Design and implementation of integrated GHz frequency capacitance cytometer with aF sensitivity for single cell characterization
Bibliographic record
Abstract
This thesis is focused on the design, implementation, and measurements of integrated electronic sensors developed for detection and analysis of single biological cells in microfluidic systems. This work introduces three separate designs. The first is a microwave frequency reflectometer on a printed circuit board (PCB) which operates at ~1.8 GHz, and achieves ~1.25 aF capacitance sensitivity in less than 100 mV sensing voltage. It is used for detection of 5.7 μm Poly-Styrene Spheres (PSS) and Chinese Hamster Ovary cells (CHO). This sensor was successfully used to detect DEP response of PSS with a sensitivity close to the connectorized resonator based microwave interferometer. The second design is a differential ring oscillator based capacitance sensor on a PCB. The oscillation frequency of two oscillators (one connected to detection microelectrodes) are compared by an XOR gate, and is monitored using a frequency counter. It achieves ~180 aF sensitivity in 100 ms averaging time and ~2.5 V sensing voltages. It is used for detection of water with Isopropyl Alcohol contents up to 1%, and for detection of 15 µm PSS. The third design is an integrated DEP cytometer sensor. It is composed of an optimized capacitance sensor implemented using 0.35 μm CMOS technology, on which a machined PMMA microfluidic is clamped to provide a path for cells to flow. The capacitance sensor operates at 500 MHz/1.4 GHz, and achieves ~14 aF sensitivity in 100 ms averaging time and ~1.4 V sensing voltage. The sensor is used for detection of 10 μm PSS and CHO cells. It is used to observe shifts in PSS and CHO cells signatures normalized peak difference histograms when positive or negative DEP forces are applied. Finally, equations of the capacitance sensor noise and sensitivity are calculated. Calculated noise in frequency and time domains are compared with measurement results and suggestions are given to improve this sensor. These integrated sensors are small in size, low cost, portable and easily reproducible compared with bulky electrical sensors. They are markerless compared with conventional single cell analysis assays. These designs introduce new approaches for detection of biomaterials in a wide range of microfluidic applications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".