jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Manihar Situmorang | Khoirul Bariah | Ijayanti Pardosi | Rudi Purwanto | Marham Sitorus | Ani Sutiani | Hendra Simanjuntak
Source: Chemistry Teacher International (article, 2026-02-21)
Field: Chemistry — Physical and Theoretical Chemistry

Active learning oriented to green chemistry with virtual laboratories to promote higher order thinking skills to achieve quantitative analytical chemistry competency Abstract Objectives Achievement of analytical chemistry competency needs to be optimized through the application of active learning models. This research aims to design an active learning model oriented towards green chemistry with virtual laboratories to promote higher order thinking skills (HOTS) as a strategy for achieving quantitative analytical chemistry competence. Methods The research stages include designing a project-based learning model oriented towards green analytical chemistry with virtual laboratories (PjBL-GACVL), standardizing and implementing it in teaching analytical chemistry to undergraduate students. Results The standard PjBL-GACVL model is equipped with innovative learning resources, mini projects, virtual laboratories, and titration project assignments, then implemented in experimental classes for teaching titrimetry, and compared to direct learning (DI) in control classes. The PjBL-GACVL model is effective in guiding students to study titrimetry, namely planning and carrying out titration projects for quantitative determination of target analytes in real samples. Students have used HOTS as scientific steps in working on their chosen contextual project. Laboratory skills have been mastered, including assembling titration equipment and analytical procedures, handling chemicals and work safety, and quantitative calculations. HOTS achievement is high and has a positive correlation with chemistry learning outcomes. The analytical chemistry learning outcomes in the experimental group were higher than those in the control group, and the two were significantly different. Gain normalization confirms the effectiveness of the PjBL-GACVL model in improving chemistry learning outcomes. Conclusions This learning model is accepted by students, is relevant to daily digital life, and is effective for achieving quantitative analytical chemistry competencies. This new model is appropriate for science teaching which requires thinking skills to increase knowledge and skills simultaneously. Active learning (machine learning) ; Higher-order thinking ; Chemistry education ; Educational technology ; Control (management) ; Teaching method ; Digital learning Various Chemistry Research Topics ; Chemistry and Chemical Engineering ; Innovative Teaching Methods
Authors: Jingxuan Wei | Xingyue Wang | Yanghaoyu Liao | Jie Dong | Yuchen Liu | Caijun Jia | Bihui Yu | Junnan Zhu
Source: ArXiv.org (article, 2026-01-08)
Field: Materials Science — Materials Chemistry

GenProve: Learning to Generate Text with Fine-Grained Provenance Large language models (LLM) often hallucinate, and while adding citations is a common solution, it is frequently insufficient for accountability as users struggle to verify how a cited source supports a generated claim. Existing methods are typically coarse-grained and fail to distinguish between direct quotes and complex reasoning. In this paper, we introduce Generation-time Fine-grained Provenance, a task where models must generate fluent answers while simultaneously producing structured, sentence-level provenance triples. To enable this, we present ReFInE (Relation-aware Fine-grained Interpretability & Evidence), a dataset featuring expert verified annotations that distinguish between Quotation, Compression, and Inference. Building on ReFInE, we propose GenProve, a framework that combines Supervised Fine-Tuning (SFT) with Group Relative Policy Optimization (GRPO). By optimizing a composite reward for answer fidelity and provenance correctness, GenProve significantly outperforms 14 strong LLMs in joint evaluation. Crucially, our analysis uncovers a reasoning gap where models excel at surface-level quotation but struggle significantly with inference-based provenance, suggesting that verifiable reasoning remains a frontier challenge distinct from surface-level citation. Interpretability ; Task (project management) ; Fidelity ; Accountability ; Verifiable secret sharing ; Projection (relational algebra) ; Annotation Machine Learning in Materials Science ; Scientific Computing and Data Management ; Artificial Intelligence in Healthcare and Education
Authors: J. H. Ding | Yunhui Meng | Lijia Zhang | Xiaobai Li | Mengyao Pan | Wenqi Dong | Kun Song | Yongpeng Liu | Shilong Yang | Hongwei Ma
Source: Apollo (University of Cambridge) (article, 2026-02-24)
Field: Chemistry — Spectroscopy

Machine Learning-Assisted Detection of Phosgene and Acetyl Chloride via a Dual-Probe Fluorescent Platform with Differential Reactivity. Phosgene and acyl chlorides are highly toxic chemicals that pose serious threats to human health and environmental safety, yet their rapid and reliable detection remains a major challenge due to their high reactivity and environmental interferences. Herein, we report the rational design and synthesis of two donor-π-acceptor (D-π-A) fluorescent probes, TPA-APPA and TPA-HPO, which incorporate excited-state intramolecular proton transfer and hybridized localized and charge-transfer characteristics. These probes exhibit fast, highly selective, and sensitive responses toward phosgene, with TPA-APPA showing distinct fluorescence enhancement and colorimetric changes for both phosgene and acetyl chloride at low detection limits. Benefiting from its excellent photostability, TPA-APPA was successfully applied to bioimaging in live cells and zebrafish. Furthermore, by integrating machine learning algorithms, including convolutional neural networks and the Swin Transformer, we developed a ceramic-fiber detection strip capable of intelligent recognition and classification of fluorescence signal variations. This hybrid system achieved high classification accuracy for toxicant vapors, demonstrating the strong potential of coupling advanced fluorescent probes with machine learning for next-generation toxic gas monitoring and bioimaging applications. Phosgene ; Fluorescence ; Toxicant ; Chloride ; Fluorescent labelling ; Reactivity (psychology) ; Proton Molecular Sensors and Ion Detection ; Sulfur Compounds in Biology ; Luminescence and Fluorescent Materials
Authors: Dongxiao Li | Weixin Liu | Yinpeng Wang | Daoye Zheng | Chengkuo Lee
Source: Micro & Nano Manufacturing (article, 2026-03-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

MEMS-enabled reconfigurable metasurface and nanophotonics: advances and perspectives Microelectromechanical systems (MEMS) have emerged as powerful technology for reconfigurable metasurface and nanophotonics, providing dynamic, low-power, and scalable control over optical functionalities that are inherently fixed in static nanophotonic devices. By introducing mechanical degrees of freedom, MEMS-enabled metasurface and nanophotonics allow real-time modulation of optical resonances, coupling strength, phase profiles, and radiation characteristics across a broad spectral range. This capability has opened new opportunities for adaptive sensing, tunable radiation sources, reconfigurable silicon photonic platforms, dynamic metalenses, and terahertz metasurfaces. In this Review, we present a comprehensive overview of recent advances in MEMS-enabled reconfigurable metasurface and nanophotonics, with a particular focus on application-driven developments in sensing, metasurface-based radiation sources, integrated silicon photonics, flat optics, and terahertz systems. Furthermore, we discuss emerging trends toward intelligent and programmable nanophotonic systems enabled by artificial intelligence–assisted design and closed-loop control. Finally, we outline the remaining challenges and future perspectives for translating MEMS-enabled reconfigurable nanophotonics from laboratory demonstrations to practical, large-scale applications. Nanophotonics ; Terahertz radiation ; Photonics ; Reconfigurability ; Scalability ; Metamaterial ; Modulation (music) ; Optical switch Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Mechanical and Optical Resonators
Authors: Haijian Lu | Wei Wang | Jing Liu
Source: ArXiv.org (article, 2026-03-20)
Field: Materials Science — Materials Chemistry

FormalEvolve: Neuro-Symbolic Evolutionary Search for Diverse and Prover-Effective Autoformalization Autoformalization aims to translate natural-language mathematics into compilable, machine-checkable statements. However, semantic consistency does not imply prover effectiveness: even semantically consistent formalizations can differ substantially in proof-search cost and success rate. In this work, we formulate autoformalization as a budgeted, test-time search for semantically consistent repertoires, and propose FormalEvolve, a compilation-gated neuro-symbolic evolutionary framework. FormalEvolve generates diverse candidates via LLM-driven mutation and crossover with bounded patch repair, while symbolic Abstract Syntax Tree (AST) rewrite operations further inject structural diversity. On CombiBench and ProofNet, under a strict generator-call budget of T = 100, FormalEvolve reaches semantic hit rates (SH@100) of 58.0% and 84.9%, and reduces cross-problem concentration of semantic successes(lower Gini). Under a fixed prover budget, FormalEvolve also improves downstream proving performance on CombiBench. Code will be released publicly. Gas meter prover ; Syntax ; Consistency (knowledge bases) ; Semantics (computer science) ; Crossover ; Evolutionary algorithm ; Bounded function ; Abstract syntax Machine Learning in Materials Science ; Mathematics, Computing, and Information Processing ; Evolutionary Algorithms and Applications
Authors: Magdalina Popova | Yana Dimitrova | Katerina Lazarova | Denitsa Elenkova
Source: Pure and Applied Chemistry (article, 2026-03-04)
Field: Chemistry — Inorganic Chemistry

Luminescent PVDF/Ln-MOF composite films for selective detection of Cr (III), Cr (VI), Fe (III) and Ag (I) in water Abstract The development of efficient, sensitive, and selective sensors for water contaminants is of paramount importance due to the increasing environmental and health concerns associated with metal ion pollution. In this work, lanthanide-based metal–organic frameworks (Ln-MOFs) were incorporated into PVDF membrane films to create robust and easy to use luminescent sensing platforms. Eu (III) and mixed-metal Eu/Tb MOFs were embedded via dip-coating. Uniform films with sufficiently good luminescent properties making the materials suitable as luminescent sensors were prepared named PVDF@[Eu 2 (BDC) 3 (H 2 O) 4 ] and PVDF@[Eu 0.2 Tb 1.8 (BDC) 3 (H 2 O) 4 ]. The films continue to demonstrate selective detection of Cr (III), Cr (VI), Fe (III), and Ag (I) in aqueous media. PVDF@[Eu 2 (BDC) 3 (H 2 O) 4 ] exhibited superior sensitivity toward Cr (III) (Ksv = 2162 M −1 , LOD = 111 µM), while PVDF@[Eu 0.2 Tb 1.8 (BDC) 3 (H 2 O) 4 ] was more responsive to Cr (VI) (Ksv = 5253 M −1 , LOD = 48 µM). The dual selectivity of these films enables differentiation between oxidation states of chromium. Both materials showed comparable quenching constants for Fe (III) (4–5 × 10 3 M −1 ) and significant responses to Ag (I). Permanent loss of luminescence was observed in the case of Cr (III), Fe (III) and Ag (I) suggesting destructive process. The samples demonstrate the potential of polymer-Ln-MOF composites as versatile and fast luminescent sensors for environmental monitoring applications. Luminescence ; Aqueous solution ; Quenching (fluorescence) ; Composite number ; Selectivity ; Metal ; Luminescent Measurements Metal-Organic Frameworks: Synthesis and Applications ; Molecular Sensors and Ion Detection ; Gas Sensing Nanomaterials and Sensors
Authors: L. L. H. Both | Isabel Zerfuss | Mandy Paschetag | Stephan Scholl
Source: Polymers (article, 2026-02-22)
Field: Materials Science — Polymers and Plastics

Solid State Alkaline Depolymerization of Polyester Elastane Textiles in a Laboratory Kneader Elastane is ubiquitous in polyester-based textiles and complicates depolymerization-based recycling because it can undergo thermal degradation and chemical bond cleavage, consuming reagents and forming low-molecular by-products that may compromise monomer quality. Here, we investigate alkaline PET depolymerization of PET/elastane blends under an intentional base-competition scenario in a laboratory kneader. Pure PET (100/0) and PET/EL blends (95/5 and 85/15, wt/wt) were processed under quasi-solid-state conditions at 140 °C for 5 min using solid NaOH dosed at 2.1 mol per mol PET repeat unit and pelletized feedstocks to ensure scale-relevant mixing and reproducible chamber filling. Torque and bulk-temperature profiles were similar across compositions, and isolated terephthalic acid yields remained in a narrow corridor (68-71%), indicating that PET depolymerization is not measurably impaired by 5-15 wt% elastane within this reaction window. Differential scanning calorimetry of water-insoluble residues revealed pronounced changes in elastane-related thermal transitions, evidencing elastane modification during treatment. Targeted <sup>1</sup>H NMR screening of recovered TA against a 4,4'-methylenedianiline spiked reference showed no detectable co-isolated aromatic diamines. Overall, the study demonstrates robust monomer recovery from mixed PET/EL textiles under solid-NaOH, short-residence, solvent-lean processing, while identifying residue analytics as the key bottleneck for quantifying elastane fate and closing component balances. Depolymerization ; Monomer ; Polyester ; Residue (chemistry) ; Differential scanning calorimetry ; Sponge ; Degradation (telecommunications) Polymer crystallization and properties ; Polymer composites and self-healing ; Microplastics and Plastic Pollution
Authors: Md Afif Al Mamun | Sayan Nath | Gias Uddin | Novarun Deb
Source: ArXiv.org (article, 2026-04-03)
Field: Materials Science — Materials Chemistry

Evaluating the Environmental Impact of using SLMs and Prompt Engineering for Code Generation The shift from cloud-hosted Large Language Models (LLMs) to locally deployed open-source Small Language Models (SLMs) has democratized AI-assisted coding; however, it has also decentralized the environmental footprint of AI. While prompting strategies - such as Chain-of-Thought and ReAct - serve as external mechanisms for optimizing code generation without modifying model parameters, their impact on energy consumption and carbon emissions remains largely invisible to developers. This paper presents the first systematic empirical study investigating how different prompt engineering strategies in SLM-based code generation impact code generation accuracy alongside sustainability factors. We evaluate six prominent prompting strategies across 11 open-source models (ranging from 1B to 34B parameters) using the HumanEval+ and MBPP+ benchmarks. By measuring Pass@1 accuracy alongside energy (kWh), carbon emissions (kgCO2eq), and inference latency, we reveal that sustainability often decouples from accuracy, allowing significant environmental optimizations without sacrificing performance. Our findings indicate that Chain-of-Thought, being a simpler prompting technique, can provide a near-optimal balance between reasoning capability and energy efficiency. Conversely, multi-sampling strategies often incur disproportionate costs for marginal gains. Finally, we identify grid carbon intensity as the dominant factor in deployment-time emissions, highlighting the need for practitioners to consider regional energy profiles. This work provides a quantitative foundation for "green" prompt engineering, enabling developers to align high-performance code generation with ecological responsibility. Code generation ; Sustainability ; Carbon footprint ; Code (set theory) ; Energy consumption ; Greenhouse gas ; Environmental impact assessment ; Grid Machine Learning in Materials Science ; Green IT and Sustainability ; Scientific Computing and Data Management
Authors: Jian Yan | Xuan Yang | Tianyi Wang | Qing Yang | Kai Qi | Meiqi Dang | Mingdong Li
Source: ACS Omega (article, 2026-03-12)
Field: Materials Science — Materials Chemistry

Development and Study of a Salt and Oil Tolerant Foamer System for Tight Gas Well Foam Drainage Gas Recovery To address the technical challenge of severe performance degradation of foaming agents caused by high-salinity formation water and condensate during drainage gas recovery in tight gas wells, this study developed a composite foam system (<i>BLZP</i>) with excellent salt and oil tolerance. Through chemical modification, zwitterionic groups (sulfonate and quaternary ammonium) were introduced into the poly-(vinyl alcohol) (<i>PVA</i>) backbone to synthesize a zwitterionic polymer <i>ZPVA</i>, which served as a stabilizer. <i>ZPVA</i> was then optimized via orthogonal experiments with the zwitterionic primary foaming agent dodecyl dimethyl betaine (<i>BS-12</i>) and the nonionic cofoaming agent lauryl dimethyl amine oxide (<i>LAO</i>). Sodium perfluorononyloxybenzenesulfonate (<i>OBS</i>) was subsequently incorporated as an enhancer. Systematic characterization and evaluation demonstrated that <i>ZPVA</i> effectively suppresses polymer aggregation in 10 wt % NaCl or CaCl<sub>2</sub> solutions, maintaining smaller hydrodynamic sizes and higher solution viscosities. The optimized <i>BLZP</i> system exhibited significantly enhanced interfacial dilatational viscoelasticity in simulated high-salinity formation water (TDS 78,050 mg/L). Oil-tolerance tests (with condensate content up to 60%) further revealed that <i>BLZP</i>, owing to its compact and highly elastic composite interfacial adsorption layer, effectively inhibits oil droplet spreading and bridging-induced foam rupture. The decrease in initial foam height (<i>H</i> <sub>0</sub>) and 5 min foam height (<i>H</i> <sub>5</sub>) was substantially lower than that of two commercial foaming agents. Even at 60% oil content, <i>BLZP</i> maintained <i>H</i> <sub>5</sub> > 150 mm, liquid carrying ratio >40%, and foam water content >4%, demonstrating outstanding potential for field applications. This study provides an innovative chemical solution for efficient foam-assisted drainage gas recovery under challenging reservoir conditions. Drainage ; Tight oil ; Fossil fuel ; Salt (chemistry) ; Petroleum ; Tight gas Pickering emulsions and particle stabilization ; Enhanced Oil Recovery Techniques ; Industrial Gas Emission Control
Authors: Denis Vladlenov
Source:  (article, 2026-03-09)
Field: Chemistry — Organic Chemistry

CURRENT TRENDS IN THE DEVELOPMENT OF SCIENCE AND SOCIETY Introductions.Heterocyclic compounds occupy a significant place among physiologically active substances.Among the fused heterocycles, the most famous are indoles, quinazolines, benzodiazepines.Pharmacophores based on them are widely represented in the literature.Condensed systems with a seven-membered heterocyl are much less common.Nevertheless, among them, compounds have been identified that exhibit antitumor and antiviral properties, are used as psychotropic drugs [1].Keywords.Triazepines, 2-aminothiazole, natural amino acids, imidoyl chlorides, phosphorus pentachloride.This work describes the synthesis of adamantylcontaining triazepinones by condensation of natural amino acid derivatives with 2-aminothiazole.Adamantyl-containing derivatives of natural glycine, L and D alanine, L and D valine (1) were obtained by the method [2] from methyl esters of the corresponding amino acids and acid chlorides of adamantyl-1-carboxylic and adamantyl-1-acetic acids.The esters of adamantylcontaining amino acids were hydrolyzed under alkaline conditions to N-acyl--amino acids.Which may be of interest as objects for biological research [3].For the synthesis of imidoylchlorides (2) with retention of the ester group, a mixture of pyridine with phosphorus pentachloride was used [4].Condensation of imidoyl chlorides (2) with 2-aminothiazole occurred in the presence of triethylamine with the formation of condensed heterocycle (3). 100 %.', , - , , . , ' , - , - . Condensation ; Triethylamine ; Hydrolysis ; Amino acid ; Pyridine ; Phosphorus pentachloride ; Phosphorus Synthesis and Reactions of Organic Compounds ; Synthesis of Tetrazole Derivatives ; Scientific Research and Studies
Authors: Kieran Nehil-Puleo | Zhongyue Yang
Source: Digital Discovery (article, 2026-01-01)
Field: Materials Science — Biomaterials

FiberForge: enabling high-throughput simulations of the mechanical properties of helical fibrils FiberForge provides an end-to-end platform for molecular modeling and design of amyloid materials, enabling physics-based identification of sequences and polymorphs with targeted mechanical behavior. Identification (biology) ; Molecular dynamics ; Amyloid fibril ; Elasticity (physics) ; Sequence (biology) ; Multiscale modeling Supramolecular Self-Assembly in Materials ; Calcium Carbonate Crystallization and Inhibition ; Connective tissue disorders research
Authors: LM-Provers | Yuxiao Qu | Amrith Setlur | Jasper Dekoninck | Edward Beeching | Jia Li | Ian Wu | Lewis Tunstall | Aviral Kumar
Source: arXiv (Cornell University) (article, 2026-04-06)
Field: Materials Science — Materials Chemistry

QED-Nano: Teaching a Tiny Model to Prove Hard Theorems Proprietary AI systems have recently demonstrated impressive capabilities on complex proof-based problems, with gold-level performance reported at the 2025 International Mathematical Olympiad (IMO). However, the training pipelines behind these systems remain largely undisclosed, and their reliance on large "internal" models and scaffolds makes them expensive to run, difficult to reproduce, and hard to study or improve upon. This raises a central question: can small, open models also be trained to achieve competitive reasoning performance on difficult Olympiad-level math? In this paper, we answer this question by building QED-Nano, a 4B model post-trained for Olympiad-level proofs. Our training recipe has three stages: (1) supervised fine-tuning to imbue good proof-writing styles by distilling from DeepSeek-Math-V2, (2) reinforcement learning (RL) with rubric-based rewards, and (3) expanding RL with a reasoning cache, which decomposes long proofs into iterative summarize-and-refine cycles and enables stronger test-time reasoning. QED-Nano surpasses the proof-generation performance of much larger open models, including Nomos-1 and GPT-OSS-120B, and approaches the performance of proprietary models like Gemini 3 Pro, at a fraction of the inference cost. To support further research on open mathematical reasoning, we release the full QED-Nano pipeline, including the QED-Nano and QED-Nano-SFT models, the FineProofs-SFT and FineProofs-RL datasets, and the training and evaluation code. Mathematical proof ; Fraction (chemistry) ; Inference ; Reinforcement learning ; Olympiad ; Variety (cybernetics) Machine Learning in Materials Science ; Constraint Satisfaction and Optimization ; Logic, programming, and type systems