Improved Titrimetric Analysis of Formate/Formic Acid and Comparison with Ion Chromatography and Nuclear Magnetic Resonance Spectroscopy
Bibliographic record
Abstract
The advancements in renewable energy technologies brought about a significant interest in developing formic acid based chemical and electrochemical processes. These include the electroreduction of carbon dioxide to produce formic acid or direct electrooxidation of formic acid to generate electricity in a fuel cell. Considering the neutral to alkaline nature of electrolytes used in many of these processes, formic acid usually exists as a formate species. Unlike oxalate salt, the direct titration to quantify formate molarity using potassium permanganate (KMnO4) is difficult due to the formation of a suspended colored complex, which poses challenges in identifying the endpoint. Herein, we report two simple and efficient methods for formate and formic acid analysis, by back titration (FBT) using oxalate and iodine, respectively. These methods were shown to have an accuracy of more than 97% at a reduced cost when benchmarked against either nuclear magnetic resonance (NMR) spectroscopy or ion chromatography (IC). The effect of the analyte medium and optimization of the analytical procedure were also investigated. The formate quantification was found to vary insignificantly when different media, such as water, potassium hydroxide, potassium bicarbonate, or potassium sulfate, were used. This signifies the robustness and broader applicability of the FBT methods. Regarding the analytical steps, selecting the appropriate temperature, time, and molar ratio of participating species allowed the quantification of formate or formic acid with accuracy and precision. The analytical methods thus developed can provide alternative, cost-effective solutions for determining formate concentration for the research and development sector and for on-site analysis in industrial settings, where maintaining NMR and IC equipment may not be feasible.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".