Constructing Nanocaged Enzymes for Synergistic Catalysis of CO<sub>2</sub> Reduction
Bibliographic record
Abstract
Abstract Promoting the activity of biological enzymes under in vitro environment is a promising technique for bioelectrocatalytic reactions, such as the conversion of carbon dioxide (CO2) into valuable chemicals, which is a promising strategy to address the environmental issue of CO2 in the atmosphere; however, this technique remains challenging. Herein, a nanocage structure for enzyme confinement is synthesized to enable the in situ encapsulation of formate dehydrogenase (FDH) in a porous metal–organic framework, which acts as a coenzyme and boosts the hybrid synergistic catalysis using enzymes. This study reveals that the synthesized FDH@ZIF‐8 nanocage‐structured hybrid (CSH) catalyst exhibits an improved catalytic ability of the enzymes and increases the hydrophobicity of the electrode and its affinity to CO2. Thus, CSH can trap CO2 and control its microenvironments. The CSH catalyst boosts the conversion rate of CO2 to formic acid (HCOOH) to 28 times higher than that when using pure FDH. The in situ attenuated total reflectance surface‐enhanced infrared absorption spectroscopy (ATR‐SEIRAS) spectra indicates that OCHO* is the key intermediate. Density functional theory (DFT) calculations show that CSH has extremely low overpotential and is particularly effective for producing formate. This protection architecture for enzymes considerably promotes their biological application under in vitro environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".