Optimization and Applications of Slow-Proton-Exchange (SPE) Nuclear Magnetic Resonance pH Sensors
Bibliographic record
Abstract
The measurement of pH is ubiquitously important in biochemistry, clinical medicine, and industrial processes. There are still improvements to be made in this field, especially in terms of accuracy, non-invasiveness for biomedical applications and real-time monitoring. Herein, a new series of NMR sensors have been evaluated and applied that employ the Slow-Proton-Exchange (SPE) sensing mechanism. Three sensors, SPE1, SPE2, and TUC, exhibit the SPE phenomenon and thus can be used in unconventional conditions to accurately and reliably quantify pH. The second generation SPE2, optimized from the previous SPE1 with biocompatible pKa and broader operating pH window, has been used to detect real-time pH changes in a series of enzymatic hydrolysis reactions, simulating a metabolic process. All sensors SPE1, SPE2, and TUC have been utilized in DMSO to measure the strengths of organic acids and characterize activity, expanding the application scope from aqueous to organic solvents.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".