Assessing electron conduction mechanisms in highly organized surface water on a model neuronal lipid using quantum chemical methods
Bibliographic record
Abstract
A comprehensive characterization of key mechanisms underlying signal transmission within the human brain remains an unsolved problem. The neuronal surface, composed principally of phosphatidylcholine (POPC), has a known ordering effect on water. This produces highly organized water layers (OWLs) at the neuron–water interface—the neurochemical implications of which are not currently understood. The human brain is 75% water by volume, with folds and grooves to maximize surface area, suggesting that characterization of neuronal OWLs may contribute to an understanding of neuronal signal transmission. Previous experimental work has measured enhanced conductivity of POPC OWLs relative to bulk water. The mechanism underlying this conductivity is still debated. Using quantum chemical methods on a POPC–water interface model system, we present data characterizing OWL conductance. Non-equilibrium Green's function calculation results demonstrate that there is negligible electron transfer-based conductivity through the OWL at biological temperatures. This is consistent with existing studies suggesting the Grotthuss mechanism as the most likely explanation for experimentally observed enhanced conductivity at the POPC–water interface. The broader implications of enhanced proton conductivity at the neuron–water interface are discussed.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".