Trisna Julian

We present an adaptive microfluidic channel that enables flexible control of impedance flow cytometry sensitivity. Reducing the channel height improves signal strength and reduces measurement variability.

Imaging and impedance flow cytometry is a label-free technique that has shown promise as a potential replacement for standard flow cytometry. This is due to its ability to provide rich information and archive high-throughput analysis. Recently, significant efforts have been made to leverage machine learning for processing the abundant data generated by those techniques, enabling rapid and accurate analysis. Harnessing the power of machine learning, imaging and impedance flow cytometry has demonstrated its capability to address various complex phenotyping scenarios. Herein, we present a comprehensive overview of the detailed strategies for implementing machine learning in imaging and impedance flow cytometry. We initiate the discussion by outlining the commonly employed setup to acquire the data (i.e., image or signal) from the cell. Subsequently, we delve into the necessary processes for extracting features from the acquired image or signal data. Finally, we discuss how these features can be utilized for cell phenotyping through the application of machine learning algorithms. Furthermore, we discuss the existing challenges and provide insights for future perspectives of intelligent imaging and impedance flow cytometry.

The reverse transcription-quantitative polymerase chain reaction (RT-qPCR) approach has been widely used to detect the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, instead of using it alone, clinicians often prefer to diagnose the coronavirus disease 2019 (COVID-19) by utilizing a combination of clinical signs and symptoms, laboratory test, imaging measurement (e.g., chest computed tomography scan), and multivariable clinical prediction models, including the electronic nose. Here, we report on the development and use of a low cost, noninvasive method to rapidly sniff out COVID-19 based on a portable electronic nose (GeNose C19) integrating an array of metal oxide semiconductor gas sensors, optimized feature extraction, and machine learning models. This approach was evaluated in profiling tests involving a total of 615 breath samples composed of 333 positive and 282 negative samples. The samples were obtained from 43 positive and 40 negative COVID-19 patients, respectively, and confirmed with RT-qPCR at two hospitals located in the Special Region of Yogyakarta, Indonesia. Four different machine learning algorithms (i.e., linear discriminant analysis, support vector machine, stacked multilayer perceptron, and deep neural network) were utilized to identify the top-performing pattern recognition methods and to obtain a high system detection accuracy (88–95%), sensitivity (86–94%), and specificity (88–95%) levels from the testing datasets. Our results suggest that GeNose C19 can be considered a highly potential breathalyzer for fast COVID-19 screening.

An electronic nose (E-nose), comprising eight metal oxide semiconductor (MOS) gas sensors, was used in situ for real-time classification of black tea according to its quality level. Principal component analysis (PCA) coupled with signal preprocessing techniques (i.e., time set value preprocessing, F1; area under curve preprocessing, F2; and maximum value preprocessing, F3), allowed grouping the samples from seven brands according to the quality level. The E-nose performance was further checked using multivariate supervised statistical methods, namely, the linear and quadratic discriminant analysis, support vector machine together with linear or radial kernels (SVM-linear and SVM-radial, respectively). For this purpose, the experimental dataset was split into two subsets, one used for model training and internal validation using a repeated K-fold cross-validation procedure (containing the samples collected during the first three days of tea production); and the other, for external validation purpose (i.e., test dataset, containing the samples collected during the 4th and 5th production days). The results pointed out that the E-nose-SVM-linear model together with the F3 signal preprocessing method was the most accurate, allowing 100% of correct predictive classifications (external-validation data subset) of the samples according to their quality levels. So, the E-nose-chemometric approach could be foreseen has a practical and feasible classification tool for assessing the black tea quality level, even when applied in-situ, at the harsh industrial environment, requiring a minimum and simple sample preparation. The proposed approach is a cost-effective and fast, green procedure that could be implemented in the near future by the tea industry.

In many cases, acetic acid is categorized as hazardous to health. A high-performance sensor for detecting acetic acid is urgently required. This study aims to observe the characteristics of quartz crystal microbalance (QCM) coated with polyacrylamide as an acetic acid sensor. For this purpose, we prepared the sensor by firstly coating with 1-dodecanethiol (using self-assembled-monolayers or SAM technique) followed subsequently by glutaraldehyde and polyacrylamide (PAM).As results, the sensors showed less sensitivity to humidity changes, which is one of the basic prerequisites for a stable sensor to an environment. We also obtained that the sensor showed relatively fast response time and high sensitivity to acetic acid vapor of about 96 seconds and 12.9 Hz/(mgL^-1), respectively. Further research, however, is still required to improve the sensitivity and specificity by choosing more selective polymers.

Short-chain alcohols are a group of volatile organic compounds (VOCs) that are often found in workplaces and laboratories, as well as medical, pharmaceutical, and food industries. Real-time monitoring of alcohol vapors is essential because exposure to alcohol vapors with concentrations of 0.15–0.30 mg·L⁻¹ may be harmful to human health. This study aims to improve the detection capabilities of quartz crystal microbalance (QCM)-based sensors for the analysis of alcohol vapors. The active layer of chitosan was immobilized onto the QCM substrate through a self-assembled monolayer of L-cysteine using glutaraldehyde as a cross-linking agent. Before alcohol analysis, the QCM sensing chip was exposed to humidity because water vapor significantly interferes with QCM gas sensing. The prepared QCM sensor chip was tested for the detection of four different alcohols: n-propanol, ethanol, isoamyl alcohol, and n-amyl alcohol. For comparison, a non-alcohol of acetone was also tested. The prepared QCM sensing chip is selective to alcohols because of hydrogen bond formation between the hydroxyl groups of chitosan and the analyte. The highest response was achieved when the QCM sensing chip was exposed to n-amyl alcohol vapor, with a sensitivity of about 4.4 Hz·mg⁻¹·L. Generally, the sensitivity of the QCM sensing chip is dependent on the molecular weight of alcohol. Moreover, the developed QCM sensing chips are stable after 10 days of repeated measurements, with a rapid response time of only 26 s. The QCM sensing chip provides an alternative method to established analytical methods such as gas chromatography for the detection of short-chain alcohol vapors.

Safrole is the main precursor for producing the amphetamine-type stimulant (ATS) drug, N-methyl-3,4-methylenedioxyamphetamine (MDMA), also known as ecstasy. We devise a polyacrylonitrile (PAN) nanofiber-based quartz crystal microbalance (QCM) for detecting safrole. The PAN nanofibers were fabricated by direct electrospinning to modify the QCM chips. The PAN nanofiber on the QCM chips has a diameter of 240 ± 10 nm. The sensing of safrole by QCM modified with PAN nanofiber shows good reversibility and an apparent sensitivity of 4.6 Hz·L/mg. The proposed method is simple, inexpensive, and convenient for detecting safrole, and can be an alternative to conventional instrumental analytical methods for general volatile compounds.

The photodegradation activity of nanocomposites for 20 ppm methylene blue solution has been investigated in this work. Nanocomposites Mn_0.5Zn_0.5Fe₂O₄/SiO₂ have been synthesized using coprecipitation method. The X-ray diffraction (XRD) pattern confirmed the formation of three phases in sample Mn_0.5Zn_0.5Fe₂O₄/SiO₂ i.e., Mn_0.5Zn_0.5Fe₂O₄, Zn(OH)₂, and SiO₂. The appearance of SiO₂ phase showed that the encapsulation process has been carried out. The calculated particles size of Mn_0.5Zn_0.5Fe₂O₄/SiO₂ is greater than Mn_0.5Zn_0.5Fe₂O₄. Bonding analysis via vibrational spectra for Mn_0.5Zn_0.5Fe₂O₄/SiO₂ confirmed the formation of bonds Me-O-Si stretching (2854.65 cm^-1) and Si-O-Si asymmetric stretching (1026.13 cm^-1). The optical gap energy of Mn_0.5Zn_0.5Fe₂O₄/SiO₂ was smaller (2.70 eV) than Mn_0.5Zn_0.5Fe₂O₄ (3.04 eV) due to smaller lattice dislocation and microstrain that affect their electronic structure. The Mn_0.5Zn_0.5Fe₂O₄/SiO₂ showed high photodegradation ability due to smaller optical gap energy and the appearance of SiO₂ ligand that can easily attract dye molecules. The Mn_0.5Zn_0.5Fe₂O₄/SiO₂ also showed high degradation activity even without UV light radiation. The result showed that photodegradation reaction doesn't follow pseudo-first order kinetics.

Here, we describe an N,N-dimethyl formamide (DMF) vapour sensor fabricated by coating polyacrylonitrile (PAN) nanofiber structured on quartz crystal microbalance (QCM). The PAN nanofiber sensors with an average diameter of 225 nm to 310 nm were fabricated via electrospinning process with different mass deposition on QCM substrate. The nanostructured of PAN nanofiber offers a high specific surface area that improved the sensing performance of nanofiber sensors. Benefiting from that fine structure, and high polymer-solvent affinity between PAN and DMF, the development of DMF sensors presented good response at ambient temperature. Since there is no chemical reaction between PAN nanofiber and DMF vapour, weak physical interaction such absorption and swelling were responsible for the sensing behavior. The results are indicating that the response of PAN nanofiber sensors has more dependency on the nanofiber structure (specific surface area) rather than its mass deposition. The sensor also showed good stability after a few days sensing. These findings have significant implications for developing DMF vapour sensor based on QCM coated polymer nanofibers.