Acoustofluidics – changing paradigm in tissue engineering, therapeutics development, and biosensing
Bibliographic record
Abstract
For more than 70 years, acoustic waves have been used to screen, diagnose, and treat patients in hundreds of medical devices. The biocompatible nature of acoustic waves, their non-invasive and contactless operation, and their compatibility with wide visualization techniques are just a few of the many features that lead to the clinical success of sound-powered devices. The development of microelectromechanical systems and fabrication technologies in the past two decades reignited the spark of acoustics in the discovery of unique microscale bio applications. Acoustofluidics, the combination of acoustic waves and fluid mechanics in the nano and micro-realm, allowed researchers to access high-resolution and controllable manipulation and sensing tools for particle separation, isolation and enrichment, patterning of cells and bioparticles, fluid handling, and point of care biosensing strategies. This versatility and attractiveness of acoustofluidics have led to the rapid expansion of platforms and methods, making it also challenging for users to select the best acoustic technology. Depending on the setup, acoustic devices can offer a diverse level of biocompatibility, throughput, versatility, and sensitivity, where each of these considerations can become the design priority based on the application. In this paper, we aim to overview the recent advancements of acoustofluidics in the multifaceted fields of regenerative medicine, therapeutic development, and diagnosis and provide researchers with the necessary information needed to choose the best-suited acoustic technology for their application. Moreover, the effect of acoustofluidic systems on phenotypic behavior of living organisms are investigated. The review starts with a brief explanation of acoustofluidic principles, the different working mechanisms, and the advantages or challenges of commonly used platforms based on the state-of-the-art design features of acoustofluidic technologies. Finally, we present an outlook of potential trends, the areas to be explored, and the challenges that need to be overcome in developing acoustofluidic platforms that can echo the clinical success of conventional ultrasound-based devices.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".