The Impact of a NiFe-Based Metal Alloy on CO2 Conversion to CH4 and Carboxylic Acids in a Microbial Electrosynthesis Cell
Bibliographic record
Abstract
This study assessed the effects of NiFe-based metal catalysts on CO2 conversion to methane (CH4) and carboxylic acids in microbial electrosynthesis (MES) cells. A NiFeBi alloy, when electrodeposited on a conductive bioring cathode, significantly decreased CH4 production from 0.55 to 0.12 L (Lc d)−1 while enhancing acetate production to 1.0 g (Lc d)−1, indicating suppressed methanogenic activity and improved acetogenic activity. On the other hand, NiFeMn and NiFeSn catalysts showed varied effects, with NiFeSn increasing both CH4 and acetate production and suggesting potential in promoting both chain elongation and CO2 uptake. When these alloys were electrodeposited on a 3D-printed conductive polylactide (cPLA) lattice, the production of longer-chain carboxylic acids like butyrate and caproate increased significantly, indicating enhanced biocompatibility and nutrient delivery. The NiFeSn-coated cPLA lattice increased caproate production, which was further enhanced using an acetogenic enrichment. However, the overall throughput remained low at 0.1 g (Lc d)−1. Cyclic voltammetric analysis demonstrated improved electrochemical responses with catalyst coatings, indicating better electron transfer. These findings underscore the importance of catalyst selection and cathode design in optimizing MES systems for efficient CO2 conversion to value-added products, contributing to environmental sustainability and industrial applications.
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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".