Complementary Metal-Oxide-Semiconductor-Based Sensing Platform for Trapping, Imaging, and Chemical Characterization of Biological Samples
Bibliographic record
Abstract
Complementary metal-oxide-semiconductor (CMOS) imaging sensors provide the unique opportunity for combining lensless imaging with new modalities that enable sample handling and chemical characterization. In this study, we present a new CMOS-based sensing platform for trapping, imaging, and chemical characterization of samples via SERS (CMOS-TrICC). The SERS substrate is fabricated directly on a CMOS imaging sensor by depositing a thin metallic layer on top of the CMOS microlenses. SERS activity is based on square unit cell patterned, closely spaced, micrometer-sized microlenses on the surface of the imaging sensor. Morphological analysis of the surface revealed an intracavity depth of approximately 700 nm and height-dependent width ranging from a minimum of just a few nm between two lenses to a maximum of 1400 nm, with a flat valley exhibiting approximately 300 nm width at the bottom between four lenses. These morphological features concentrate electromagnetic fields into SERS hot spots and at the same time help trap nanometer-sized particles in the wells created by the microlenses. The strongest plasmonic effect is expected in the gaps between the microlenses. Simulations were used to map the distribution of the electromagnetic field enhancement on the SERS substrate surface and at a distance above it. The performance of the SERS substrate and its dependence on the silver layer thickness were examined using 4-aminotheophenol and rhodamine 6G with the experimental enhancement factor measured to be 5.0 × 104. We demonstrated the use of this substrate for parallel trapping of 100 nm nanospheres and extracellular vesicles (EVs) in the gaps between the microlenses and SERS characterization of these particles in the hot spots. SERS intensities are 2 orders of magnitude higher in the nanogaps between the microlenses (intracavity area) than on top of the microlenses, and for polystyrene, they exhibited signature peaks centered at 1000 and 1600 cm–1. SERS spectra of small EVs collected from intracavity areas where EVs were trapped show peaks known to arise from their main biochemical constituents, such as lipids, proteins, and nucleic acids. While the surface of the CMOS imaging sensor became SERS active by the addition of the metallic layer, the imaging capability is maintained and provides the opportunity for direct on-chip lensless imaging with spatial resolution limited by the pixel size, opening new directions for integrated (bio)sensing devices.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".