A Quantitative Determination of Dielectric Thresholds for DNA Precipitation by Ethanol v1
Bibliographic record
Abstract
Current DNA preservation practices rely on empirically determined volumetric rules rather than quantitative theoretical frameworks. Existing models overestimate ethanol requirements, limiting reproducibility and hindering the development of efficient protocols. A dielectric threshold–based model for predicting ethanol volumes during DNA precipitation establishes a framework that will reduce reagent waste and thus, environmental impact. This quantitative framework predicts the minimum ethanol requirement for human somatic DNA precipitation as a function of DNA molarity, derived from spectrophotometric absorbance data using the Beer-Lambert Law and Coulomb’s Law. For four different DNA conditions, 12 replicate samples were prepared across an ethanol concentration gradient from 54% to 90% v/v in 2% increments (n = 12 per concentration). DNA molarity and the dielectric constants of ethanol (ε = 24.5) and water (ε = 80.1) were used to calculate the dielectric constant of each solution, enabling determination of the threshold for DNA precipitation. DNA precipitation begins at 58-60% ethanol and reaches a maximum yield of 95% at 72% ethanol. Across all four DNA concentrations (2, 5, 8, and 10 ng/µL), replicates demonstrated consistent dielectric thresholds and precipitation profiles, generating a reproducible sigmoidal recovery curve. ε = 40.07 is the dielectric threshold at which DNA precipitates optimally, corresponding to the point at which our yield plateaus (varying ± 3% across replicates). These results validate the dielectric-threshold framework, showing a slight shift in the onset relative to theoretical predictions, while demonstrating that ethanol volumes can be optimized without compromising DNA integrity. Adopting the dielectric-threshold framework reduces ethanol requirements by 3% compared to standard DNA precipitation protocols, while ensuring optimal yield and quality. Standardizing this model will improve reproducibility, enable automation, and reduce biohazardous and flammable waste across high-throughput workflows.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".