A Modular Flow-through Platform for Spectroelectrochemical Analysis
Bibliographic record
Abstract
Electrochemical flow systems are broadly used in research and applied fields such as environmental, industrial and medical monitoring because they cost-effectively determine concentrations, yield energy data (redox potential) and elucidate reaction mechanism via kinetic analysis. The use of a flow system enables the automation of a process and can provide a much needed design flexibility. Moreover, the combination of reaction-oriented electrochemical flow systems with species-focused spectroscopy enables complete analysis of reactions involving multiple electron transfer steps, as well as unstable intermediates. In this work, we present a new type of flow platform for electrochemical and spectroelectrochemical measurements is presented. Finite element method simulations confirm that the hydrodynamic profile within the device is not turbulent, and provides an analytical platform for the investigation of homogenous kinetics, radical lifetimes and reaction mechanisms. The modular “plug and play” configuration of the platform allows one to carry out electrochemistry and spectroscopy individually or simultaneously. Specific demonstrations of electroanalytical measurements using the flow system platform includes voltammetric analysis of organometallic compounds and quantitative analysis of ascorbic acid in commercial orange juice samples. Combined spectroelectrochemical (SEC) demonstrations include electrochemical luminescence of ruthenium compounds and ligand exchange reactions of iron complexes using UV-Vis spectroscopy. Noyhouzer et al, Anal. Chem., 2017, 89 (10), pp 5246–5253 Figure 1
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".