Rapid Prototyping for Accelerated Establishment of Film Processing‐Performance Relationships in Silicon Phthalocyanine OFETs
Bibliographic record
Abstract
Abstract Understanding the complex relationships underlying the performance of organic electronic devices, such as organic field‐effect transistors (OFETs), requires researchers to navigate a multi‐dimensional parameter space that includes material design, solution formulation, fabrication parameters, and device geometry. Herein, a recently developed materials acceleration platform is demonstrated, named the RoboMapper, to perform direct on‐chip fabrication of OFETs by ultrasonic meniscus printing using silicon phthalocyanine (SiPc) derivatives as the semiconductor. OFETs using bis(tri‐n‐butylsilyl oxide) SiPc ((3BS)2‐SiPc) exhibited the best device performance characterized by the highest electron field‐effect mobility (µe). Through optical microscopy and grazing‐incidence wide‐angle X‐ray scattering (GIWAXS), the favorable performance of (3BS)2‐SiPc is attributed to the specific film morphology and molecular packing achieved with optimal print conditions. Investigating the impact of deposition parameters reveals the crucial role of solvent evaporation rate and print speed in achieving high‐quality film formation. Overall, optimal fabrication conditions for (3BS)2‐SiPc devices include slow print speeds and fast evaporating solutions achieved by using a mixture of co‐solvents and an elevated substrate temperature. The results of this work reveal distinct relationships between deposition conditions, film properties, and device performance for each SiPc derivative and emphasize the necessity of high throughput experimentation to comprehensively understand process‐performance relationships in organic semiconductors.
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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 | 0.000 |
| 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".