jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jingyi Tan | Ye Tang | Zhao Bi | Guoqiang Dong | Miao Liu | Chenhui Yang
Source: Molecules (article, 2026-03-26)
Field: Materials Science — Materials Chemistry

Beyond Titanium Carbide: The Promise of Vanadium-Based MXenes for Aqueous Supercapacitors Aqueous supercapacitors are a class of crucial high-power, long-life, safe and reliable energy storage devices, with their performance fundamentally dependent on electrode materials. Two-dimensional (2D) vanadium-based MXenes, possessing rich multivalent redox activity and tunable layered structures, have emerged as one of highly promising electrode candidates, exhibiting significantly superior specific capacitance and pseudocapacitive properties compared to conventional Ti<sub>3</sub>C<sub>2</sub>T<i><sub>z</sub></i>. To overcome inherent limitations in conductivity and structural stability, this review summarizes strategies for regulating composition and microstructure through transition metal solid solution and medium-/high-entropy design. These approaches synergistically optimize electron conduction, expand ion migration pathways, and suppress electrode degradation, thereby comprehensively enhancing rate performance, cycle life, and energy density. This review systematically reveals the composition-structure-performance relationships, providing critical design insights and theoretical foundations for developing next-generation high-performance, long-life aqueous MXene-based supercapacitors. MXenes ; Supercapacitor ; Capacitance ; Electrode ; Energy storage ; Titanium carbide ; Aqueous solution ; Conductivity MXene and MAX Phase Materials ; Supercapacitor Materials and Fabrication ; Advancements in Battery Materials
Authors: Bo Tong | Tengda Du | Jinhong Du | Xin Yu | Jinmeng Tong | Jiajun Xu | Wanying Li | Zhibo Liu | Chen Li | Wencai Ren
Source: Nature Communications (article, 2026-03-18)
Field: Materials Science — Materials Chemistry

Multimodal ion-gated transistor based on 2D superionic conductor for in-memory computing in deep learning For neuromorphic computing, integrating multiply-accumulate operations and nonlinear activation within a single device can reduce latency and power consumption while improving computational efficiency. However, these operations impose conflicting requirements: multiply-accumulate requires highly linear and non-volatile resistance states, while activation requires diverse nonlinear synaptic behaviors. These contrasting demands make integrating both functions in a single device challenging. Here, we construct a multimodal ion-gate transistor using 2D CdPS<sub>3</sub>-Li as dielectric layer and MoS<sub>2</sub> as channel material. The layered structure of CdPS<sub>3</sub>-Li facilitates anisotropic ion transport for Li<sup>+</sup> storage and produces strong ion-electron coupling, resulting in high-linearity and non-volatile resistance states under electrical pulses. Moreover, Cd vacancies in CdPS<sub>3</sub>-Li attract and trap photo-generated holes from MoS<sub>2</sub>, leading to rich nonlinear behavior under light pulses. Therefore, the CdPS<sub>3</sub>-Li transistor can simultaneously perform both operations. The CdPS<sub>3</sub>-Li transistor arrays achieved high accuracy in handwritten digit classification, offering a promising hardware solution for neuromorphic computing. Neuromorphic engineering ; Transistor ; Nonlinear system ; Deep learning ; Dielectric ; Transistor model ; Power consumption ; Construct (python library) 2D Materials and Applications ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices
Authors: Binh Duc Nguyen | H. Lee | Jae-Yup Kim
Source: Applied Sciences (article, 2026-01-06)
Field: Materials Science — Materials Chemistry

Preparation and Solar-Energy Applications of PbS Quantum Dots via In Situ Methods In situ preparation routes have become central to advancing lead sulfide (PbS) quantum dots (QDs) for solar-energy conversion, owing to their ability to create strongly coupled QD/oxide interfaces that are difficult to achieve with ex situ colloidal methods, along with their simplicity and potential for low-cost, scalable processing. This review systematically examines the fundamental mechanisms, processing levers, and device implications of the dominant in situ approaches successive ionic layer adsorption and reaction (SILAR), voltage-assisted SILAR (V-SILAR), and chemical bath deposition (CBD). These methods enable conformal QD nucleation within mesoporous scaffolds, improved electronic coupling, and scalable low-temperature fabrication, forming the materials foundation for high-performance PbS-based architectures. We further discuss how these in situ strategies translate into enhanced solar-energy applications, including quantum-dot-sensitized solar cells (QDSSCs) and photoelectrochemical (PEC) hydrogen production, highlighting recent advances in interfacial passivation, scaffold optimization, and bias-assisted growth that collectively suppress recombination and boost photocurrent utilization. Representative device metrics reported in recent studies indicate that in-situ-grown PbS quantum dots can deliver photocurrent densities on the order of ~5 mA cm−2 at applied potentials around 1.23 V versus RHE in photoelectrochemical systems, while PbS-based quantum-dot-sensitized solar cells typically achieve power conversion efficiencies in the range of ~4–10%, depending on interface engineering and device architecture. These performances are commonly associated with conformal PbS loading within mesoporous scaffolds and quantum-dot sizes in the few-nanometer regime, underscoring the critical role of morphology and interfacial control in charge transport and recombination. Recent studies indicate that performance improvements in PbS-based solar-energy devices are primarily governed by interfacial charge-transfer kinetics and recombination suppression rather than QD loading alone, with hybrid heterostructures and inorganic passivation layers playing a key role in modifying band offsets and surface trap densities at the PbS/oxide interface. Remaining challenges are associated with defect-mediated recombination, transport limitations in densely loaded porous scaffolds, and long-term chemical stability, which must be addressed to enable scalable and durable PbS-based photovoltaic and photoelectrochemical technologies. Quantum dot ; Photocurrent ; Mesoporous material ; In situ ; Nucleation ; Lead sulfide ; Ionic bonding ; Charge carrier ; Electrode Quantum Dots Synthesis And Properties ; TiO2 Photocatalysis and Solar Cells ; Advanced Photocatalysis Techniques
Authors: Jessica L. Pimlott | Dale A. C. Brownson | Edward P. Randviir | Eric Brack | Craig E. Banks | Samuel J. Rowley‐Neale
Source: Environmental Science Nano (article, 2026-01-01)
Field: Chemistry — Electrochemistry

Electroanalytical overview: recent advances in the sensing of arsenic using screen-printed electrochemical platforms SPEs towards the detection of arsenic with a focus on electrode modifications to enhance sensitivity and selectivity. Arsenic ; Electrochemistry ; Electrode ; Sensitivity (control systems) ; Electroanalytical method ; Inorganic arsenic Electrochemical Analysis and Applications ; Arsenic contamination and mitigation ; Electrochemical sensors and biosensors
Authors: Youan Ji | Wenshi Xu | Ziyue Wu | Mengke Peng | Fuli Wang | Xinyu Zhang | Aibing Chen | Juan Du
Source: Communications Materials (article, 2026-04-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Advanced carbon-based electrodes for zinc-ion hybrid supercapacitors enhanced by heteroatom doping The growing demand for sustainable energy storage requires devices that combine high energy density with rapid charging and long cycle life. Zinc-ion hybrid supercapacitors offer a promising solution by integrating battery-like and capacitor-like electrodes, yet their performance depends critically on advanced carbon materials for the positive electrode. In this Review, we examine how incorporating nitrogen, oxygen, phosphorus, sulfur, boron and other elements into carbon structures enhances energy storage through multiple mechanisms. We analyze how single and multiple element combinations improve electronic properties, surface chemistry and pore structures to optimize device performance. We further discuss quantitative design principles for pore architecture, heteroatom ratios, and active site distribution. Finally, we address remaining challenges in electrode stability and manufacturing scalability, and outline future directions involving electrolyte engineering and optimized doping strategies for next-generation energy storage systems. Zinc-ion hybrid supercapacitors combine battery‑ and capacitor‑type electrodes, but their performance relies on advanced carbon materials. Here, the authors summarize how heteroatom doping enhances carbon-based electrodes and outline key design principles, stability and scalability challenges, and future directions. Supercapacitor ; Heteroatom ; Energy storage ; Carbon fibers ; Electrode ; Electrolyte ; Graphene ; Doping Supercapacitor Materials and Fabrication ; Advanced battery technologies research ; Advancements in Battery Materials
Authors: Monika Fedyna | Jakub Mokrzycki
Source: Materials (article, 2026-03-15)
Field: Materials Science — Materials Chemistry

Advanced Nanoporous and Mesoporous Materials: Emerging Innovations, Multiscale Design, and Future Perspectives Nanoporous and mesoporous materials have become some of the most dynamic and rapidly evolving areas of modern materials science [...]. Mesoporous material ; Nanoporous ; Key (lock) ; Porous medium ; Emerging technologies Mesoporous Materials and Catalysis ; Nanoporous metals and alloys ; Anodic Oxide Films and Nanostructures
Authors: Tatsuya Mori | Shunsuke Mieda | Koharu Kodama | Tomoyuki Miyao
Source: Journal of Polymer Science (article, 2026-01-09)
Field: Chemistry — Organic Chemistry

Mechanism‐Oriented Reaction Descriptors for Dispersity Prediction in Nitroxide‐Mediated Radical Polymerization Considering Mediator Stability ABSTRACT In recent years, there has been tremendous growth in data‐driven approaches in the field of polymer science, mainly using large databases. However, these analyses do not often lead to mechanism‐based interpretations, such as validating a hypothesis for a key reaction phenomenon. In this study, quantitative structure–property relationship (QSPR) models for dispersity ( Đ ) are constructed using reaction representations based on a putative reaction mechanism. These models are built on nitroxide‐mediated radical polymerization reaction datasets collected from the literature. The proposed reaction representation consists of 17 descriptors based on the quasi‐equilibrium reaction of forming dormant species, including the stability of mediator radicals. The tendency for the decomposition of the mediator radicals is quantified. Excluding an unstable mediator radical based on this descriptor significantly increases the prediction accuracy for a poly( n ‐butyl acrylate) dataset. Removing reactions proceeding on different reaction paths contributes to more accurate QSPR‐based Đ prediction models, and also to clearer model interpretation in light of the mechanism assumption. Additionally, prediction is found to be possible for datasets from different research groups under similar experimental conditions, while no meaningful prediction is achieved for reactions conducted under different experimental conditions and monomer conversion. Dispersity ; Radical ; Polymerization ; Representation (politics) ; Monomer ; Decomposition ; Reaction mechanism ; Polymer Advanced Polymer Synthesis and Characterization ; Machine Learning in Materials Science ; Antimicrobial agents and applications
Authors: M. Abdelhamid Shahat | Ashraf S. Khamees | Ahmed Ghitas | Hend  A. Ezzat
Source: Scientific Reports (article, 2026-03-07)
Field: Materials Science — Materials Chemistry

Highly sensitive hierarchically structured Si-based UV sensor–photodetectors via optimized ZnO–Al2O3 nanocomposite architectures The rapid and reliable detection of ultraviolet (UV) radiation is critical for applications ranging from environmental monitoring to optoelectronic security systems. This study presents an integrated theoretical and experimental investigation into highly sensitive, hierarchically structured Si-based UV sensor-photodetectors optimized via ZnO-Al<sub>2</sub>O<sub>3</sub> nanocomposite architectures. A combination of density functional theory (B3LYP/6-31G(d,p)) calculations and comprehensive materials characterization was employed to elucidate the interplay between electronic structure, surface morphology, and optical performance. Theoretical modeling provided detailed insights into band alignment, total and partial density of states, frontier molecular orbitals, and electrostatic potential distributions for pure and hybrid oxide systems, revealing that ZnO-Al<sub>2</sub>O<sub>3</sub> exhibits superior electronic coupling and enhanced carrier mobility pathways. Experimentally, ZnO and Al<sub>2</sub>O<sub>3</sub> nanoparticles were synthesized via hydrothermal routes, integrated into hybrid thin-film architectures on Si substrates, and structurally verified by XRD, FE-SEM, and EDX analyses. Surface roughness and apparent porosity measurements indicated that Al<sub>2</sub>O<sub>3</sub> incorporation increased roughness from 6.7 to 8.2 µm and porosity from 26 to 36%, fostering enhanced light scattering and active site density. Optical absorption spectroscopy (250-650 nm) revealed strong UV selectivity with calculated band gaps of 3.18 eV (ZnO), 3.11 eV (Al<sub>2</sub>O<sub>3</sub>), and 3.26 eV (ZnO-Al<sub>2</sub>O<sub>3</sub>), while electrochemical impedance spectroscopy confirmed reduced charge transfer resistance in the hybrid architecture. Electrical conductivity improved from 27.7 × 10<sup>-2</sup> S/m (ZnO) to 44.5 × 10<sup>-2</sup> S/m (ZnO-Al<sub>2</sub>O<sub>3</sub>), correlating with faster response and recovery dynamics under UV illumination. These synergistic structural, optical, and electronic enhancements establish ZnO-Al<sub>2</sub>O<sub>3</sub> as a promising candidate for next-generation, high-performance UV photodetectors with superior sensitivity, stability, and spectral selectivity. Nanocomposite ; Dielectric spectroscopy ; Spectroscopy ; Band gap ; Surface roughness ; Hybrid material ; Porosity ; Ultraviolet ; Electron mobility ; Nanoparticle ZnO doping and properties ; Ga2O3 and related materials ; Gas Sensing Nanomaterials and Sensors
Authors: Hosein Alimardani | Shayan Abaei | Mehrdad Asgari
Source: Chemical Science (article, 2026-01-01)
Field: Chemistry — Inorganic Chemistry

Decoding the unseen: unsupervised anomaly detection in metal–organic frameworks for discovery beyond the norm The discovery of chemically novel or structurally anomalous metal-organic frameworks (MOFs) is essential for expanding reticular design space and enhancing dataset reliability. We present CHEM-AD (Chemically Unusual Metal-organic Frameworks <i>via</i> Autoencoder-based Detection), a label-free, CPU-efficient pipeline that detects anomalous MOFs using 81 engineered descriptors (32 geometric/chemical/topological scalars plus a 49-dimensional metal-composition encoding). A compact symmetric autoencoder (∼1.8 × 10<sup>5</sup> trainable parameters) learns the latent distribution of typical MOFs and assigns anomaly scores based on reconstruction error. Applied to 26 025 entries from MOFxDB, CHEM-AD identifies 488 outliers (∼1.87%) featuring distinctive topologies, unusual pore metrics (PLD: 2.56-29.48 Å; LCD: 4.89-63.59 Å), and extreme densities (0.057-4.27 g cm<sup>-3</sup>). These anomalies consistently occupy peripheral clusters in PCA embeddings and exhibit substantial Mahalanobis distances from normal MOFs, indicating multivariate deviation. Feature attribution reveals <i>connectivity</i> (<i>e.g.</i>, edge/node counts, degree dispersion) as the primary driver of anomalies, followed by window-limited geometry and linker-metal composition. We categorize results into three groups: (A) topologically unusual yet plausible candidates, (B) anomalies with chemically resolvable issues, and (C) likely structural artifacts. The full pipeline executes in under six minutes on standard CPUs and does not require 3D structure fitting or graph parsing. CHEM-AD generalizes to other porous materials, providing a scalable framework for discovery, database curation, and robust preprocessing in materials informatics. Autoencoder ; Anomaly detection ; Pattern recognition (psychology) ; Outlier ; Multilinear map ; Scalability ; Preprocessor ; Interpretability ; Decoding methods ; Probabilistic logic Metal-Organic Frameworks: Synthesis and Applications ; Nanocluster Synthesis and Applications ; Supramolecular Chemistry and Complexes
Authors: María Teresa Colomer
Source: International Journal of Molecular Sciences (article, 2026-03-12)
Field: Materials Science — Materials Chemistry

Special Issue “Molecular Scale Design, Synthesis, and Application of Macroporous, Mesoporous, and Microporous Materials” The aim of this Special Issue is to emphasize the significance of molecular-scale design, synthesis, and application of macro-, meso-, and microporous materials in order to achieve optimal performance across diverse fields [...]. Microporous material ; Scale (ratio) ; Order (exchange) ; Field (mathematics) Mesoporous Materials and Catalysis ; Zeolite Catalysis and Synthesis ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Arnab Kumar Das | Tanmoy Dr.
Source: Advanced Theory and Simulations (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

First‐Principles Investigation of Point Defects in Na‐Antiperovskite Cathodes ABSTRACT Sodium antiperovskites have emerged as promising cathode materials for next‐generation sodium‐ion batteries owing to their structural flexibility and high sodium content. In this work, we present a comprehensive first‐principles investigation of point defects and sodium diffusion in defect containing Na‐rich antiperovskites (, where TM = 3d transition metals). The formation energies of Frenkel (Na, S, O, and transition metal) and Schottky ( and ) defect pairs are systematically evaluated under neutral charge conditions to elucidate the intrinsic defect chemistry and thermodynamic stability. Among all defect types, TM‐Frenkel and Na‐Frenkel pairs exhibit the lower formation energies, suggesting their predominance under equilibrium conditions. The energy above hull calculation (2–97 eV/atom) suggests that the Na‐Frenkel defect containing structures are metastable. The influence of Na‐Frenkel defects on sodium migration is further explored through diffusion pathway analysis using climbing image nudged elastic band method, revealing reduced activation barriers and enhanced Na mobility in the defected lattice, especially along (011) direction. Finally, interfacial thermodynamics between these Na‐antiperovskite cathodes with Na‐Frenkel defect pairs and representative solid electrolytes are examined to assess chemical compatibility and interphase stability. These results provide atomistic insight into defect‐mediated ionic transport and interfacial stability, guiding the design of high‐performance Na‐antiperovskite‐based cathodes. Crystallographic defect ; Schottky defect ; Cathode ; Frenkel defect ; Electrolyte ; Ionic bonding ; Schottky diode ; Diffusion Thermal Expansion and Ionic Conductivity ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Ivan Kodrin | Ivana Biljan
Source: Polymers (article, 2026-03-17)
Field: Materials Science — Materials Chemistry

Porous Organic Polymers with Azo, Azoxy, and Azodioxy Linkages: Design, Synthesis, and CO2 Adsorption Properties Rising atmospheric CO<sub>2</sub> levels have increased the demand for robust, scalable adsorbents for practical CO<sub>2</sub> capture and separation. Porous organic polymers (POPs) are attractive candidates because their pore architecture and binding site properties can be precisely tuned via building blocks and linkage formation. This review summarizes experimental and computational studies of azo-linked POPs and, more broadly, nitrogen-nitrogen (N-N) linked systems, emphasizing how synthetic routes, building blocks, and framework topology govern CO<sub>2</sub> uptake. We highlight key synthetic strategies and representative systems, including porphyrin-azo networks, and discuss the relatively sparse experimental literature on alternative N-N linked POPs incorporating azoxy and azodioxy motifs. Emphasis is placed on reversible nitroso/azodioxide chemistry as a potential pathway to ordered porous organic materials. Computational studies provide a practical route to connect structure with adsorption behavior in largely amorphous or partially ordered networks. We review hierarchical workflows combining periodic DFT and electrostatic potential properties, grand canonical Monte Carlo (GCMC) simulations, and binding energy calculations to rationalize trends and identify favorable binding environments. Computational findings demonstrate that pore accessibility and stacking models can strongly influence predicted CO<sub>2</sub> adsorption. This review provides guidelines for designing POPs with enhanced CO<sub>2</sub> adsorption, offering an outlook and discussing challenges for future studies. Stacking ; Adsorption ; Polymer ; Porous medium ; Porosity ; Scalability ; Mesoporous material ; Workflow Covalent Organic Framework Applications ; Carbon Dioxide Capture Technologies ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Meng Ji | Dagang Li | Zilei Zhang | Haocun Tan | Yue Zhang | Yingjun Dong | Chuanle Lu | Jinying Li | Dongxiang Zhang
Source: Research Square (preprint, 2026-03-02)
Field: Materials Science — Materials Chemistry

A radiation-resistant supermacroporous aerogel for ultrafast and high-capacity gaseous iodine capture Aerogel ; Adsorption ; Saturation (graph theory) ; Macropore ; Iodine ; Aqueous solution ; Diffusion Covalent Organic Framework Applications ; Carbon Dioxide Capture Technologies ; Chemical Looping and Thermochemical Processes
Authors: Lechen Dong | Fang Liu
Source: ChemRxiv (article, 2026-03-24)
Field: Materials Science — Materials Chemistry

Accelerating Quantum Chemistry Automation: Early Detection and Real-time Recovery of SCF Convergence Failure via Time Series Feature Extraction Self-consistent field (SCF) convergence failures bottleneck high-throughput quantum chemistry, particularly in open-shell systems.To address this, we introduce an autonomous pipeline treating the electronic structure descriptors from initial spin-unrestricted SCF iterations as a time series.Using statistical descriptors extracted from just the first ten iterations, a lightweight gradient-boosted classifier accurately predicts convergence failures.The model achieves 94.8% accuracy on 55,000 test molecules after training on only 10,000 examples.Coupling this early-warning predictor with a #-level-shift restart (#-RST) heuristic, derived from Bayesian Optimization of the shift parameters, enables automated recovery.Across six diverse calculation settings, our pipeline successfully rescued 57.5% of inherently difficult-to-converge molecules while maintaining a 94.1% convergence rate for easy cases, resulting in a 460,000-iteration reduction across the 1200 calculations.Ultimately, this framework provides a highly transferable, data-efficient, and autonomous solution for largescale electronic structure calculations. Convergence (economics) ; Series (stratigraphy) ; Quantum chemistry ; Feature (linguistics) ; Quantum ; Extraction (chemistry) Machine Learning in Materials Science ; Software System Performance and Reliability ; Quantum Computing Algorithms and Architecture