Field-directed assembly of nanowires: identifying directors, disruptors and indices to maximize the device yield
Bibliographic record
Abstract
Individually-addressable nano-electro-mechanical (NEMS) devices have been used to demonstrate sensitive mass detection to the single-proton level, as well as neutral-particle mass spectrometry. The cost of individually securing or patterning such devices is proportional to their number or the chip area covered. This limits statistical support for new research, as well as paths to the commercial availability of extraordinarily sensitive instruments. Field-directed assembly of synthesized nanowires addresses this problem and shows potential for low-cost, large-area coverage with NEMS devices. For positive dielectrophoresis (pDEP) as the main assembly director, the space of field, geometric and material parameters is large, with combinations that can serve either as directors or disruptors for directed assembly. We seek parameter values to obtain the best yield, by introducing a rational framework to reduce trial-and-error. We show that sorting the disruptors by severity and eliminating those weakly coupled to the director, allows reduction of the parameter space. The remaining disruptors are then represented compactly by dimensionless parameters. In the example protocol chosen, a single dimensionless parameter, the yield index, allows minimization of disruptors by the choice of frequency. Following this, the voltage may be selected to maximize the yield. Using this framework, we obtained 94% pre-clamped and 88% post-clamped yield over 57000 nanowire sites. Organizing the parameter space using a director-disruptor framework, with economy introduced by non-dimensional parameters, provides a path to controllably decrease the effort and cost of manufacturing nanoscale devices. This should help in the commercialization of individually addressable nanodevices.
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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".