Comparing the backfilling of mesoporous titania thin films with hole conductors of different sizes sharing the same mass density
Bibliographic record
Abstract
Efficient infiltration of a mesoporous titania matrix with conducting organic polymers or small molecules is one key challenge to overcome for hybrid photovoltaic devices. A quantitative analysis of the backfilling efficiency with time-of-flight grazing incidence small-angle neutron scattering (ToF-GISANS) and scanning electron microscopy (SEM) measurements is presented. Differences in the morphology due to the backfilling of mesoporous titania thin films are compared for the macromolecule poly[4,8-bis(5-(2-ethylhexyl)thiophen-2-yl)benzo[1,2-b;4,5-b′]dithiophene-2,6-diyl-alt-(4-(2-ethylhexyl)-3-fluorothieno[3,4-b]thiophene-)-2-carboxylate-2-6-diyl)] (PTB7-Th) and the heavy-element containing small molecule 2-pinacolboronate-3-phenylphenanthro[9,10-b]tellurophene (PhenTe-BPinPh). Hence, a 1.7 times higher backfilling efficiency of almost 70% is achieved for the small molecule PhenTe-BPinPh compared with the polymer PTB7-Th despite sharing the same volumetric mass density. The precise characterization of structural changes due to backfilling reveals that the volumetric density of backfilled materials plays a minor role in obtaining good backfilling efficiencies and interfaces with large surface contact.
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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) | 0.001 | 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".