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Record W4321380274 · doi:10.1039/d2lc00439a

Acoustofluidics – changing paradigm in tissue engineering, therapeutics development, and biosensing

2023· review· en· W4321380274 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueLab on a Chip · 2023
Typereview
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsJohn Simon Guggenheim Memorial Foundation
KeywordsMicroscale chemistryAcoustic sensorComputer scienceNanotechnologySound waveBiocompatible materialSystems engineeringEngineeringAcousticsBiomedical engineeringMaterials science

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.063
GPT teacher head0.285
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it