Emerging opportunities with metal-organic framework electrosynthetic platforms
Bibliographic record
Abstract
The development of electrochemical technologies is becoming increasingly important due to their growing part in renewable energy conversion and storage. Within this context, metal organic frameworks (MOFs) are finding an important role as electrocatalysts. Specifically, their molecularly defined structure across several lengths scales endows them functionality not accessible with conventional heterogeneous catalysts. To this end, this perspective will focus on the unique features within MOFs and their analogs that enable them to carry out electrocatalytic reactions in unique ways to synthesize fuels and value-added chemicals from abundant building blocks like CO2 and N2. We start with a brief overview of the initial advent of MOF electrocatalysts prior to moving to overview the forefront of the field of MOF-based electrosynthesis. The main discussion focuses on three principal directions in MOF-based electrosynthesis: multifunctional active sites, electronic modulation, and catalytic microenvironments. To conclude, we identify several challenges in the next stage of MOF electrocatalyst development and offer several key directions to take as the field matures.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".