Deposition, patterning, and utility of conductive materials for the rapid prototyping of chemical and bioanalytical devices
Bibliographic record
Abstract
Rapid prototyping is a critical step in the product development cycle of miniaturized chemical and bioanalytical devices, often categorized as lab-on-a-chip devices, biosensors, and micro-total analysis systems. While high throughput manufacturing methods are often preferred for large-volume production, rapid prototyping is necessary for demonstrating and predicting the performance of a device and performing field testing and validation before translating a product from research and development to large volume production. Choosing a specific rapid prototyping method involves considering device design requirements in terms of minimum feature sizes, mechanical stability, thermal and chemical resistance, and optical and electrical properties. A rapid prototyping method is then selected by making engineering trade-off decisions between the suitability of the method in meeting the design specifications and manufacturing metrics such as speed, cost, precision, and potential for scale up. In this review article, we review four categories of rapid prototyping methods that are applicable to developing miniaturized bioanalytical devices, single step, mask and deposit, mask and etch, and mask-free assembly, and we will focus on the trade-offs that need to be made when selecting a particular rapid prototyping method. The focus of the review article will be on the development of systems having a specific arrangement of conductive or semiconductive materials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".