Building a Better Battery: Ionic Conductivity of Granular Hydrate Crystals
Bibliographic record
Abstract
Although solid state batteries are an alternative to conventional batteries, they generally produce low power due to their poor ionic conductivity. This study tested the ionic conductivities of powdered potassium sodium tartrate and potassium aluminum sulfate, two double salts that have garnered attention as potential solid state electrolytes. The substances were hypothesized on having abnormally high conductivities. Conductivity was measured using electrical impedance spectroscopy on an Arduino UNO. The results indicated that the double salts had indeed high conductivities for an ionic crystal at 5.6e-5±6e-6 S/m for PST and 7.8e-6±9e-7 S/m for PAS. However, the other 3 materials tested all produced similar values. Furthermore, one of the materials tested, sodium chloride, has a documented conductivity of 10-12 S/m. The high conductivities as well as the discrepancy between the measured and documented conductivities of sodium chloride were attributed to the powder form of the materials. Caking may have occurred and increased conductivity by increasing defect concentration as well as by creating moisture channels through which free ions could flow. These results have vast implications if a powdered electrolyte material may be successfully applied to existing fast ion conductors instead of insulators such as sodium chloride.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".