ReviewAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025
Computational Modeling and Experimental Approaches for Understanding the Mechanisms of [FeFe]-Hydrogenase.
Review in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Probing the Role of Accessory Domains in Oxygen Stability of [FeFe]-Hydrogenases.Journal of the American Chemical Society · 2026Article
- Computational Modeling and Experimental Approaches for Understanding the Mechanisms of [FeFe]-Hydrogenase.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Learning from nature has emerged as a promising strategy for catalyst development, wherein the remarkable performance of catalysts selected by nature over billions of years of evolution serves as a basis for the creative design of high-performance catalysts. Hydrogenases, with their exceptional catalytic activity in hydrogen oxidation and production, have been employed as prototypes for human learning to achieve better catalyst design. A comprehensive understanding of hydrogenases' structures and catalytic mechanisms is crucial to replicate and exceed their performance. Computational modeling has proven to be a powerful tool for elucidating the reduction chemistry of [FeFe]-hydrogenases. This review overviews recent computational and experimental efforts, focusing on density functional theory (DFT) calculations applied to [FeFe] hydrogenases. It summarizes current knowledge on identifying active sites in [FeFe] hydrogenases and the reaction cycles involved in hydrogen metabolism.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.