A microfluidic-enabled combinatorial formulation and integrated inkjet printing platform for evaluating functionally graded material blends
Bibliographic record
Abstract
Sample library preparation is a central step in the process of evaluating materials with the general aim of efficient library formulation while minimizing resource consumption. We demonstrate here the first implementation of a microfluidic-enabled thin film sample library formulation platform with integrated inkjet printing capability for directly patterning these libraries with reduced material wastage. System development and general performance screening protocol for these patterned thin films are described. We study the combinatorial formulation capabilities of this system by focusing on some practical case studies for probing the electrical conductivity in organic, biocompatible and electroactive polymer/additive (PEDOT:PSS/DMSO and PEDOT:PSS/EG) blends. Functionally-graded thin film libraries are prepared by mixing ink components and directly dispensing the processed blends into programmed geometries using the integrated platform. Electrical and morphological characterization of these printed thin film libraries is conducted to validate the formulation efficacy of the platform. Interrogating these printed libraries, we were able to iteratively identify the location of conductivity maxima for the studied blends and corroborate the morphological basis of this enhancement with established theories.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".