Old acid, new chemistry. Negative metal anions generated from alkali metal oxalates and others
Bibliographic record
Abstract
A brief search in Sci Finder for oxalic acid and oxalates will reward the researcher with a staggering 129,280 hits. However, the generation of alkali metal and silver anions via collision-induced dissociation of the metal oxalate anion has not been previously been reported, though Tian and coworkers recently investigated the dissociation of lithium oxalate. The exothermic decomposition of alkali metal oxalate anion to carbon dioxide in the collision cell of a triple quadrupole mass spectrometer leaves no place for the electron to reside, resulting in a double electron-transfer reaction to produce an alkali metal anion. This reaction is facilitated by the negative electron affinity of carbon dioxide and, as such, the authors believe that metal oxalates are potentially unique in this respect. The observed dissociation reactions for collision with argon gas (1.7-1.8 × 10(-3) mbar) for oxalic acid and various alkali metal oxalates are discussed and summarized. Silver oxalate is also included to demonstrate the propensity of this system to generate transition-metal anions, as well.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".