A critical analysis of electrospray techniques for the determination of accelerated rates and mechanisms of chemical reactions in droplets
Bibliographic record
Abstract
relative to macroscale bulk solutions. Despite electrospray's relative simplicity to both generate and detect reaction products in charged droplets using mass spectrometry, substantial complexity exists in how the electrospray process itself impacts the interpretation of the mechanism of these observed accelerated rates. ESI and ESSI are both coupled multi-phase processes, in which analytes in small charged droplets are transferred and detected as gas-phase ions with a mass spectrometer. As such, quantitative examination is needed to evaluate the impact of multiple experimental factors on the magnitude and mechanisms of reaction acceleration. These include: (1) evaporative concentration of reactants as a function of droplet size and initial concentration, (2) competition from gas-phase chemistry and reactions on experimental surfaces, (3) differences in ionization efficiency and ion transmission and (4) droplet charge. We examine (1-4) using numerical models, new ESI/ESSI-MS experimental data, and prior literature to assess the limitations of these approaches and the experimental best practices required to robustly interpret acceleration factors in micro- and nano-droplets produced by ESI and ESSI.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".