Micro-electro-fluidic module to control magnetotactic bacteria for micromanipulation tasks under an optical microscope
Bibliographic record
Abstract
This project describes a Multi-Chip Module (MCM) that contains a microelectronic circuit and a microfluidic device that could be combined to implement a "bacterial microfactory". The microchip contains two decoders connected to arrays of horizontal and vertical wires respectively, forming a matrix used to process commands received from an external computer. The electrical current flowing through the matrix is generated from internal voltage-to-current converters. The electrical current circulating through a metal conductor generates a magnetic field that is used to guide the movement of Magnetotactic Bacteria (MTB) in the microfluidic device. The dedicated microfluidic device is micro-fabricated on a glass wafer. Preliminary results show that a single MC-1 MTB can push a 2 μm microbead at speeds reaching 100μm/s under the control of an external magnetic field of less than 10 Gauss. A Carl Zeiss microscopy software (AxioVision) is used to control and configure the Axio Imager Z1 optical microscope and allows us to develop customized plug-in with Visual Basic for Application (VBA). The control electronic die was hence programmed as a VBA module, simplifying interoperability between the control, data recordings and microscopy observations. The parallel port of an Intel Pentium 4, 3.0 GHz equipped with 2.87 Go of RAM running Windows XP was used to communicate with the circuit. Connected to the parallel port, two demultiplexers interface the chip and the port. Patterns to control the bacteria such as left-right and up-down displacements were implemented and tested. Other more complex patterns to capture, attract and repel the bacteria from the center of the chip were also designed and validated.
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 | 0.001 | 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".