jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tao Ji | Ze Xu | Ying Wang | Xingchen Lu | Yuqian Zhu | Qingli Huang | Quanshuang Li | Shibao Li
Source: Rare Metals (article, 2026-01-11)
Field: Materials Science — Materials Chemistry

Colorimetric and Photothermal Hollow Nanocomposite Based on Silver‐Seeded Prussian Blue Analog for Dopamine Monitoring and Photothermal Sterilization ABSTRACT Multi‐element Prussian blue analog (PBA) nanocomposites have attracted wide attention for their superior characteristics to single‐functional nanomaterials in extensive applications of diagnosis and therapy. In this study, a novel hollow nanocomposite was designed and fabricated by embedding S 2− and Ag nanoparticles into a NiCuMnFe (PBA). The introduction S 2− and Ag nanoparticles greatly enhanced the catalytic and photothermal performance of multi‐element NiCuMnFe PBA. Based on its versatility of outstanding performance, a rapid and sensitive colorimetric detection platform for dopamine (DA), with a limit of detection (LOD) of 0.567 μM was constructed, and an efficient antibacterial nanoplatform was obtained with highly effective antibacterial performance (100% inhibition rate of bacteria under optimal conditions). This work provides a feasible reference for the reasonable design of multifunctional nanocomposites in biosensor and medical treatment applications. Prussian blue ; Photothermal therapy ; Nanocomposite ; Nanomaterials ; Nanoparticle ; Detection limit Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics ; Carbon and Quantum Dots Applications
Authors: Prianka Saha | Md. Mahiuddin
Source: Micro & Nano Letters (article, 2026-01-01)
Field: Chemistry — Organic Chemistry

Facile and Green Synthesis of Bismuth Nanoparticles for the Catalytic Reduction of 4‐Nitrophenol ABSTRACT The green pathway is considered an alternative to the conventional chemical method for synthesising metal nanoparticles. This study demonstrates a facile biogenic synthesis of bismuth nanoparticles (BiNPs) using Piper chaba stem extract as a reducing and capping source. Synthesised BiNPs were characterised using various analytical tools. XRD analysis confirmed the formation of crystalline BiNPs. FTIR analysis identified the functional groups from P. chaba extract involved in the BiNPs stabilisation process, while zeta potential measurement confirmed their stabilisation. The TEM image revealed spherical nanoparticles with an average size of 15 nm. EDX analysis showed bismuth as the dominant element, with traces of carbon and oxygen from organic moieties on the surface of BiNPs. TGA also supports the existence of phytochemicals with BiNPs and good thermal stability up to 800°C, supporting its suitability for high‐temperature applications. The catalytic activity of BiNPs in reducing 4‐nitrophenol (4‐NP) to 4‐aminophenol (4‐AP) with NaBH 4 showed rapid conversion of 4‐NP to 4‐AP within 22 min, with a rate constant of 6.77 s −1 g −1 , demonstrating the excellent catalytic efficiency of BiNPs. Following green chemistry principles, these findings suggest that P. chaba ‐mediated BiNPs can serve as highly effective nanocatalysts for various chemical transformations and environmental purification processes. Bismuth ; Catalysis ; Nanomaterial-based catalyst ; Zeta potential ; Fourier transform infrared spectroscopy ; Nanoparticle ; Thermal stability ; Carbon fibers Nanomaterials for catalytic reactions ; Catalysis for Biomass Conversion ; Catalytic Cross-Coupling Reactions
Authors: Jagjeet Singh | Riyaz Ahmed
Source: International Journal of Drug Delivery Technology (article, 2026-03-25)
Field: Chemistry — Organic Chemistry

Heterogeneous Catalysis: A Tool for the Synthesis of Natural Product Analogues Natural products have historically served as a rich source of biologically active molecules for pharmaceutical and chemical research. However, limitations in natural extraction and structural complexity often restrict their direct application. The development of catalytic strategies has therefore become essential for synthesising natural product analogues with improved biological and physicochemical properties. This review aims to highlight recent advances in heterogeneous catalytic systems used for the synthesis and modification of natural product analogues, emphasising their efficiency, selectivity, and relevance to sustainable chemical synthesis. Recent literature on heterogeneous catalysis was analysed to examine catalytic strategies such as hydrogenation, oxidation, carbon–carbon bond formation, and multicomponent reactions. Emerging catalytic platforms, including single-atom catalysts, metal–organic frameworks, nanostructured catalysts, and photocatalytic systems, were also evaluated. In addition, green chemistry approaches such as solvent-free reactions, microwave-assisted catalysis, and flow chemistry were considered for their role in sustainable synthesis. The analysis indicates that heterogeneous catalysts provide significant advantages in catalyst recyclability, reaction efficiency, and environmental compatibility. Advances in catalyst design have enabled improved selectivity and catalytic performance in complex organic transformations relevant to natural product analogue synthesis. Heterogeneous catalysis offers a promising pathway for developing efficient and sustainable synthetic methodologies. Continued innovation in catalyst design and integration of modern computational tools will further enhance the synthesis of structurally complex and biologically relevant natural product derivatives. Natural product ; Catalysis ; Heterogeneous catalysis ; Product (mathematics) ; Natural (archaeology) ; Flow chemistry Multicomponent Synthesis of Heterocycles ; Asymmetric Hydrogenation and Catalysis ; Catalytic C–H Functionalization Methods
Authors: Nurul Athikah Azizan | Norhaslinda Nasuha | Suriati Sufian | Hawaiah Imam Maarof | W. Z. W. Ismail
Source: Journal of Engineering and Applied Science (article, 2026-01-05)
Field: Chemistry — Inorganic Chemistry

Sustainable synthesis of iron-sludge derived MOFs : solvent-directed optimization for enhanced methylene blue degradation Abstract The sustainable production of advanced materials from industrial waste becomes a cost-efficient and environmentally friendly strategy for functional catalysts. In this study, an Fe-rich industrial byproduct of iron metal sludge (IMS) was employed as a precursor in the development of conventional iron-based MOFs fixed with 1,3,5-benzenetricarboxylic acid (BTC) ligand but employing N, N-dimethylformamide (DMF) as solvent and modulator. The IMS-MOFs, prepared based on the different ratios of IMS: BTC: DMF, were systematically tested by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy with energy-dispersive X-ray analysis (SEM-EDX), and cyclic voltammetry (CV) as well as electrochemical impedance spectroscopy (EIS) measurements coupled with Methylene Blue degradation tests. When the ratio of DMF was decreased, not much linker could coordinate and the crystallinity is weak; however, when larger amounts of DMF were added to the mixture, a more integral framework with better structural order and electrochemical activity could be obtained. With the optimal 1:2:400 ratio, the obtained IMS-MOFs owned more regular crystallinity and promoted charge transfer process, providing the maximum MB degradation efficiency. These results highlight the importance of DMF in controlling MOF growth and indicate that IMS can be a potential waste-derived precursor for green applications. Crystallinity ; Methylene blue ; Dielectric spectroscopy ; Cyclic voltammetry ; Fourier transform infrared spectroscopy ; Degradation (telecommunications) ; Electrochemistry ; Linear sweep voltammetry Metal-Organic Frameworks: Synthesis and Applications ; Environmental remediation with nanomaterials ; Magnesium Oxide Properties and Applications
Authors: André L. A. Marinho | Marie-Laure David | Noé Condé | Sophie Morisset | Christine Canaff | Patrick Chartier | Olivier Heintz | A. Krystianiak | Isidoro López | N. Guignard | M. Marteau | L. Pichon | D. Eyidi | Vincent Mauchamp | Stéphane Celerier
Source: ChemRxiv (article, 2026-04-07)
Field: Materials Science — Materials Chemistry

Straightforward assessment of structural disorder in Ti3C2Tx MXene using Raman spectroscopy. MXenes, a large family of two-dimensional transition metal carbides and nitrides, have attracted considerable interest owing to their physical-chemical properties. It is now well established that structural defects play a key role in these properties with possible significant improvement but also detrimental impact when uncontrolled. This issue is particularly important for MXenes due to the potentially harsh chemical etching conditions used for their exfoliation from the MAX phase precursors. Despite their important role, characterization defects remains highly challenging. Herein, we employ Raman spectroscopy as the primary probe to systematically investigate defect formation in carefully defect-engineered Ti3C2Tx MXenes derived from ion-implanted Ti3AlC2 MAX phases and exfoliated via a mild LiF/HCl route. Controlled ion implantation in the MAX precursor is used to generate well-defined defect densities, enabling direct correlation between implantation dose and Raman response in the resulting MXenes, together with improved exfoliation efficiency. In particular, we demonstrate the high sensitivity of the A1g mode at 206 cm—1, characteristic of Ti-C vibrational modes, to structural disorder with a peak broadening linearly scaling with defect density. This sensitivity is also confirmed for samples obtained with increasingly harsh etching conditions. Raman characterizations were corroborated by complementary spectroscopic analysis at various scales based on XPS/HAXPES and TEM-EELS. Together, these results demonstrate that Raman spectroscopy is a robust, accessible and reliable technique for mapping defects in MXenes, and provide a practical spectroscopic workflow for correlating vibrational, chemical and electronic signatures of disorder in Ti3C2Tx layers. MXenes ; Raman spectroscopy ; Exfoliation joint ; Characterization (materials science) ; Graphene ; Etching (microfabrication) ; MAX phases MXene and MAX Phase Materials ; 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices
Authors: Ron Shprints | Peter Holderrieth | Juno Nam | Rafael Gómez-Bombarelli | Tommi Jaakkola
Source: ArXiv.org (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

FragmentFlow: Scalable Transition State Generation for Large Molecules Transition states (TSs) are central to understanding and quantitatively predicting chemical reactivity and reaction mechanisms. Although traditional TS generation methods are computationally expensive, recent generative modeling approaches have enabled chemically meaningful TS prediction for relatively small molecules. However, these methods fail to generalize to practically relevant reaction substrates because of distribution shifts induced by increasing molecular sizes. Furthermore, TS geometries for larger molecules are not available at scale, making it infeasible to train generative models from scratch on such molecules. To address these challenges, we introduce FragmentFlow: a divide-and-conquer approach that trains a generative model to predict TS geometries for the reactive core atoms, which define the reaction mechanism. The full TS structure is then reconstructed by re-attaching substituent fragments to the predicted core. By operating on reactive cores, whose size and composition remain relatively invariant across molecular contexts, FragmentFlow mitigates distribution shifts in generative modeling. Evaluated on a new curated dataset of reactions involving reactants with up to 33 heavy atoms, FragmentFlow correctly identifies 90% of TSs while requiring 30% fewer saddle-point optimization steps than classical initialization schemes. These results point toward scalable TS generation for high-throughput reactivity studies. Initialization ; Generative grammar ; Generative model ; Scalability ; Invariant (physics) ; Molecule ; Set (abstract data type) ; Reactivity (psychology) Machine Learning in Materials Science ; Cyclization and Aryne Chemistry ; Computational Drug Discovery Methods
Authors: Guang Yang | Xinling Tang | Zhongkang Lin | Yulin Gu | Wei Hao | Yujie Du | Xiaoguang Wei
Source: ArXiv.org (article, 2026-03-17)
Field: Materials Science — Materials Chemistry

Tuning Cu/Diamond Interfacial Thermal Conductance via Nitrogen-Termination Engineering Cu-diamond composites are recognized as promising high-thermal-conductivity candidates for electronic cooling, offering tunable properties and competitive cost. However, their performance is significantly limited by the poor Cu/diamond interfacial thermal conductance (ITC). Here, we propose a nitrogen-termination strategy to tune the ITC of Cu/diamond interfaces and unravel atomistic mechanisms by which nitride interlayers tailor phonon transport. Based on the MACE machine-learning interatomic potential (MLIP) framework, we fine-tune the pre-trained MACE-MPA-0 foundation model by incorporating customized C-N-Cu training datasets. Through MLIP-driven lattice dynamics simulations, we demonstrate that an atomically flat N-termination on diamond enhances the ITC by 21% compared to the bare Cu/diamond interface. Mode-resolved phonon spectroscopy reveals that the LA phonons with frequency above 4 THz and wavevectors near Γ-X and Γ-U directions are selectively modulated by N-termination engineering. Analyses of local vibrational states and interfacial bonding further indicate that the N-termination on diamond tunes the interfacial heat conduction via surficial mass modification and bonding regulation, as evidenced by variations in LDOS overlap and COHP spectra. These findings open venues for tuning heat transfer across Cu/diamond interfaces via non-metallic modification, which avoids the graphitization issues associated with metallic coatings, and provide novel guidelines for upgrading the phonon-mediated heat transfer in Cu-diamond composites. Phonon ; Thermal conductivity ; Thermal conduction ; Diamond ; Thermal ; Molecular dynamics ; Heat transfer ; Conductance ; Lattice (music) Thermal properties of materials ; Aluminum Alloys Composites Properties ; Advanced ceramic materials synthesis
Authors: Caleb Simiyu Khaemba | Hongsong Feng | D. L. Chen | Chun‐Long Chen | Guo-Wei Wei
Source: Journal of Chemical Information and Modeling (article, 2026-02-17)
Field: Chemistry — Inorganic Chemistry

Commutative Algebra Modeling in Materials Science – A Case Study on Metal–Organic Frameworks (MOFs) Metal-organic frameworks (MOFs) are a class of important crystalline and highly porous materials whose hierarchical geometry and chemistry hinder interpretable predictions in materials properties. Commutative algebra is a branch of abstract algebra that has been rarely applied in data and material sciences. We introduce the first ever commutative algebra modeling and prediction in materials science. Specifically, category-specific commutative algebra (CSCA) is proposed as a new framework for MOF representation and learning. It integrates element-based categorization with multiscale algebraic invariants to encode both local coordination motifs and global network organization of MOFs. These algebraically consistent, chemically aware representations enable compact, interpretable, and data efficient modeling of MOF properties such as Henry's constants and uptake capacities for common gases. Compared to traditional geometric and graph-based approaches, CSCA achieves comparable or superior predictive accuracy while substantially improving interpretability and stability across data sets. By aligning commutative algebra with the chemical hierarchy, the CSCA establishes a rigorous and generalizable paradigm for understanding structure and property relationships in porous materials and provides a nonlinear algebra-based framework for data-driven material discovery. Interpretability ; Commutative property ; Algebra over a field ; Representation (politics) ; Algebraic structure ; Algebraic number ; Algebraic geometry ; Stability (learning theory) ; Class (philosophy) Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Matrix Theory and Algorithms
Authors: Hao Jiang | Yue Xing | Zhifeng Ma | Guangjin Fan | Zhongwei Liu | Song Yang (162335) | Lin Cai
Source: Nature Communications (article, 2026-03-20)
Field: Materials Science — Materials Chemistry

Copper single-atom nanozyme with intelligent capture and photo-enhanced activity for controlling plant bacterial diseases Nanozymes hold promise for controlling plant bacterial diseases, but conventional ones suffer from low bacterial affinity, inefficient enzyme-like activity, and thus poor antibacterial efficacy. Here, we report a photo-enhanced copper single-atom (CuSA)-loaded ZnS@MoS<sub>2</sub> nanozyme with high affinity and efficient peroxidase (POD)-like activity. CuSA-loaded ZnS@MoS<sub>2</sub> exhibits higher efficacies against bacterial speck and bacterial wilt diseases in tomatoes, surpassing the commercial thiodiazole copper by 13.33% and 52.77%, respectively. Mechanistically, it catalyzes H<sub>2</sub>O<sub>2</sub> to generate toxic hydroxyl radicals (·OH) via POD-like activity; near-infrared irradiation boosts this activity by lowering activation energy and accelerating mass transfer. Density functional theory (DFT) calculations reveal that CuSA-loaded ZnS@MoS<sub>2</sub> captures bacteria via Metal-O-P bonds on cell surfaces, reducing ·OH short-range quenching to enhance efficacy. This SA nanozyme design, integrating intelligent capture and photo-enhanced activity, offers an insight for plant bacterial disease control. Bacteria ; Copper ; Bacterial protein Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics ; Nanocluster Synthesis and Applications
Authors: Matthew S Johnson | David H. Bross | Lars L. Schaaf | Álvaro Vázquez‐Mayagoitia | Gábor Csányi | Judit Zádor
Source: ChemRxiv (article, 2026-02-23)
Field: Materials Science — Materials Chemistry

Fusing Delta Learning and Machine Learned Interatomic Potentials for Efficient High Throughput Calculation of Surface Kinetic Parameters Heterogeneous catalysis is critical in most industrial chemical processes. Microkinetic models can be used to greatly facilitate optimization of catalyst design and process conditions, but require thermochemical and kinetic parameters for all relevant species 1 and reactions. Our software Pynta enables fully automated calculation of thermochemical and kinetic parameters, however, the computational cost of density functional theory (DFT) calculations makes it difficult to calculate kinetic parameters at scale. In this work, we combine finetuning of the MACE multi-head v0 model with graph-based delta learning of stationary points using subgraph isomorphic decision trees (SIDT). We first generate a target chemical space on Pt111 by using the Reaction Mechanism Generator (RMG) software to generate all possible surface reactions between a set of 153 chemically adsorbed species containing C,H,N, and O. Applying Pynta to this reaction set we are able to generate geometries for many gas phase species, adsorbates and transition states in this chemical space allowing us to efficiently sample near stationary points for finetuning and stationary points for delta learning. In particular, we adapt Pynta's harmonically force saddle point search (HFSP) algorithm to enable efficient and reliable sampling of near transition state points. With this training data we show that SIDT-driven delta learning of the foundation MLIP alone can achieve similar accuracies on enthalpies to foundation model finetuning approaches. However, we show that by combining the two approaches into one framework we are able to significantly improve our accuracies over either approach allowing us to achieve 0.07 eV MAE relative to DFT on test barrier heights. Applying this framework across all reactions in our chemical space, we are able to obtain near DFT accuracy rate coefficients for 3111 reactions on Pt(111). Chemical space ; Kinetic energy ; Saddle point ; Software ; Density functional theory ; Stationary point ; Surface (topology) ; Throughput ; Set (abstract data type) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Thompson Izuagie | Daniel Lebbie
Source: RSC Advances (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

<sup>17</sup> O NMR spectroscopy in polyoxometalate chemistry: advances, challenges, and applications in structure and catalysis <sup>17</sup>O Nuclear Magnetic Resonance (NMR) spectroscopy has become an increasingly valuable technique for investigating the structure, dynamics, and reactivity of polyoxometalates (POMs), a diverse class of metal-oxygen clusters with broad applications in catalysis, energy storage, and materials science. The oxygen framework of POMs plays a very important role in dictating their physical and chemical properties, making direct probing of oxygen environments essential. However, the quadrupolar nature and low natural abundance (0.037%) of <sup>17</sup>O nuclei impose significant experimental challenges, including low sensitivity and broad line shapes. Recent methodological breakthroughs such as the development of ultra-high-field NMR instrumentation, the use of magic angle spinning (MAS) to minimize anisotropic broadening, and the implementation of dynamic nuclear polarisation (DNP) to boost signal intensity have greatly enhanced the resolution and feasibility of <sup>17</sup>O NMR studies. These advances now enable the differentiation of terminal, bridging, and internal oxygen sites, offering unique insights into structural isomerism, substitution effects, and protonation states in various POM archetypes including Lindqvist, Keggin, and Dawson structures. Beyond structural assignments, <sup>17</sup>O NMR has provided mechanistic understanding of catalytic processes by tracking oxygen participation in redox transformations and proton-coupled electron transfer. When integrated with computational approaches such as density functional theory (DFT) and artificial intelligence (AI), <sup>17</sup>O NMR delivers predictive power for interpreting chemical shifts, quadrupolar parameters, and dynamic behaviour. This review consolidates recent progress, highlights case studies, and underscores the emerging role of <sup>17</sup>O NMR as a cornerstone for advancing POM chemistry at the interface of structural science, catalysis, and theoretical modeling. Polyoxometalate ; Magic angle spinning ; Nuclear magnetic resonance spectroscopy ; Protonation ; Nuclear magnetic resonance crystallography ; Chemical shift ; Density functional theory ; Catalysis Polyoxometalates: Synthesis and Applications ; Advanced NMR Techniques and Applications ; Zeolite Catalysis and Synthesis