Reverse Titration Using Tablets for Accurate Water Hardness Measurement with Improved Resistance to Interference
Bibliographic record
Abstract
We report a novel tablet-based reverse titration system for rapid, point-of-use measurement of water hardness, overcoming key limitations of conventional EDTA titration. Reagents are encapsulated in pullulan matrix giving two separate tablets. The first tablet contains the Eriochrome black T (EBT) and N-cyclohexyl-3-aminopropanesulfonic acid (CAPS) buffer, while the second encapsulates ethylenediaminetetraacetic acid (EDTA) disodium salt dihydrate. The system employs a trimodal detection strategy: qualitative screening via immediate color change with the EBT tablet, semi-quantitative estimation through combined tablet dissolution and adjusting the sample volume to a reference level, and quantitative determination using reverse titration, where water is gradually added until the red wine endpoint appears. This approach enhances interference tolerance from competing metal ions and improves accuracy over traditional methods. Testing with real water samples showed excellent agreement with standard titration. The tablets remain stable for over seven months, and the system eliminates the need for skilled personnel, laboratory equipment, or bulky instrumentation. This low-cost, user-friendly, and interference-tolerant platform enables rapid and accurate water hardness assessment at the point of use.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".