Chemistry ; Organic chemistry ; Metallurgy ; Composite material ; Bacteria ; Catalysis Nanoparticles: synthesis and applications ; Magnesium Oxide Properties and Applications ; Transition Metal Oxide Nanomaterials
Authors: Chloe J. Balhatchet | Jamie W. Gittins | Seung‐Jae Shin | Kangkang Ge | Xinyu Liu | Teedhat Trisukhon | Shivani Sharma | Thomas Kress | Pierre‐Louis Taberna | Patrice Simon | Aron Walsh | Alexander C. Forse
Source: ChemRxiv (preprint, 2024-04-18)
Field: Materials Science — Polymers and Plastics

Revealing Ion Adsorption and Charging Mechanisms in Layered Metal-Organic Framework Supercapacitors with Solid-State Nuclear Magnetic Resonance Conductive layered metal-organic frameworks (MOFs) have demonstrated promising electrochemical performances as supercapacitor electrode materials. The well-defined chemical structures of these crystalline porous electrodes facilitate structure-performance studies, however there is a fundamental lack in the molecular-level understanding of charge storage mechanisms in conductive layered MOFs. To address this, we employ solid-state nuclear magnetic resonance (NMR) spectroscopy to study ion adsorption in nickel 2,3,6,7,10,11-hexaiminotriphenylene, Ni3(HITP)2. In this system, we find that separate resonances can be observed for the MOF’s in-pore and ex-pore ions. The chemical shift of in-pore electrolyte is found to be governed by specific chemical interactions with the MOF functional groups, with this result supported by quantum-mechanics/molecular-mechanics (QM/MM) and density functional theory (DFT) calculations. Quantification of the electrolyte environments by NMR was also found to provide a proxy for electrochemical performance, which could facilitate the rapid screening of synthesised MOF samples. Finally, the charge storage mechanism was explored using a combination of ex-situ NMR and operando electrochemical quartz-crystal microbalance (EQCM) experiments. These measurements revealed that cations are the dominant contributor to charge storage in Ni3(HITP)2, with anions contributing only a minor contribution to the charge storage. Overall, this work establishes the methods for studying MOF-electrolyte interactions via NMR spectroscopy. Understanding how these interactions influence the charging storage mechanism will aid the design of MOF-electrolyte combinations to optimise the performance of supercapacitors, as well as other electrochemical devices including electrocatalysts and sensors. Electrolyte ; Supercapacitor ; Electrochemistry ; Density functional theory ; Solid-state nuclear magnetic resonance ; Materials science ; Adsorption ; Ion ; Chemical physics ; Electrode ; Chemistry ; Chemical engineering ; Nanotechnology ; Physical chemistry ; Nuclear magnetic resonance ; Computational chemistry ; Organic chemistry Conducting polymers and applications ; Metal-Organic Frameworks: Synthesis and Applications ; Supercapacitor Materials and Fabrication
Authors: Sk Jahir Abbas | Sabina Yesmin | Sandeepa Kulala Vittala | Nayim Sepay | Fangfang Xia | S. Ali | Wei-Chun Chang | Yao‐Ching Hung | Wen‐Lung Ma
Source: Metabolites (review, 2024-12-02)
Field: Chemistry — Organic Chemistry

Target Bioconjugation of Protein Through Chemical, Molecular Dynamics, and Artificial Intelligence Approaches Covalent modification of proteins at specific, predetermined sites is essential for advancing biological and biopharmaceutical applications. Site-selective labeling techniques for protein modification allow us to effectively track biological function, intracellular dynamics, and localization. Despite numerous reports on modifying target proteins with functional chemical probes, unique organic reactions that achieve site-selective integration without compromising native functional properties remain a significant challenge. In this review, we delve into site-selective protein modification using synthetic probes, highlighting both chemical and computational methodologies for chemo- and regioselective modifications of naturally occurring amino acids, as well as proximity-driven protein-selective chemical modifications. We also underline recent traceless affinity labeling strategies that involve exchange/cleavage reactions and catalyst tethering modifications. The rapid development of computational infrastructure and methods has made the bioconjugation of proteins more accessible, enabling precise predictions of structural changes due to protein modifications. Hence, we discuss bioconjugational computational approaches, including molecular dynamics and artificial intelligence, underscoring their potential applications in enhancing our understanding of cellular biology and addressing current challenges in the field. Bioconjugation ; Chemical biology ; Biopharmaceutical ; Posttranslational modification ; Chemistry ; Computational biology ; Nanotechnology ; Computer science ; Combinatorial chemistry ; Biochemistry ; Biology ; Materials science Click Chemistry and Applications ; Peptidase Inhibition and Analysis ; Chemical Synthesis and Analysis
Authors: Chi Zhang | Dmytro Antypov | Matthew J. Rosseinsky | Matthew S. Dyer
Source: Digital Discovery (article, 2024-01-01)
Field: Chemistry — Inorganic Chemistry

Accelerating metal–organic framework discovery <i>via</i> synthesisability prediction: the MFD evaluation method for one-class classification models The maximum fraction difference (MFD) method for evaluating OCC model performance without needing negative data for validation and its implementation in MOFs synthesisability prediction. Class (philosophy) ; Computer science ; Artificial intelligence ; Data mining Metal-Organic Frameworks: Synthesis and Applications ; Polyoxometalates: Synthesis and Applications ; Machine Learning in Materials Science
Authors: Chen‐Chen Er | Yee Wen Teh | Xin Ying Kong | Soungmin Bae | Yu Kumagai | Siang‐Piao Chai | Yee Sin Ang
Source: ChemRxiv (preprint, 2024-03-18)
Field: Materials Science — Materials Chemistry

The Big Problem in Surface Catalysis Modelling The existence of native point defects in crystalline solids are inevitable during synthesis. Such native defects will usually strongly impact the device performance of materials in catalysis applications. In recent years, the ability of first-principles approaches to accurately predict defect behavior has seen major progress. The underlying issue, as one would have guessed, is that these predictions of defect behavior and catalytic efficiency are extremely sensitive to the atomic structure of defect. Therefore, the proper identification of possible defect sites, their ground-state and metastable structures, and the correction scheme is crucial to resolving our understanding and control of defects’ role on catalytic performance. In this Comment, we aim to highlight recent advances of first-principles prediction for defect behavior while proposing a protocol for rigorous computational modelling. Surface (topology) ; Computer science ; Mathematics ; Geometry Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: Thijs Stuyver
Source: ChemRxiv (preprint, 2024-01-24)
Field: Materials Science — Materials Chemistry

TS-tools: Rapid and Automated Localization of Transition States based on a Textual Reaction SMILES Input Here, TS-tools is presented, a Python package facilitating the automated localization of transition states (TS) based on a textual reaction SMILES input. TS searches can either be performed at xTB or DFT level of theory, with the former yielding guesses at marginal computational cost, and the latter directly yielding accurate structures at greater expense. On a benchmarking dataset of mono- and bimolecular reactions, TS-tools reaches an excellent success rate of 95\% already at xTB level of theory. For tri- and multimolecular reaction pathways – which are typically not benchmarked when developing new automated TS search approaches, yet are relevant for various types of reactivity, cf. solvent- and autocatalysis and enzymatic reactivity – TS-tools retains its ability to identify TS geometries, though a DFT treatment becomes essential in many cases. Throughout the presented applications, a particular emphasis is placed on solvation-induced mechanistic changes, another issue that received limited attention in the automated TS search literature so far. Transition (genetics) ; Computer science ; Transition state ; Natural language processing ; Chemistry ; Catalysis ; Biochemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Chloe J. Balhatchet | Jamie W. Gittins | Seung‐Jae Shin | Kangkang Ge | Xinyu Liu | Teedhat Trisukhon | Shivani Sharma | Thomas Kress | Pierre‐Louis Taberna | Patrice Simon | Aron Walsh | Alexander C. Forse
Source: ChemRxiv (preprint, 2024-04-17)
Field: Materials Science — Polymers and Plastics

Revealing Ion Adsorption and Charging Mechanisms in Layered Metal-Organic Framework Supercapacitors with Solid-State Nuclear Magnetic Resonance Conductive layered metal-organic frameworks (MOFs) have demonstrated promising electrochemical performances as supercapacitor electrode materials. The well-defined chemical structures of these crystalline porous electrodes facilitate structure-performance studies, however there is a fundamental lack in the molecular level understanding of charge storage mechanisms in in conductive layered MOFs. To address this current lack of understanding of charge storage, we employ solid-state nuclear magnetic resonance (NMR) spectroscopy to study ion adsorption in nickel 2,3,6,7,10,11-hexaiminotriphenylene, Ni3(HITP)2. In this system, we find that separate resonances can be observed for the MOF’s in-pore and ex-pore ions. The chemical shift of in-pore electrolyte is found to be governed by specific chemical interactions with the MOF functional groups, with this result supported by quantum-mechanics/molecular-mechanics (QM/MM) and density functional theory (DFT) calculations. Quantification of the electrolyte environments by NMR was also found to provide a proxy for electrochemical performance, which could facilitate the rapid screening of synthesised MOF samples. Finally, the charge storage mechanism was explored using a combination of ex-situ NMR and operando electrochemical quartz-crystal microbalance (EQCM) experiments. These measurements revealed that cations are the dominant contributor to charge storage in Ni3(HITP)2, with anions contributing only a minor contribution to the charge storage. Overall, this work establishes the methods for studying MOF-electrolyte interactions via NMR spectroscopy. Understanding how these interactions influence the charging storage mechanism will aid the design of MOF-electrolyte combinations to optimise the performance of supercapacitors, as well as other electrochemical devices including electrocatalysts and sensors. Electrolyte ; Supercapacitor ; Electrochemistry ; Density functional theory ; Materials science ; Solid-state nuclear magnetic resonance ; Chemical physics ; Ion ; Electrode ; Chemical engineering ; Nanotechnology ; Chemistry ; Physical chemistry ; Nuclear magnetic resonance ; Computational chemistry ; Organic chemistry Conducting polymers and applications ; Metal-Organic Frameworks: Synthesis and Applications ; Supercapacitor Materials and Fabrication
Authors: Ziyang Yu | Wenbing Huang | Yang Liu
Source: arXiv (Cornell University) (preprint, 2024-08-27)
Field: Chemistry — Spectroscopy

Force-Guided Bridge Matching for Full-Atom Time-Coarsened Dynamics of Peptides Molecular Dynamics (MD) is crucial in various fields such as materials science, chemistry, and pharmacology to name a few. Conventional MD software struggles with the balance between time cost and prediction accuracy, which restricts its wider application. Recently, data-driven approaches based on deep generative models have been devised for time-coarsened dynamics, which aim at learning dynamics of diverse molecular systems over a long timestep, enjoying both universality and efficiency. Nevertheless, most current methods are designed solely to learn from the data distribution regardless of the underlying Boltzmann distribution, and the physics priors such as energies and forces are constantly overlooked. In this work, we propose a conditional generative model called Force-guided Bridge Matching (FBM), which learns full-atom time-coarsened dynamics and targets the Boltzmann-constrained distribution. With the guidance of our delicately-designed intermediate force field, FBM leverages favourable physics priors into the generation process, giving rise to enhanced simulations. Experiments on two datasets consisting of peptides verify our superiority in terms of comprehensive metrics and demonstrate transferability to unseen systems. Bridge (graph theory) ; Matching (statistics) ; Dynamics (music) ; Molecular dynamics ; Nanotechnology ; Physics ; Computer science ; Chemistry ; Mathematics ; Materials science ; Computational chemistry ; Biology ; Statistics Mass Spectrometry Techniques and Applications ; Chemical Synthesis and Analysis ; Supramolecular Self-Assembly in Materials
Authors: Jianbo Lin | Tomofumi Tada | Ai Koizumi | Masato Sumita | Koji Tsuda | Ryo Tamura
Source: ChemRxiv (preprint, 2024-10-14)
Field: Chemistry — Spectroscopy

Determination of stable proton configurations by black-box optimization using an Ising machine Stable proton configurations in solid-state materials are a prerequisite for the theoretical microscopic investigation of solid-state proton-conductive materials. However, a large number of initial atomistic configurations should be considered to find stable proton configurations, and relaxation calculations using the density functional theory approach are required for each initial configuration. Consequently, the determination of stable configurations is a difficult and time-consuming task. Furthermore, when the size of the simulation cells or the number of doped atoms increases, the number of initial configurations leads to a combinatorial explosion, rendering the computation infeasible. In this study, black-box optimization was combined with an Ising machine and density functional calculations to perform an efficient search for stable proton configurations. Scandium-doped barium zirconate, a typical high-proton conductive oxide, was selected as the model system. The Ising machine was able to rapidly select the initial atomistic configuration, ultimately leading to stable proton configurations after subsequent relaxation calculations. This optimization strategy should be able to solve various issues related to configuration optimization in solid-state materials, thereby promoting novel scientific discoveries. Black box ; Ising model ; Proton ; Computer science ; Physics ; Statistical physics ; Materials science ; Nuclear physics ; Artificial intelligence Mass Spectrometry Techniques and Applications ; Molecular spectroscopy and chirality ; Metabolomics and Mass Spectrometry Studies