Efficient Photochemical Vapor Generation from Low Concentration Formic Acid Media
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Herein, we report on surprisingly efficient photochemical vapor generation (PVG) of Ru, Re, and especially Ir, achieved from very dilute HCOOH media employing a thin-film flow-through photoreactor operated in flow injection mode. In the absence of added metal ion sensitizers, efficiencies near 20% for Ir and approximately 0.06% for Ru and Re occur in a narrow range of HCOOH concentrations (around 0.01 M), significantly higher than previously reported from conventionally optimized HCOOH concentrations (1–20 M). A substantial enhancement in efficiency, to around 9 and 1.5%, could be realized for Ru and Re, respectively, when 0.005 M HCOONa served as the PVG medium. The addition of metal ion sensitizers (particularly Cd 2+ and Co 2+ ) to 0.01 M HCOOH significantly enhanced PVG efficiencies to 17, 2.2, and 81% for Ru, Re, and Ir, respectively. Possible mechanistic aspects occurring in dilute HCOOH media are discussed, wherein this phenomenon is attributed to the action of 185 nm radiation available in the thin-film flow-through photoreactor. An extended study of PVG of Fe, Co, Ni, As, Se, Mo, Rh, Te, W, and Bi from both dilute HCOOH and CH 3 COOH was undertaken, and several elements for which a similar phenomenon appears were identified (i.e., Co, As, Se, Te, and Bi). Although use of dilute HCOOH media is attractive for practical analytical applications employing PVG, it is less tolerant toward dissolved gases and interferents in the liquid phase due to the likely lower concentrations of free radicals and aquated electrons required for analyte ion reduction and product synthesis.
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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) | 0.002 | 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".