Toxic gas removal with kaolinite, metakaolinite, radiolarite, and diatomite.

In this study, some clays and dead microorganisms were compared in terms of their adsorption ability against special toxic gases. To this end, an experimental investigation was conducted to explore the adsorption kinetics of kaolinite, metakaolinite, radiolarite, and diatomite to ammonia (NH3), ethylene (C2H4), and carbon dioxide (CO2). Numerous analyses, such as x-ray fluorescence (XRF), x-ray diffraction (XRD), thermo-gravimetric analysis (TGA), scanning electron microscopy (SEM), and particle size distribution, have been performed for mineralogical and structural characterization of studied materials. Also, adsorption characteristics were investigated with the help of an ultra-precision scale and computer-controlled multi-gas control system. Since ammonia has the highest dipole moment among all studied gases, its removal efficiency was found as the highest in all materials. Regarding clay substances, metakaolinite indicated a lower response than kaolinite due to phase transformation. But, considering the microorganisms, diatomite toxic gas uptake is at least five times better than examined clays while the gas uptake behavior of radiolarite is analog to metakaolinite. Moreover, the adsorption behaviors of proposed materials are clarified with Langmuir isotherms, The results could facilitate improvements in applying microorganisms to the toxic gas environment as a natural adsorbent material.

Hollow nano-CaCO3's VOC sensing properties: A DFT calculation and experimental assessments.

Air is the most critical and necessary for life, and air quality significantly impacts people's health. Both indoor and outdoor pollution frequently contain volatile organic compounds (VOCs). Such contaminants provide immediate or long-term health risks to the living system. The present study investigates sorption characteristics of VOCs on hollow nano calcite (CaCO3) particles with 250 nm and 40 nm pore sizes to remove from the air ambient using the quartz crystal microbalance (QCM) technique at room temperature both experimentally and theoretically. The results were supported by density functional theory (DFT), and adsorption-desorption characteristics were studied with Langmuir adsorption isotherms. The QCM measurements showed a stable signal without having hysteresis, and the response of polar VOCs on hollow nano-CaCO3 particles such as ethanol, propanol, and humidity with higher polarity was less compared to solvents such as chloroform and dichloromethane, which revealed that the surfaces of CaCO3 particles have mostly non-polar properties. CaCO3 surface and VOC molecule interactions overlap with the Langmuir model. With DFT calculations, VOC and water molecule adsorption changes the CaCO3 Egap. Our findings show that the ΔEgap values increase as chloroform > dichloromethane > propanol > ethanol > water. This order suggests that the sensing response of the hollow CaCO3 structure is linearly proportional to the adsorption energies of VOC and water. The linear adsorption characteristics, high sensing response, and short recovery time illustrated that the newly synthesized nano-CaCO3 could be implemented as a new VOC adsorbent material for health, environmental sustainability, and in vitro microbiome cultures.

Gold Nanourchins Improve Virus Targeting and Plasmonic Coupling for Virus Diagnosis on a Smartphone Platform.

Point-of-care detection of pathogens is critical to monitor and combat viral infections. The plasmonic coupling assay (PCA) is a homogeneous assay and allows rapid, one-step, and colorimetric detection of intact viruses. However, PCA lacks sufficient sensitivity, necessitating further mechanistic studies to improve the detection performance of PCA. Here, we demonstrate that gold nanourchins (AuNUs) provide significantly improved colorimetric detection of viruses in PCA. Using respiratory syncytial virus (RSV) as a target, we demonstrate that the AuNU-based PCA achieves a detection limit of 1400 PFU/mL, or 17 genome equivalent copies/µL. Mechanistic studies suggest that the improved detection sensitivity arises from the higher virus-binding capability and stronger plasmonic coupling at long distances (∼10 nm) by AuNU probes. Furthermore, we demonstrate the virus detection with a portable smartphone-based spectrometer using RSV-spiked nasal swab clinical samples. Our study uncovers important mechanisms for the sensitive detection of intact viruses in PCA and provides a potential toolkit at the point of care.

Development and application of a low-cost smartphone-based turbidimeter using scattered light.

We demonstrate the development and application of a low-cost smartphone turbidimeter system to be used on water samples collected from natural resources. The proposed system depends on the spectroscopic measurements of both forward- and side-scattered light. A custom-designed cradle was fabricated using 3D printing, and plastic optical fibers were used to couple light from the smartphone's built-in flash and transmit the collected scattered light to the camera sensor. The performance parameters of the smartphone turbidimeter were investigated and compared to commercial systems, and the lowest limit of detection was found to be 5.58 NTU for forward-scattered detection. The results obtained in the proposed scattered-light-based spectroscopic turbidimeter and the practicality achieved by this extremely low-cost device will have a great impact on water science and technology.

Single-Image-Referenced Colorimetric Water Quality Detection Using a Smartphone.

In this paper, we present a smartphone platform for colorimetric water quality detection based on the use of built-in camera for capturing a single-use reference image. A custom-developed app processes this image for training and creates a reference model to be used later in real experimental conditions to calculate the concentration of the unknown solution. This platform has been tested on four different water quality colorimetric assays with various concentration levels, and results show that the presented platform provides approximately 100% accuracy for colorimetric assays with noticeable color difference. This portable, cost-effective, and user-friendly platform is promising for application in water quality monitoring.

Smartphone-based colorimetric detection via machine learning.

We report the application of machine learning to smartphone-based colorimetric detection of pH values. The strip images were used as the training set for Least Squares-Support Vector Machine (LS-SVM) classifier algorithms that were able to successfully classify the distinct pH values. The difference in the obtained image formats was found not to significantly affect the performance of the proposed machine learning approach. Moreover, the influence of the illumination conditions on the perceived color of pH strips was investigated and further experiments were conducted to study the effect of color change on the learning model. Non-integer pH levels are identified as their nearest integer pH values, whereas the test results for integer pH levels using JPEG, RAW and RAW-corrected image formats captured under different lighting conditions lead to perfect classification accuracy, sensitivity and specificity, which proves that colorimetric detection using machine learning based systems is able to adapt to various experimental conditions and is a great candidate for smartphone-based sensing in paper-based colorimetric assays.