Femtosecond laser 3D printing of optofluidic devices and systems
Bibliographic record
Abstract
Femtosecond laser micro-/nano-fabrication has been recognized as an enabling technology with unprecedented high precision and quality, which achieves the fabrication of various optoelectronic devices, including optofluidic devices for chemical and biomedical diagnostics with merits of versatile functionalities, compactness, high degree of integration, minimized waste, and low cost. Starting from the study on the fabrication of three-dimensional structures in dielectrics with the fundamental output of a femtosecond laser (wavelength at 800 nm, repetition rate of 1 kHz, and pulse energy up to 1 mJ), we report either a chemical etching-assisted femtosecond laser microfabrication technique or femtosecond laser induced multiphoton absorption technique to realize optofluidic devices. In this study, effects of fabrication parameters, such as laser energy, polarization of laser, and writing speed, have been investigated in order to identify optimal parameters for the realization of microstructures of different designs and specifications. Complex features have been designed and achieved to implement different functionalities. Fluidic movement in the optofluidic devices of different configurations, such as laminar flow and diffusion, has been explored for particle sorting. The applications of the femtosecond laser printed optofluidic devices and systems in sensing different environmental parameters, such as temperature, refractive index, pressure, and concentration, will be discussed, together with the revelation of different sensing mechanisms and the possibility of multiparameter sensing.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".