Base metal chemistry and catalysis
Bibliographic record
Abstract
This perspective invites conversation concerning base metal catalysis as a green and sustainable solution in industrial and academic contexts. We explore what it means to be ‘sustainable’ and provide information on current efforts in synthetic chemistry. We establish a definition of a base metal and reflect on what considerations might apply to that definition. Throughout, we offer recent case studies, highlighting topics relevant to ligand development, metal sourcing, recyclability, and comparative reactivity using precious metal relatives. We challenge non-specialist readers to consider how, where, and why base metal catalysts are utilized. Finally, we offer social context, asking broad questions relevant to social acceptability. For example, decisions related to catalyst development are often driven by factors including costliness, safety, social adoptability, and performance. How can we move base metal catalysis to the forefront? Does society really care if materials are fabricated from nickel instead of palladium or platinum? How can our community guide this knowledge translation? Is this a job for us alone?
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".