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Record W2079296135 · doi:10.1115/imece2008-68032

Design and Numerical Modeling of an On-Board Chemical Release Module for Motion Control of Bacteria-Propelled Swimming Micro-Robots

2008· article· en· W2079296135 on OpenAlexaff
Bahareh Behkam, Amrinder S. Nain, Cristina H. Amon, Metin Sitti

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRobotTransient (computer programming)MicrofluidicsMotion controlWaxMotion (physics)Materials scienceBiological systemComputer scienceNanotechnologySimulationArtificial intelligenceComposite material

Abstract

fetched live from OpenAlex

On/off motion control of bacteria-propelled synthetic bodies was previously achieved using a chemical switching technique. A chemical agent (CuSO4) directly binds to the rotor of the flagellar motor inhibiting it. When desired, a second chemical agent (EDTA) is introduced, which binds to the CuSO4 molecules, freeing the motor and allowing the bacteria to resume its motion [1]. To facilitate localized delivery of the control agents and reduce the diffusion time, we propose an on-board chemical release scheme for the first time. The proposed microfluidic motion control module contains two sets of optomechanically responsive nanocomposite paraffin wax micro-valves which can be independently actuated. To demonstrate the feasibility of the concept, a transient two-dimensional mass transfer numerical model is developed and the transient concentration profiles of the diffused chemicals at different locations on the robot body is studied. The results of this work provide crucial information required to determine the number, size, and location of the required micro-valves.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.497
Threshold uncertainty score0.441

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.023
GPT teacher head0.230
Teacher spread0.208 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2008
Admission routes1
Has abstractyes

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