Columnar organization of stack-assembled trimesic acid on graphene
Bibliographic record
Abstract
The stack-assembly of trimesic acid molecules into a highly organized columnar structure and their adsorption on graphene has been investigated by a DFT-based ab initio calculation method. Trimesic acid (TMA, benzene-1,3,5-tricarboxylic acid) constitutes an interesting building block for intermolecular hydrogen-bonding architecture by creating a strong net dipole moment which favors a symmetric π-stacking of molecular wire. Both the single orientation (syn) and alternating orientation (anti) of two- and three-unit TMA configurations are optimized, and determine that anti or AB pattern TMA wire is energetically more favorable than the syn case. Meanwhile, a decreasing band gap during the formation of the molecular wire proves the presence of delocalized π-electrons over the entire stack-assembly. The adsorption energy for a columnar TMA stack on graphene was found to be roughly less than of a single TMA adsorbed on graphene. The relative contribution of hydrogen bonding to column packing energy showed to be comparative and reasonable, with the energy of a conventional hydrogen bond. The magnitude of the band gap opening appears strongly correlated with the breaking of the symmetry of π-states of graphene by the TMA columnar patterning on the surface. Our results suggest that a stack-assembled molecular could be used to tune and control the electronic properties of graphene.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".