Experimental methods in chemical engineering: <scp>pH</scp>
Bibliographic record
Abstract
Abstract All chemical, biochemical, and biological processes depend on pH. Since the 1920s, when the first electrode was introduced to determine the concentration of hydrogen ions, pH measurement techniques have been evolving to fit the application at laboratory and industrial scales. These techniques include conventional methods based on electrical and optical methods like glass electrodes and variants. Most of the current methods still require a probe to be immersed in a solution. However, biomedical applications in the development stages involve non‐invasive probes that measure hydrogen ion concentration or electrical conductivity, which is related to the concentration of all ions. Instruments also measure both these properties simultaneously for water analysis, agriculture, and electrochemistry. pH drops by as much as 90% increasing temperature from 5–45°C (for MgSO 4 , NaCl, and an acetate buffer). The repeatability is excellent for a glass electrodes, which continues to be the measurement technique of choice for most laboratories, with a standard deviation of better than 0.08% for low molar concentrations (0.05 M) that increases to above 0.2% at high molar concentrations (>0.7 M). Besides the standard potentiometric methods, emerging techniques include ion‐sensitive field transistors, pH imaging, conductometric, acoustic microsensors, microcantilevers, and spectroscopy. In the first 6 months of 2020, Web of Science indexed almost 10 000 articles that mentioned pH as a keyword; most were in environmental sciences, multidisciplinary chemistry, and chemical engineering. Here, we review the latest developments, including spectroscopic methods, progress towards miniaturization, in particular for bio‐medical applications like skin and bio‐fluids, unconventional sampling, repeatability, and uncertainty.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.100 | 0.105 |
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".