IR spectroscopy of aqueous alkali halide solutions: Pure salt-solvated water spectra and hydration numbers
Bibliographic record
Abstract
Extrapolation techniques were used to obtain pure salt-solvated water spectra from the attenuated total reflection infrared spectra (ATR-IR) of aqueous solutions of the nine alkali halide salts LiCl, NaCl, KCl, CsCl, NaBr, KBr, NaI, KI, and CsI and the alkaline–earth chloride salt MgCl2. These salts ionize completely in water. The ions by themselves do not absorb in the IR, but their interactions with water can be observed and analyzed. A pure salt-solvated water spectrum is easier to analyze than that of a combined solution of pure water and salt-solvated water. Although the salt-solvated water spectra examined have distinctive signatures, they can be classified in three categories: those similar to NaCl; those not similar to NaCl; and MgCl2, in a class by itself. Each of the pure salt-solvated water spectra differs from that of liquid water, though the number of bands is the same. From the Gaussian band fitting, we found that the positions of the bands were fairly constant, whereas their intensities differed. The salt hydration numbers were determined: for NaCl, KCl, NaBr, KBr, and CsI solutions it is 5; for KI and MgCL2 it is 4; for NaI it is 3.5; for CsCl it is 3; and for LiCl it is 2. From these results we found that each pair of ions (monoatomic ions) of the ten salt solutions studied are close bound and form a complex in a cluster organization with a fixed number of water molecules.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".