COSMOS: Correlation‐Optimized Spectral Modulation Optical Sensing
Source abstract
Abstract
Abstract Spectroscopy is a fundamental technique in chemical sensing. Correlation spectroscopy (CS) leverages prior knowledge of the known spectrum and can enhance detection sensitivity by actively illuminating the sample of interest with light of a designed spectral profile. However, existing CS methods are primarily laser‐based, which limits their use to samples with sharp absorption features. In this work, correlation‐optimized spectral modulation optical sensing (COSMOS) is proposed. It uses a digital micromirror device (DMD) to generate various optimized broadband spectra for CS to target samples with broadband absorption. A second‐order derivative (SOD)‐matching theory is introduced to maximize detection efficiency. COSMOS achieves a detection limit as low as 32 ppb for color dyes in solution and exhibits strong resistance to spectral interference, outperforming commercial spectrometers. With its high sensitivity and robustness, COSMOS shows excellent potential for environmental monitoring, biomedical diagnostics, industrial process control, and chemical analysis.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Spectroscopy and Laser Applications · Spectroscopy and Chemometric Analyses · Analytical Chemistry and Sensors
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mechanical Engineering National University of Singapore 9 Engineering Drive 1 Singapore 117575 Singapore
Funding
Ministry of Education - Singapore
References (43)
Show source-provided references
1. 10.1038/s41377-022-00853-110.1038/s41377-022-00853-1 ↗
2. 10.1038/s41467-021-27662-110.1038/s41467-021-27662-1 ↗
3. 10.1364/OE.16.00434710.1364/OE.16.004347 ↗
4. 10.1126/science.abe072210.1126/science.abe0722 ↗
5. 10.1002/ansa.20240001810.1002/ansa.202400018 ↗
6. 10.1109/JSEN.2022.316933410.1109/JSEN.2022.3169334 ↗
7. 10.3390/s2413438510.3390/s24134385 ↗
8. M.Zhou H.Chen L.Ren G.Sapiro L.Carin J.Paisley Advances in neural information processing systems 22 NeurIPS Vancouver2009 pp.2295–2303.
9. 10.1364/OE.20.00492710.1364/OE.20.004927 ↗
10. 10.1364/OE.24.00085910.1364/OE.24.000859 ↗
11. 10.1364/OL.35.00174910.1364/OL.35.001749 ↗
12. Wang Z. · 2018 · 49
13. 10.3390/s2217670710.3390/s22176707 ↗
14. 10.1038/s41598-024-61644-910.1038/s41598-024-61644-9 ↗
15. 10.1109/JPHOT.2014.236878510.1109/JPHOT.2014.2368785 ↗
16. 10.1364/OL.40.00418610.1364/OL.40.004186 ↗
17. 10.3389/fphy.2022.85396610.3389/fphy.2022.853966 ↗
18. 10.1073/pnas.190995311610.1073/pnas.1909953116 ↗
19. Z. H.Lim J. X. Y.Chew Q.Guan G.Zhou InMOEMS and Miniaturized Systems XXIII Vol.12899 SPIE Washington USA2024 pp.41–46.
20. 10.1016/j.slasd.2024.10019210.1016/j.slasd.2024.100192 ↗
21. 10.1002/cphc.20200046410.1002/cphc.202000464 ↗
22. 10.1007/s00340-014-5956-110.1007/s00340-014-5956-1 ↗
23. 10.1364/OE.26.00926310.1364/OE.26.009263 ↗
24. 10.1016/j.optlastec.2023.11032310.1016/j.optlastec.2023.110323 ↗
25. 10.1016/j.jms.2016.03.00210.1016/j.jms.2016.03.002 ↗
26. 10.1364/OL.46004210.1364/OL.460042 ↗
27. 10.3390/s2211417510.3390/s22114175 ↗
28. 10.1016/j.infrared.2021.10366110.1016/j.infrared.2021.103661 ↗
29. 10.1002/jbio.20100010710.1002/jbio.201000107 ↗
30. 10.1364/AO.48.00554610.1364/AO.48.005546 ↗
31. 10.1016/j.optlaseng.2022.10722210.1016/j.optlaseng.2022.107222 ↗
32. 10.1117/1.JBO.25.11.11050110.1117/1.JBO.25.11.110501 ↗
33. 10.1002/smtd.20240049810.1002/smtd.202400498 ↗
34. 10.1364/OE.39049010.1364/OE.390490 ↗
35. 10.1364/OE.43759410.1364/OE.437594 ↗
36. 10.1017/CBO978051180444110.1017/CBO9780511804441 ↗
37. Bellman R. E. · 2015
38. 10.1016/j.measurement.2019.10693210.1016/j.measurement.2019.106932 ↗
39. 10.1289/ehp.130727210.1289/ehp.1307272 ↗
40. 10.1080/1040834920805164610.1080/10408349208051646 ↗
41. 10.1351/pac19956708137710.1351/pac199567081377 ↗
42. 10.1038/s41566-018-0300-710.1038/s41566-018-0300-7 ↗
43. 10.1038/lsa.2016.14210.1038/lsa.2016.142 ↗