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Record W2298365430 · doi:10.1149/ma2014-01/40/1486

Low Light CMOS Contact Imager with Integrated Dual Band Emission Filters for Fluorescence Detection

2014· article· en· W2298365430 on OpenAlexaff
Sanjeev Kumar Mahto, Orly Yadid-Pecht

Bibliographic record

VenueECS Meeting Abstracts · 2014
Typearticle
Languageen
FieldEngineering
TopicCCD and CMOS Imaging Sensors
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsImage sensorCMOSMaterials scienceComputer scienceFilter (signal processing)CMOS sensorComputer hardwareOptoelectronicsArtificial intelligenceComputer vision

Abstract

fetched live from OpenAlex

In recent times, the scope of optical imaging systems has been widened substantially to address the need for inexpensive, portable and miniaturized sensory system for applications such as in situ biomedical and environmental monitoring. Contact imaging is a relatively new and low-cost technique that offers significant advantages over conventional imaging. Conventional imaging systems often require bulky and expensive magnifying objectives along with photodetector devices. In contrast, contact imaging lacks the need of optical elements such as lenses between the sample and the sensor array, and therefore provides better light collection efficiency without optical loss and a significant improvement in sensitivity. Such unique features are achieved by contact imaging systems mainly due to direct coupling of the sensor array with the sample of interest via a thin-film filter. Fluorescence imaging is a powerful and highly sensitive tool that is widely used in diverse areas including diagnostic purposes, bioassays, pharmaceutical research and sensor development. Recently, contact image sensors fabricated using complementary metal-oxide semiconductor (CMOS) technology have been foreseen as an alternative to conventional fluorescence detection systems. Owing to the ability of CMOS imagers to integrate possibly all functions required for timing, exposure control, analog-to-digital (ADC) conversion, color processing, image enhancement and image compression on the same platform, they are considered key components of future micro-total-analysis-system (µTAS). CMOS imagers consume low power and require low voltage as well as facilitate a vast range of flexibility. However, fabrication of a high quality emission filter, also known as absorption filter, remains one of the major challenges in developing high sensitive CMOS-based fluorescence detection system. In particular, the integration of a CMOS image sensor with different types of filters that possess discrete cut-off wavelengths has so far been unexplored. Here, we demonstrate the fabrication of polyvinyl acetate (PVAc) based two distinct emission filters integrated with a single CMOS contact imager for fluorescence detection of two different analytes. PVAc was used as an adhesive for the emission filter due to its optical transparency, inexpensiveness and biocompatiblity properties. The fabrication process of emission filters involve dissolving PVAc in methanol, mixing it with absorbing specimens and finally drying up in air to harden the filter. Two distinct emission filters that possess discrete spectra are integrated onto a single CMOS image sensor. The integrated sensory system thus incorporates dual bandpass emission filters that are capable of selectively isolating fluorescence emission from two probes simultaneously. The thickness of the filters is optimized by calculating the desired signal-to-noise ratio (SNR) using Beer-Lambert’s law for liquids, Quantum Yield of the fluorophore and the Quantum Efficiency of the sensor array. Finally, the efficacy of the sensory system is tested by examining two different analytes i.e., calcium and pH. Overall, CMOS image sensor that combines dual bandpass emission filters holds much potential for low-cost and on-site deployable sensor development.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.713

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 head0.005
GPT teacher head0.192
Teacher spread0.187 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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