Microscale diamond protection for a ZnO coated fiber optic sensor

Kosowska, Monika; Listewnik, Paulina; Majchrowicz, Daria; Rycewicz, Michał; Bechelany, Mikhael; Fleger, Yafit; Chen, Mingzhou; Fixler, Dror; Dholakia, Kishan; Jędrzejewska‐Szczerska, Małgorzata

doi:10.1038/s41598-020-76253-5

Cited by 9 publications

(4 citation statements)

References 31 publications

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“…The light source operated at the central wavelength of 1550 ± 20 nm with a spectral width of 35 nm. The Fabry–Perot resonance cavity was formed by the polished fiber end-face and a silver mirror 29 , 30 .…”

Section: Methodsmentioning

confidence: 99%

Predictions of cervical cancer identification by photonic method combined with machine learning

Kruczkowski

Drabik-Kruczkowska

Marciniak

et al. 2022

Sci Rep

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Cervical cancer is one of the most commonly appearing cancers, which early diagnosis is of greatest importance. Unfortunately, many diagnoses are based on subjective opinions of doctors—to date, there is no general measurement method with a calibrated standard. The problem can be solved with the measurement system being a fusion of an optoelectronic sensor and machine learning algorithm to provide reliable assistance for doctors in the early diagnosis stage of cervical cancer. We demonstrate the preliminary research on cervical cancer assessment utilizing an optical sensor and a prediction algorithm. Since each matter is characterized by refractive index, measuring its value and detecting changes give information about the state of the tissue. The optical measurements provided datasets for training and validating the analyzing software. We present data preprocessing, machine learning results utilizing four algorithms (Random Forest, eXtreme Gradient Boosting, Naïve Bayes, Convolutional Neural Networks) and assessment of their performance for classification of tissue as healthy or sick. Our solution allows for rapid sample measurement and automatic classification of the results constituting a potential support tool for doctors.

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Section: Methodsmentioning

confidence: 99%

Predictions of cervical cancer identification by photonic method combined with machine learning

Kruczkowski

Drabik-Kruczkowska

Marciniak

et al. 2022

Sci Rep

Self Cite

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“…The light source operated at the central wavelength of 1550 ± 20 nm with a spectral width of 35 nm. The Fabry-Perot resonance cavity was formed by the polished fiber end-face and a silver mirror 21,22 .…”

Section: Dataset Acquisitionmentioning

confidence: 99%

Machine learning for predictions of cervical cancer identification – preliminary investigation based on refractive index

Kruczkowski

Drabik-Kruczkowska

Marciniak

et al. 2021

Preprint

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Cervical cancer is one of the most commonly appearing cancers, which early diagnosis is of greatest importance. Unfortunately, many diagnoses are based on subjective opinions of doctors – to date, there is no general measurement method with a calibrated standard. The problem can be solved with the measurement system being a fusion of an optoelectronic sensor and machine learning algorithm to provide reliable assistance for doctors in the early diagnosis stage of cervical cancer. We demonstrate the preliminary research on cervical cancer assessment utilizing optical sensor and prediction algorithm. Since each matter is characterized by refractive index, measuring its value and detecting changes give information about the state of the tissue. The optical measurements provided datasets for training and validating the analyzing software. We present data preprocessing, machine learning results utilizing three algorithms (Random Forest, eXtreme Gradient Boosting, Naïve Bayes) and assessment of their performance for classification of tissue as healthy or sick. All of them provided high values (>89%) of the measures describing them. Our solution allows for rapid sample measurement and automatic classification of the results constituting a potential support tool for doctors.

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“…In recent years, fiber sensors are widely used in environmental, biological and chemical sensing. Kosowska etal [17] prepared a fiber probe by depositing ZnO on the end face of a single-mode fiber and attaching nanocrystalline diamond, and verified its optical properties. In addition, people have combined fiber sensing technology with SERS technology to prepare fiber SERS probe, which can realize large SERS enhancement and good detection reproducibility, simultaneously [18] .…”

Section: Introductionmentioning

confidence: 99%

Microcavity Fiber SERS Probe Coated With Ag Nanoparticles For Detecting Antibiotic in Milk

Wang

et al. 2021

IEEE Photonics J.

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In this paper, an Ag nanoparticles (AgNPs) modified multimode fiber (MMF) SERS probe prepared based on sol self-assembly method is proposed and fabricated. Different fiber SERS probes (AgNPs-MMF-x, x is the self-assembly time) are obtained by adjusting the self-assembly time. Using rhodamine 6G (R6G) solution as the probe molecule, the far-end performances of the prepared fiber SERS probe are studied, and the AgNPs-MMF-30 probe with obvious advantages in SERS performance is obtained. The limit of detection (LOD) for R6G is as low as 10-9 M, demonstrating the high sensitivity of AgNPs-MMF-30 probe. The enhancement factor (EF) of AgNPs-MMF-30 probe to 10-7 M R6G is about 1.36×10 8. In addition, the AgNPs-MMF-30 probe has an outstanding reproducibility across the entire area with the maximum value of relative standard deviation (RSD) of less than 10.94%. The AgNPs-MMF-30 probe exhibit a long-term stability regarding Raman enhancement of up to 35 days. Importantly, the high-performance AgNPs-MMF-30 probe is further applied as a highly sensitive SERS platform for the trace detection of antibiotic in milk. The fiber SERS probe has the characteristics of fast, effective, remote real-time detection and so on, so it has great potential application in biochemical sensing and food security.

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Microscale diamond protection for a ZnO coated fiber optic sensor

Cited by 9 publications

References 31 publications

Predictions of cervical cancer identification by photonic method combined with machine learning

Predictions of cervical cancer identification by photonic method combined with machine learning

Machine learning for predictions of cervical cancer identification – preliminary investigation based on refractive index

Microcavity Fiber SERS Probe Coated With Ag Nanoparticles For Detecting Antibiotic in Milk

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