jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jonas Rein | Bartosz Górski | Ayça M Keskin | Minh Hoang Le | Song Lin
Source: ChemRxiv (article, 2026-01-29)
Field: Chemistry — Organic Chemistry

Desymmetrization of Meso-Pyrrolidines via Oxoammonium-Catalyzed Enantioselective Hydride Transfer We report the oxidative desymmetrization of N-urea-protected pyrrolidines via site-selective hydride transfer from enantiotopic C-H bonds. The optimal oxoammonium-peptide conjugate catalyst provided over 90% ee across all tested pyrrolidines, providing products that can readily undergo subsequent N-deprotection and other derivatization reactions to form medicinally relevant compounds. We isolated key on-cycle catalytic intermediates, which allowed us to elucidate both the mechanism of catalytic activation and stereochemical induction in detail. In particular, a stereochemical model for asymmetric induction emerged from analyzing a covalent catalyst-substrate-adduct, which served as an isolable analog of the enantiodetermining transition state. In this model, a tight hydrogen bond between the urea protecting group and the peptide directs the asymmetric hydride transfer. Desymmetrization ; Enantioselective synthesis ; Hydride ; Catalysis ; Asymmetric induction ; Aziridine ; Conjugate ; Covalent bond Catalytic C–H Functionalization Methods ; Asymmetric Hydrogenation and Catalysis ; Asymmetric Synthesis and Catalysis
Authors: Seema Rani
Source: International Journal of Applied and Behavioral Sciences (article, 2026-01-10)
Field: Chemistry — Organic Chemistry

Polymers in the 21st Century: Design, Functionality and Performance Polymers remain central to modern materials science because of their tunable structures, broad functional range, and scalable processing. In the 21st century, advances in controlled synthesis, precision/sequence-defined architectures, data-driven design, and manufacturing (including additive manufacturing) have dramatically expanded polymer functionality — enabling stimuli-responsive “smart” polymers, high-performance composites, biobased and degradable systems, and multifunctional devices. Simultaneously, the field faces urgent challenges related to durability vs. recyclability trade-offs environmental impact, and scale-up of precision chemistries. This article reviews contemporary design strategies, the link between molecular architecture and functional performance, emerging computational and manufacturing tools that accelerate discovery, and the major sustainability and standards challenges shaping future research directions. Sustainability ; Scalability ; Durability ; Field (mathematics) ; Architecture Advanced Polymer Synthesis and Characterization ; Machine Learning in Materials Science ; Polymer composites and self-healing
Authors: Anupama M | Santhosh G | Vidhya G | Suchithra M
Source: Global Journal of Engineering Innovations and Interdisciplinary Research (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

Predictive Modeling of Material Properties Using Nanotechnology and Artificial Intelligence Techniques Accurate prediction of material properties is essential for accelerating the development of advancedengineering materials, particularly those incorporating nanoscale features. Traditional experimentaland physics-based modeling approaches, while reliable, are often limited by high computational costand the complexity of capturing nonlinear nanoscale interactions. This study presents an integratedpredictive modeling framework that combines nanotechnology-derived material descriptors withartificial intelligence techniques to estimate key material properties. Nanoscale parameters such asparticle size, volume fraction, and structural characteristics are used as inputs to train machine learningmodels capable of learning complex structure–property relationships. The performance of AI-basedmodels is evaluated and compared with conventional empirical and physics-based approaches. Resultsdemonstrate that the artificial intelligence–driven framework achieves higher prediction accuracy andsignificantly reduced computational effort while maintaining consistency with experimental observations.The proposed approach highlights the potential of combining nanotechnology and artificial intelligenceto support efficient material design and optimization in advanced engineering applications. Consistency (knowledge bases) ; Material properties ; Key (lock) ; Nonlinear system ; Scale (ratio) ; Multiscale modeling ; Empirical modelling Machine Learning in Materials Science ; Composite Material Mechanics ; Model Reduction and Neural Networks
Authors: Léna Triestram | François‐Xavier Coudert
Source: Chemical Science (article, 2026-01-01)
Field: Chemistry — Inorganic Chemistry

Identifying phase transitions in zeolitic imidazolate frameworks: microscopic insight from molecular simulations Metal-organic frameworks (MOFs) feature a rich structural diversity, including crystalline, amorphous, and liquid phases of varying topologies. Their structural characterization is often performed either at the local scale (through pair distribution functions, bond angle distributions, <i>etc.</i>) or, for crystalline phases, through topology analysis of the periodic framework-leaving out disordered and amorphous phases. In this work, we develop a computational methodology for the structural characterization of middle-range order in MOFs that is applicable to both crystalline and amorphous phases. We base our method on the statistical analysis of the geometry of the supramolecular framework at the microscopic level, and its evolution during molecular simulation. We analyze the statistics of metal-organic rings, their distribution in size, as well as their geometrical characteristics through mathematical tools derived from polymer physics: radius of gyration, asphericity, and writhe. We show that this advanced characterization can be leveraged for the identification of phases and the detection and analysis of phase transitions. Characterization (materials science) ; Zeolitic imidazolate framework ; Amorphous solid ; Molecular dynamics ; Supramolecular chemistry ; Phase transition ; Polymer ; Phase (matter) Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; X-ray Diffraction in Crystallography
Authors: Rajapriya Govindaraju | Jongsung Kim
Source: The Analyst (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

MXene-based fluorescent aptasensors: advances and prospects in diagnostics and environmental monitoring MXene-based fluorescent aptasensors leverage the synergistic integration of the intrinsic physicochemical properties of MXenes, including tunable surface chemistry, broad-spectrum optical absorption, and superior fluorescence quenching efficiency, with the molecular recognition capabilities and strong binding affinity of aptamers. These two-dimensional transition metal carbides and nitrides efficiently suppress background fluorescence in dye-labeled aptamer systems through electrostatic interactions and π-π stacking. In the absence of the target analyte, the aptamers adsorb onto the MXene surface, facilitating non-radiative energy transfer and thereby suppressing the signal. Upon specific target recognition, a conformational rearrangement of the aptamer reduces its surface affinity, leading to desorption and subsequent fluorescence recovery <i>via</i> a target-induced "signal-on" mechanism. Such platforms demonstrate ultra-low detection limits, excellent selectivity, and modular adaptability for the detection of a broad spectrum of analytes, including clinical biomarkers, pathogenic microorganisms, environmental toxins, and heavy metal ions. This comprehensive review systematically summarises the mechanistic foundations of MXene-aptamer interactions, recent advancements in analytical applications, and emerging directions for translational development in biomedical diagnostics and environmental monitoring. Aptamer ; Fluorescence ; Optical sensing ; Energy transfer ; Förster resonance energy transfer ; Quenching (fluorescence) MXene and MAX Phase Materials ; Advanced biosensing and bioanalysis techniques ; Advanced Wireless Communication Technologies
Authors: Selvam Sathiyavimal | Devaraj Bharathi | Ezhaveni Sathiyamoorthi
Source: Pharmaceuticals (article, 2026-02-19)
Field: Materials Science — Materials Chemistry

Metal-Functionalized Nanozymes in Antibacterial Wound Management: Recent Advances and Future Perspectives Chronic and infected wounds continue to pose significant clinical challenges due to microbial infections, biofilm development, inflammation, and poor tissue regeneration. Traditional antibiotics medications often show low efficacy and lack stability. The demand for new therapeutic approaches is increasing due to bacterial resistance. Metal-based nanozymes have intrinsic enzyme-like catalytic activity and emerged as a promising class of antibacterial agents for wound-healing applications. The functionalization with metals such as silver (Ag), copper (Cu), iron (Fe), manganese (Mn), cerium (Ce), platinum (Pt) and gold (Au) enhances peroxidase (POD)-, oxidase (OXD)-, and catalase (CAT)-like biomimetic activities. This improvement enables efficient reactive oxygen species (ROS) production, biofilm inhibition, and microenvironment-responsive antibacterial activity. These metal-nanozymes also alter the immune response, increase angiogenesis, and promote extracellular matrix remodeling when combined with metals and also polysaccharides. This review summarizes recent advances in metal-incorporated antibacterial nanozymes including their design, catalytic mechanisms, structure-activity relationships, and integration into hydrogels, films, and fibers for wound healing. Key challenges such as biosafety, metal ion release, the inflammatory balance, and clinical translation are critically discussed. Emerging directions such as single-atom nanozymes, cascade enzyme systems, and stimuli-responsive platforms are highlighted as promising routes for next-generation wound therapeutics. Overall, this review underscores the clinical potential of metal-functionalized nanozymes for infected wound management; however, concerns regarding ion leakage and long-term safety persist emphasizing the need for controlled designs and biocompatible systems to enable safe translation. Biofilm ; Antibacterial activity ; Surface modification ; Biocompatible material ; Antibiotics Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics ; Nanocluster Synthesis and Applications
Authors: Zexin Li | Genchang Gou | Xiang Xu | Haoyun Wang | Teng Liu | Hao Luo | Jinsong Wu | Kailang Liu | Liang Gao | Tianyou Zhai | Xing Zhou
Source: Nature Communications (article, 2026-02-20)
Field: Materials Science — Materials Chemistry

Oriented ion migration in dielectric Sb4O5Cl2 single crystals for multifunctional two-dimensional electronics Exploring dielectrics with high gate capacitance and artificial tailoring of electronic states is highly desirable for the development of 2D electronics. Here, we report the synthesis of a single-crystalline ionic dielectric Sb<sub>4</sub>O<sub>5</sub>Cl<sub>2</sub> with aligned ionic channels, achieving a dielectric constant of 23.3 (at a thickness of ~ 25 nm) and non-volatile programmable regulation of the ionic migration. The fabricated 2D MoS<sub>2</sub> transistors display on/off ratios up to 10<sup>9</sup><sub>,</sub> leakage currents down to 10<sup>-14</sup> A, and mobility of 33.4 cm<sup>2 </sup>V<sup>-1</sup> s<sup>-1</sup>. Oriented ionic channels facilitate efficient and controllable migration of Cl<sup>-</sup> ions, thus realizing a non-structural-damage and reconfigurable transition between a quasi-metallic and semiconducting state in MoS<sub>2</sub>, with a retention time exceeding 3000 s. During 100 cycles of state transitions, the quasi-metallic state exhibits a conductivity of 10<sup>-5</sup> S, which is 10<sup>3</sup> times that of the semiconducting state. Bio-inspired ionic channel further allows neuromorphic devices to integrate image storage, processing, and recognition capabilities, achieving a recognition accuracy improvement from 80.7% to 90.9% within just 5 training epochs through non-volatile preprocessing lasting over 300 s. Our study highlights the significant potential of this single-crystal ionic dielectric as a promising platform for fundamental research on electronic state modulation and advanced electronics. Dielectric ; Neuromorphic engineering ; Ionic bonding ; Capacitance ; Electronics ; Transistor ; Gate dielectric ; Ion 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices ; Advanced Memory and Neural Computing
Authors: Malik Muhammad Shoaib | Fatima Iftikhar | Tariq Mahmood | Khurshid Ayub | Basit Niaz
Source: Scientific Reports (article, 2026-03-22)
Field: Chemistry — Inorganic Chemistry

DFT based photophysical assessment of 2-substituted-3-(pyridin-2-yl)-benzo-[d][1,3]-azaphosphole P-oxide for organic optoelectronic applications P-containing heterocycles such as 1,3-benzazaphospholes offer a versatile platform for tunable organic semiconductors. This computational study demonstrates that replacing a terminal phenyl group with a pyridin-2-yl unit in 2-substituted benzo-[d][1,3]- azaphosphole P-oxides reconfigures the intramolecular charge-transfer pathway. Fragment orbital analysis quantifies this inversion: the pyridine (PY) ring contributes 52–75% of HOMO density, establishing it as the electron donor, while the opposing aryl (AR) substituent contributes 33–59% of LUMO density, functioning as the acceptor. This electronic reconfiguration creates a push-pull framework where the AR group dictates optoelectronic properties across three distinct classes. π-extended heteroacene derivatives (6–9) exhibit tunable emission (367–515 nm) with high oscillator strengths, enhanced nonlinear optical response (β up to 2621 a.u.), and favorable hole transport (λh = 0.20–0.28 eV). Thermal stability improves with π-extension (P-C BDE > 78 kcal/mol). Across the series, all properties scale with HOMO-LUMO spatial separation establishing a unified mechanistic framework where greater orbital separation systematically red-shifts emission, lowers reorganization energy, narrows ΔEST, and increases hyperpolarizability. Three compounds emerge with particularly favorable combinations: 6 (cyan emission, λh = 0.22 eV, β = 1871 a.u.), 7 (blue emission, highest f = 0.793, β = 2621 a.u.), and 8 (orange emission, balanced λh/λe, highest BDE = 80.3 kcal/mol). These computational predictions, provide a foundation for rational design of P-based organic semiconductors, with compounds 6–8 prioritized for experimental validation. Substituent ; Control reconfiguration ; Intramolecular force ; HOMO/LUMO ; Rational design ; Ring (chemistry) ; Aryl ; Pyridine Synthesis and characterization of novel inorganic/organometallic compounds ; Organic Light-Emitting Diodes Research ; Organometallic Compounds Synthesis and Characterization
Authors: Nouran Alwisi | Sarah Aqel | Janatul Naeim | Dana Al-Hashimi | Samer El Hayek | Firas Kobeissy | Abdullah Shaito
Source: International Journal of Nanomedicine (article, 2026-02-01)
Field: Materials Science — Biomaterials

Multifunctional Nanoparticles in Traumatic Brain Injury: From Targeted Imaging and Diagnosis to Innovative Therapeutics Traumatic brain injury (TBI) remains a leading cause of morbidity and mortality worldwide, with limited therapeutic progress due to challenges such as impermeability of the blood-brain barrier (BBB) and the multifactorial nature of secondary neurodegeneration. Nanoparticle-based platforms, owing to their tunable physicochemical properties, surface modifiability, and multifunctionality, have emerged as promising tools for both diagnosis and therapy. A wide range of inorganic, organic, and carbon-based nanoparticles has demonstrated improved imaging contrast, enhanced biosensing capabilities, and potential for targeted, real-time diagnostics. On the therapeutic front, nanoparticles have shown the ability to concentrate therapeutic agents at or near injury sites; however, achieving precise delivery remains a major challenge. Indeed, nanoparticle-based therapies are still limited by off-target accumulation in peripheral organs, incomplete BBB penetration, and heterogeneous tissue distribution. Addressing these barriers requires optimizing particle size, surface charge, ligand conjugation, and degradability to improve site-specific targeting and minimize systemic toxicity. In this review, we examine major classes of nanoparticles, including organic, inorganic, carbon-based, and biologically derived nanocarriers, and discuss the key physicochemical properties governing their interactions with the central nervous system. We evaluate their applications in TBI diagnosis, neuroimaging, and therapy, emphasizing the design principles influencing blood-brain barrier penetration, targeting specificity, biodistribution, and clearance. We further assess emerging nanoparticle-based strategies to improve site-specific delivery and mitigate secondary brain injury, and highlight key translational challenges and future clinical directions. Continued research into biodegradable, biomimetic, and environmentally sustainable synthesis methods is essential to advancing nanoparticle design and ensuring their safe and effective integration into the clinical management of TBI. Traumatic brain injury ; Drug delivery ; Biocompatible material ; Nanomedicine ; Blood–brain barrier Nanoparticle-Based Drug Delivery ; Barrier Structure and Function Studies ; S100 Proteins and Annexins
Authors: Giustino Sulpizio | Charlotte Gerhaher | Esther Heid | Kjell Jorner
Source: ChemRxiv (article, 2026-03-16)
Field: Materials Science — Materials Chemistry

Bridging CGR Representations and Language Models for Reaction Property Prediction Accurate prediction of reaction properties, such as activation energies, enthalpies, rate constants and yields, is key to the design of efficient and sustainable chemical processes. While graph-based models using Condensed Graphs of Reaction (CGR) have shown strong performance, text-based Transformer models trained on reaction SMILES have gained popularity due to their scalability and performance. In this work, we analyze the performance of an existing CGR-based string representation for reaction property prediction, the SMILES/CGR. In contrast to conventional reaction SMILES, this representation explicitly encodes atom and bond changes within a unified SMILES-like sequence. A key limitation of SMILES/CGR is that it does not encode stereochemistry. To overcome this, we introduce the superimposed reaction SMILES (sr-SMILES), a compact CGR-based representation that preserves full stereochemical information. To evaluate both representations, we pretrained a BERT transformer on two million USPTO reactions via masked language modeling, and then finetuned separate models on six benchmark datasets. We compared against models trained on standard reaction SMILES, naïve baselines, and results from the literature. Our results show that sr-SMILES consistently matches or outperforms reaction SMILES, particularly on benchmarks where mechanistic information is critical, as well as in low-data regimes. In contrast, SMILES/CGR tends to underperform, highlighting that the details of CGR encoding matter. While sr-SMILES did not close the performance gap with graph-based CGR methods, it shows that language models can benefit from explicit mechanistic information when appropriately encoded. Moreover, by comparing SMILES/CGR and sr-SMILES models with different atom mappings, we reinforce the notion that CGR-based approaches are highly sensitive to the mapping quality. A plausible explanation for the similar performance observed across string-based representations is that language models implicitly learn the atom mapping during (pre-)training, rendering explicit mapping unnecessary for many prediction tasks. Scalability ; Bridging (networking) ; Representation (politics) ; Transformer ; Language model ; Encoding (memory) ; Property (philosophy) Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Advanced Graph Neural Networks
Authors: Dayong Hou | Xiangpeng Li | N. Zhang | Xingyan Fu | Hao Wang
Source: Cancer Biology and Medicine (article, 2026-03-12)
Field: Chemistry — Organic Chemistry

Click chemistry-driven tumor theranostics: recent advances, challenges, and future perspectives Tumor theranostics, which integrates accurate diagnosis and precise therapy, has emerged as a pivotal approach in modern oncology for improving treatment efficacy and reducing off-target toxicity. Click chemistry, which is characterized by high efficiency, selectivity, biocompatibility, and modularity, has become an indispensable tool for constructing versatile theranostic systems. This review systematically summarizes the recent progress in click chemistry-based tumor theranostic systems, starting with an overview of core click reactions and the unique features in biomedical applications. We then focus on the application of click chemistry within a diagnosis-therapy-theranostics framework to three key aspects of tumor theranostics: (i) tumor diagnosis (molecular imaging probes and circulating tumor cell detection); (ii) tumor therapy (chemotherapy, phototherapy, immunotherapy, and gene therapy); and (iii) integrated theranostics (multimodal imaging-guided combinatorial therapy). Furthermore, the current challenges, such as the biocompatibility of catalysts and <i>in vivo</i> reaction efficiency, are critically discussed. Finally, we highlight promising directions, including stimuli-responsive click reactions, AI-assisted probe design, and personalized theranostic systems. This review not only serves as a comprehensive reference for researchers but also highlights how click chemistry uniquely bridges molecular design with clinical functionality, distinguishing click chemistry from conventional conjugation or labeling methods by enabling spatiotemporal control, modular integration, and bioorthogonal precision in complex biological settings. Click chemistry ; Bioorthogonal chemistry ; Modular design ; Personalized medicine ; Tumor cells Click Chemistry and Applications ; Nanoplatforms for cancer theranostics ; Protein Degradation and Inhibitors