A gas flow meter with linear sensitivity based on freely-suspended nanofilms of smectic liquid crystal

Green, Adam A. S.; Dutch, Evan; Qi, Zhiyuan; Briggs, C.K.; Park, Cheol S.; Glaser, Matthew A.; Maclennan, Joseph E.; Clark, Noel A.

doi:10.1063/1.5083967

Cited by 6 publications

(3 citation statements)

References 35 publications

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“…According to Bernoulli's principle, the flow velocity of the base liquid likewise increases with variations in the pressure inside the chamber. 42,43 Figure 3e indicates that the flow velocity of the liquid changes more rapidly with the rotation of the larger MB. Figure 3f shows the observed velocity and pressure attenuation with distance between the particle and chamber edge during the magnetization rotation of the MB.…”

Section: Concentration Detection Of Mbs By the Mmd-qcmmentioning

confidence: 98%

Magnetically Modulated Differential Quartz Crystal Microbalances for Rapid, Ultrasensitive, and Direct Probing of Prostate-Specific Antigens Conjugated with Magnetic Beads

Chen,

Wen,

et al. 2023

ACS Sens.

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The occurrence and development of diseases are closely related to overexpression of specific biomarkers in the serum of patients. Rapid and sensitive biomarker detection is beneficial for early diagnosis and treatment. However, the current laboratory processes and assays for biomarker detection are expensive and time-consuming, and their operation also requires a large number of professionals. We developed a magnetically modulated differential quartz crystal microbalance (MMD-QCM) method combined with magnetic bead (MB) labels for rapid and highly sensitive quantitative detection of prostate-specific antigen (PSA). Because MBs exhibit magnetized rotation motion under an applied AC magnetic field, a pair of QCMs are utilized to measure the difference between the magnetic motion intensities of the MBs and the MB−PSA immune complex to determine the PSA concentration. Experimental results demonstrate that the proposed method can be adopted to determine the PSA concentration in a wide range of 0.01− 1000 ng/mL as well as exhibit a low detection limit of 0.065 ng/mL. In addition, the proposed scheme enables fast detection and low sample consumption. The single detection process takes less than 4 h and requires only 113 μL of sample solution. The proposed detection strategy is superior to the existing detection method and can be effectively used in early screening and prognostic diagnosis of cancer and other related diseases owing to its simplicity, low cost, and high speed.

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Section: Concentration Detection Of Mbs By the Mmd-qcmmentioning

confidence: 98%

Magnetically Modulated Differential Quartz Crystal Microbalances for Rapid, Ultrasensitive, and Direct Probing of Prostate-Specific Antigens Conjugated with Magnetic Beads

Chen,

Wen,

et al. 2023

ACS Sens.

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“…Applications in security labels and anti-counterfeiting technologies as well as lens fabrication have also been reported [7]. Liquid crystal films have been used as sensors for lipids and other biomolecules [9] as well as flow sensors [10]. LC coatings on aluminum plates have also been used for flow visualization and for measuring shear stresses over planar surfaces in flow, e.g., flows around cylinders [11].…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Structural Color of Liquid Crystals via Machine Learning

Nguyen,

Childs,

Salter

et al. 2023

Liquids

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Materials that generate structural color may be promising alternatives to dyes and pigments due to their relative long-term stability and environmentally benign properties. Liquid crystal (LC) mixtures of cholesteryl esters demonstrate structural color due to light reflected from the helical structure of the self-assembled molecules. The apparent color depends on the pitch length of the liquid crystal. While a wide range of colors have been achieved with such LC formulations, the nature of the pitch–concentration relationship has been difficult to define. In this work, various machine learning approaches to predict the reflected wavelength, i.e., the position of the selective reflection band, based on LC composition are compared to a Scheffe cubic model. The neural network regression model had a higher root mean squared error (RMSE) than the Scheffe cubic model with improved predictions for formulations not included in the dataset. Decision tree regression provided the best overall performance with the lowest RMSE and predicted position of the selective reflection band within 0.8% of the measured values for LC formulations not included in the dataset. The predicted values using the decision tree were over two-fold more accurate than the Scheffe cubic model. These results demonstrate the utility of machine learning models for predicting physical properties of LC formulations.

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“…In this work, we designed a substrate-free technique developed for the detection of the SARS-CoV-2 virus in nasopharyngeal swab samples. We used textile grids instead of copper grids ( Green et al, 2019 ) without using any glass or polymer material as substrate for anchoring the LCs. This also means that the presence of air both above and below the LCs causes their homotropic alignment.…”

Section: Introductionmentioning

confidence: 99%

Rapid, label-free and low-cost diagnostic kit for COVID-19 based on liquid crystals and machine learning

Esmailpour¹,

Mohammadimasoudi²,

Shemirani³

et al. 2022

Biosensors and Bioelectronics: X

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A gas flow meter with linear sensitivity based on freely-suspended nanofilms of smectic liquid crystal

Cited by 6 publications

References 35 publications

Magnetically Modulated Differential Quartz Crystal Microbalances for Rapid, Ultrasensitive, and Direct Probing of Prostate-Specific Antigens Conjugated with Magnetic Beads

Magnetically Modulated Differential Quartz Crystal Microbalances for Rapid, Ultrasensitive, and Direct Probing of Prostate-Specific Antigens Conjugated with Magnetic Beads

Prediction of the Structural Color of Liquid Crystals via Machine Learning

Rapid, label-free and low-cost diagnostic kit for COVID-19 based on liquid crystals and machine learning

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