Dipole-induced effects on charge transfer and charge transport. Why do molecular electrets matter?
Bibliographic record
Abstract
Charge transfer (CT) and charge transport (CTr) are at the core of life-sustaining biological processes and of processes that govern the performance of electronic and energy-conversion devices. Electric fields are invaluable for guiding charge movement. Therefore, as electrostatic analogues of magnets, electrets have unexplored potential for generating local electric fields for accelerating desired CT processes and suppressing undesired ones. The notion about dipole-generated local fields affecting CT has evolved since the middle of the 20th century. In the 1990s, the first reports demonstrating the dipole effects on the kinetics of long-range electron transfer appeared. Concurrently, the development of molecular-level designs of electric junctions has led the exploration of dipole effects on CTr. Biomimetic molecular electrets such as polypeptide helices are often the dipole sources in CT systems. Conversely, surface-charge electrets and self-assembled monolayers of small polar conjugates are the preferred sources for modifying interfacial electric fields for controlling CTr. The multifaceted complexity of such effects on CT and CTr testifies for the challenges and the wealth of this field that still remains largely unexplored. This review outlines the basic concepts about dipole effects on CT and CTr, discusses their evolution, and provides accounts for their future developments and impacts.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".