Relationship between deposition techniques and nanoparticle dispersions for flexible and printed electronics
Bibliographic record
Abstract
Abstract Reverse micelles composed of polystyrene-b-poly(2-vinylpyiridine) have been used to synthesize nanoparticles composed of a wide range of materials, including metals, metal oxides, dielectrics, semiconductors perovskites, and core–shell nanoparticles. In this contribution, we examine the effect of deposition parameters on two-dimensional nanoparticle arrangements from colloidal solutions created using spin coating, dip coating, slot-die coating, and electrospray deposition. Despite the importance of achieving uniform coatings of ordered arrays of colloidal particles, previous studies have not thoroughly addressed this challenge. We show that the adjustability of interparticle distance depends on the deposition technique used and only occurs within the stable defect-free operating window of the deposition parameters. Establishing the specific operating window for each technique for a model system, we propose general guidelines that can be used for ensuring uniform coatings regardless of precursor loading and provide a guide for adjusting the deposition conditions when coating defects occur. We introduces a novel application of ellipsometry to evaluate interparticle spacing in nanoparticle arrays, enhancing our ability to assess film uniformity, allowing for quick and easy tuning of nanoparticle dispersion. Comparisons between spin, dip, and slot-die coating techniques reveal insights into the correlation between interparticle spacing and ordering, highlighting the importance of fitting relationships for various coating samples. This comprehensive comparison and discussion provide a roadmap for future research, outlining current challenges and trends and offering insights into achievable spacings and ordering in coating processes. This allows the classification of various deposition techniques with respect to their suitability for tailored applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".