Hasil untuk "Organic chemistry"

Yong Wang, Xinchen Wang, M. Antonietti

A. Hashmi

R. Porta, M. Benaglia, A. Puglisi

C. Sanchez, B. Julián, P. Belleville et al.

Zhijie Chen, Sylvia L. Hanna, Louis R. Redfern et al.

Abstract In the past two decades, reticular chemistry has developed into a powerful tool for the design and synthesis of porous, crystalline framework materials. The discovery of the first hexanuclear, zirconium cluster-based MOF (i.e. UiO-66; fcu net) led to a vast library of functional Zr-MOFs with various properties. The versatile connectivity of zirconium hexanuclear clusters and the adaptable tunability of organic ligands have resulted in the rational synthesis of a large set of Zr-MOFs based on edge-transitive nets; these nets commonly exists in crystalline network structures as suggested by reticular chemistry. In this review, we summarize recent advances in the synthesis of zirconium cluster-based MOFs in the light of reticular chemistry design principles. Isoreticular tuning of MOF parent structures and post-synthetic modification of Zr-MOFs for targeted applications are also deliberated.

R. Pearson, J. Songstad

K. B. Wiberg, W. Trahanovsky

John D. Roberts, M. Caserio

C. Kappe, Alexander Stadler

Chi‐Huey Wong, G. Whitesides

Giacomo E. M. Crisenza, P. Melchiorre

Can organic chemistry mimic nature in efficiency and sustainability? Not yet, but recent developments in photoredox catalysis animated the synthetic chemistry field, providing greener opportunities for industry and academia.

Mohammadmahdi Vahediahmar, Matthew A. McDonald, Feng Liu

Crystal structure prediction is a long-standing challenge in materials science, with most data-driven methods developed for inorganic systems. This leaves an important gap for organic crystals, which are central to pharmaceuticals, polymers, and functional materials, but present unique challenges, such as larger unit cells and strict chemical connectivity. We introduce a flow-matching model for predicting organic crystal structures directly from molecular graphs. The architecture integrates molecular connectivity with periodic boundary conditions while preserving the symmetries of crystalline systems. A bond-aware loss guides the model toward realistic local chemistry by enforcing distributions of bond lengths and connectivity. To support reliable and efficient training, we built a curated dataset of organic crystals, along with a preprocessing pipeline that precomputes bonds and edges, substantially reducing computational overhead during both training and inference. Experiments show that our method achieves a Match Rate more than 10 times higher than existing baselines while requiring fewer sampling steps for inference. These results establish generative modeling as a practical and scalable framework for organic crystal structure prediction.

Xianrui Zhong, Bowen Jin, Siru Ouyang et al.

Retrieval-augmented generation (RAG) has emerged as a powerful framework for enhancing large language models (LLMs) with external knowledge, particularly in scientific domains that demand specialized and dynamic information. Despite its promise, the application of RAG in the chemistry domain remains underexplored, primarily due to the lack of high-quality, domain-specific corpora and well-curated evaluation benchmarks. In this work, we introduce ChemRAG-Bench, a comprehensive benchmark designed to systematically assess the effectiveness of RAG across a diverse set of chemistry-related tasks. The accompanying chemistry corpus integrates heterogeneous knowledge sources, including scientific literature, the PubChem database, PubMed abstracts, textbooks, and Wikipedia entries. In addition, we present ChemRAG-Toolkit, a modular and extensible RAG toolkit that supports five retrieval algorithms and eight LLMs. Using ChemRAG-Toolkit, we demonstrate that RAG yields a substantial performance gain -- achieving an average relative improvement of 17.4% over direct inference methods. We further conduct in-depth analyses on retriever architectures, corpus selection, and the number of retrieved passages, culminating in practical recommendations to guide future research and deployment of RAG systems in the chemistry domain. The code and data is available at https://chemrag.github.io.

Yueqing Zhang, Wentao Liu, Yan Zhang et al.

Large language models (LLM) have achieved impressive progress across a broad range of general-purpose tasks, but their effectiveness in chemistry remains limited due to scarce domain-specific datasets and the demand for precise symbolic and structural reasoning. Here we introduce ECNU-ChemGPT(name after East China Normal University), a chemistry-specialized LLM engineered for deep chemical knowledge understanding and accurate retrosynthetic route planning. Our approach is distinguished by four key strategies: structured prompt-based knowledge distillation from authoritative chemistry textbooks to construct a high-quality question-answering dataset; domain-specific prompt engineering using curated chemical keywords, combined with LLMs APIs for data derivation and knowledge distillation; large-scale fine-tuning on a meticulously cleaned and enriched Pistachio reaction dataset to enhance retrosynthesis prediction accuracy; and integration of BrainGPT, a dynamic multi-model scheduling framework that enables task-specific invocation of multiple specialized models trained for diverse chemistry-related tasks. ECNU-ChemGPT exhibits superior performance on chemistry question-answering and retrosynthetic planning benchmarks, outperforming leading general-purpose models-including Deepseek-R1, Qwen-2.5, and GPT-4o. In retrosynthesis, it achieves a Top-1 accuracy of 68.3% on the USPTO_50K dataset and successfully reconstructed 13 complete experimental pathways for real-world drug molecules from medicinal chemistry journals. These results underscore the effectiveness of domain-adapted fine-tuning combined with dynamic multi-model task scheduling, providing a scalable and robust solution for chemical knowledge question answering and retrosynthetic planning.

Ryotaro Nakazawa, Masaya Kitaoka, Ryota Kaimori et al.

Accurate determination of low-density electronic states in the bandgap (in-gap states) is crucial for optimizing the performance of organic optoelectronic devices. Derivative photoelectron yield spectroscopy (PYS) is employed to estimate the density of states (DOS) of in-gap states. However, low-energy photons in PYS can generate excitons and anions in organic semiconductors, raising questions about whether derivative PYS spectra truly represent the DOS. We revealed that PYS signals originate from the single-quantum external photoelectron effect (SQEPE) of in-gap states, SQEPE of the singly occupied molecular orbital (SOMO) of anions, and the biphotonic electron emission (BEE) effect via exciton fusion. Because BEE signals mask the DOS contribution, derivative PYS misestimates the DOS of in-gap states. In contrast, constant final state yield spectroscopy (CFS-YS) reliably determines the DOS by separating these components. For a tris(8-hydroxyquinoline) aluminum (Alq3) film, CFS-YS revealed the DOS of in-gap and SOMO states over six orders of magnitude, clarifying why the Alq3 layer works effectively in organic light-emitting diodes. In the devices, BEE can act as carrier-generation and degradation processes, and CFS-YS can also probe it. We provide the practical guidelines of low-energy photon measurements for DOS determination, such as measurements of photon-flux dependency.

Zhiling Zheng

Artificial intelligence (AI) and data science are transforming chemical research, yet few formal courses are tailored to synthetic and experimental chemists, who often face steep entry barriers due to limited coding experience and lack of chemistry-specific examples. We present the design and implementation of AI4CHEM, an introductory data-driven chem-istry course created for students on the synthetic chemistry track with no prior programming background. The curricu-lum emphasizes chemical context over abstract algorithms, using an accessible web-based platform to ensure zero-install machine learning (ML) workflow development practice and in-class active learning. Assessment combines code-guided homework, literature-based mini-reviews, and collaborative projects in which students build AI-assisted workflows for real experimental problems. Learning gains include increased confidence with Python, molecular property prediction, reaction optimization, and data mining, and improved skills in evaluating AI tools in chemistry. All course materials are openly available, offering a discipline-specific, beginner-accessible framework for integrating AI into synthetic chemistry training.

Marlena Stielow, Łukasz Fijałkowski, Aidas Alaburda et al.

Studies have shown that sodium-glucose cotransporter type 2 (SGLT2) inhibitors not only help lower blood glucose levels but also offer cardioprotective effects, reduce the progression of heart failure, and may even slow the progression of aortic stenosis. The mechanisms of these beneficial properties are thought to involve multiple pathways, including reducing inflammation, oxidative stress, and improving cellular energy metabolism. Advancing knowledge about the mechanisms of action of these drugs and their effects on the course of the aforementioned diseases has become the subject of intensive clinical and scientific research. This publication aims to provide insight into the role of SGLT2 inhibitors in the context of diabetes mellitus, heart failure and acute coronary syndrome, through clinical analysis, mechanistic insights and comparison of the effects of these drugs.

Cingir Koker Sahika, Dogan Turacli Irem

The capacity to sense and respond to cellular energy stress is an important factor in tumorigenesis. We aimed to investigate the proliferation patterns of KRAS-mutant NSCLC cell lines (A549, Calu-1, H2009) under high (HG) and low glucose (LG) conditions w/wo DCA, which alters metabolic pathways and promotes oxidative phosphorylation.

R. Silverman

Debora Maria Conti, Claudia Urru, Giovanna Bruni et al.

The NASICON-structured Na₃MnZr(PO₄)₃ compound is a promising high-voltage cathode material for sodium-ion batteries (SIBs). In this study, an easy and scalable electrospinning approach was used to synthesize self-standing cathodes based on Na₃MnZr(PO₄)₃ loaded into carbon nanofibers (CNFs). Different strategies were applied to load the active material. All the employed characterization techniques (X-ray powder diffraction (XRPD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy-dispersive X-ray spectroscopy (EDS), thermal gravimetric analysis (TGA), and Raman spectroscopy) confirmed the successful loading. Compared to an appositely prepared tape-cast electrode, Na₃MnZr(PO₄)₃/CNF self-standing cathodes demonstrated an enhanced specific capacity, especially at high C-rates, thanks to the porous conducive carbon nanofiber matrix. Among the strategies applied to load Na₃MnZr(PO₄)₃ into the CNFs, the electrospinning (vertical setting) of the polymeric solution containing pre-synthesized Na₃MnZr(PO₄)₃ powders resulted effective in obtaining the quantitative loading of the active material and a homogeneous distribution through the sheet thickness. Notably, Na₃MnZr(PO₄)₃ aggregates connected to the CNFs, covered their surface, and were also embedded, as demonstrated by TEM and EDS. Compared to the self-standing cathodes prepared with the horizontal setting or dip–drop coating methods, the vertical binder-free electrode exhibited the highest capacity values of 78.2, 55.7, 38.8, 22.2, 16.2, 12.8, 10.3, 9.0, and 8.5 mAh/g at C-rates of 0.05C, 0.1C, 0.2C, 0.5C, 1C, 2C, 5C, 10C, and 20C, respectively, with complete capacity retention at the end of the measurements. It also exhibited a good cycling life, compared to its tape-cast counterpart: it displayed higher capacity retention at 0.2C and 1C, and, after cycling 1000 cycles at 1C, it could be further cycled at 5C, 10C, and 20C.