Revealing the improved sensitivity of PEDOT:PSS/PVA thin films through secondary doping and their strain sensors application.

The field of strain sensing involves the ability to measure an electrical response that corresponds to a strain. The integration of synthetic and conducting polymers can create a flexible strain sensor with a wide range of applications, including soft robotics, sport performance monitoring, gaming and virtual reality, and healthcare and biomedical engineering. However, the use of insulating synthetic polymers can impede the semiconducting properties of sensors, which may reduce sensor sensitivity. Previous research has shown that the doping process can significantly enhance the electrical performance and ionic conduction of conducting polymers, thereby strengthening their potential for use in electronic devices. However the full effects of secondary doping on the crystallinity, stretchability, conductivity, and sensitivity of conducting polymer blends have not been studied. In this study, we investigated the effects of secondary doping on the properties of poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate)/poly(vinyl alcohol) (PEDOT:PSS/PVA) polymer blend thin films and their potential use as strain sensors. The thin films were prepared using a facile drop-casting method. Morphology analysis using profilometry and atomic force microscopy confirmed the occurrence of phase segregation and revealed surface roughness values. This evidence provided a comprehensive understanding of the chemical interactions and physical properties of the thin films, and the effects of doping on these properties. The best films were selected and applied as sensitive strain sensors. EG-PEDOT:PSS/PVA thin films showing a significant increase of conductivity values from the addition of 1 vol% to 12 vol% addition, with conductivity values of 8.51 × 10-5 to 9.42 × 10-3 S cm-1. Our 12% EG-PEDOT:PSS/PVA sensors had the highest GF value of 2000 too. We compared our results with previous studies on polymeric sensors, and it was found that our sensors quantitatively had better GF values. Illustration that demonstrates the DMSO and EG dopant effects on PEDOT:PSS structure through bonding interaction, crystallinity, thermal stability, surface roughness, conductivity and stretchability was also provided. This study suggests a new aspect of doping interaction that can enhance the conductivity and sensitivity of PEDOT:PSS for device applications.

Novel formulation for co-delivery of cinnamon- and cumin-loaded polymeric nanoparticles to enhance their oral bioavailability.

Nanobiotechnology has been an encouraging approach to improving the efficacy of hydrophobic bioactive compounds. The biologically active constituents present in herbal extracts are poorly absorbed, resulting in loss of bioavailability and efficacy. Hence, herbal medicine and nanotechnology are combined to overcome these limitations. The surface-to-volume ratio of nanoparticles is high and as the size is small, the functional properties are enhanced. The present study reports the synthesis of cinnamon and cumin (Ci-Cu) dual drug-loaded poly (D, L-lactide-co-glycolide) (PLGA) nanoparticles (NPs) to overcome the limitations of oral bioavailability and extend the effect of these drugs for alleviating health problems. The solvent evaporation method was adopted for the synthesis, and the as-prepared nanoparticles were characterized by Scanning electron microscopy (SEM), Fourier transform infrared (FTIR) spectroscopy, Transmission electron microscopy (TEM) and X-ray diffraction (XRD). The average size of the formed spherical Ci-Cu nanoparticles ranged between 90 and 120 nm. The encapsulation efficiency of the drug was found to be 79% ± 4.5%. XRD analysis demonstrated that cinnamon and cumin were amorphously scattered in the PLGA matrix. The FTIR bands showed no evident changes suggesting the no direct molecular interactions between the drug and the polymer. At pH 6.9, the release studies in vitro exhibited a burst initially followed by a tendency to obtain a slower steady release. The results indicated that the Cu-Ci dual drug-loaded polymeric NPs has drug release at a slower rate. The time taken for 25% release of drug in Ci-Cu-loaded PLGA NPs was twice as compared to cumin-loaded PLGA Nps, and three times compared to cinnamon-loaded PLGA NPs.

A smartphone-interfaced, low-cost colorimetry biosensor for selective detection of bronchiectasis via an artificial neural network.

Exhaled breath (EB) contains several macromolecules that can be exploited as biomarkers to provide clinical information about various diseases. Hydrogen peroxide (H2O2) is a biomarker because it indicates bronchiectasis in humans. This paper presents a non-invasive, low-cost, and portable quantitative analysis for monitoring and quantifying H2O2 in EB. The sensing unit works on colorimetry by the synergetic effect of eosin blue, potassium permanganate, and starch-iodine (EPS) systems. Various sampling conditions like pH, response time, concentration, temperature and selectivity were examined. The UV-vis absorption study of the assay showed that the dye system could detect as low as ∼0.011 ppm levels of H2O2. A smart device-assisted detection unit that rapidly detects red, green and blue (RGB) values has been interfaced for practical and real-time application. The RGB value-based quantification of the H2O2 level was calibrated against NMR spectroscopy and exhibited a close correlation. Further, we adopted a machine learning approach to predict H2O2 concentration. For the evaluation, an artificial neural network (ANN) regression model returned 0.941 R 2 suggesting its great prospect for discrete level quantification of H2O2. The outcomes exemplified that the sensor could be used to detect bronchiectasis from exhaled breath.

A highly sensitive wearable pressure sensor capsule based on PVA/Mxene composite gel.

Wearable sensors have drawn considerable interest in the recent research world. However, simultaneously realizing high sensitivity and wide detection limits under changing surrounding environment conditions remains challenging. In the present study, we report a wearable piezoresistive pressure sensor capsule that can detect pulse rate and human motion. The capsule includes a flexible silicon cover and is filled with different PVA/MXene (PVA-Mx) composites by varying the weight percentage of MXene in the polymer matrix. Different characterizations such as XRD, FTIR and TEM results confirm that the PVA-Mx silicon capsule was successfully fabricated. The PVA-Mx gel-based sensor capsule remarkably endows a low detection limit of 2 kPa, exhibited high sensitivity of 0.45 kPa-1 in the ranges of 2-10 kPa, and displayed a response time of ~ 500 ms, as well as good mechanical stability and non-attenuating durability over 500 cycles. The piezoresistive sensor capsule sensor apprehended great stability towards changes in humidity and temperature. These findings substantiate that the PVA/MXene sensor capsule is potentially suitable for wearable electronics and smart clothing.

Breath Analysis for the In Vivo Detection of Diabetic Ketoacidosis.

Human breath analysis of volatile organic compounds has gained significant attention recently because of its rapid and noninvasive potential to detect various metabolic diseases. The detection of ketones in the breath and blood is key to diagnosing and managing diabetic ketoacidosis (DKA) in patients with type 1 diabetes. It may also be of increasing importance to detect euglycemic ketoacidosis in patients with type 1 or type 2 diabetes or heart failure, treated with sodium-glucose transporter-2 inhibitors (SGLT2-i). The present research evaluates the efficiency of colorimetry for detecting acetone and ethanol in exhaled human breath with the response time, pH effect, temperature effect, concentration effect, and selectivity of dyes. Using the proposed multidye system, we obtained a detection limit of 0.0217 ppm for acetone and 0.029 ppm for ethanol in the detection range of 0.05-50 ppm. A smartphone-assisted unit consisting of a portable colorimetric device was used to detect relative red/green/blue values within 60 s of the interface for practical and real-time application. The developed method could be used for rapid, low-cost detection of ketones in patients with type 1 diabetes and DKA and patients with type 1 or type 2 diabetes or heart failure treated with SGLT2-I and euglycemic ketoacidosis.

A Smart Colorimetric Platform for Detection of Methanol, Ethanol and Formic Acid.

Carbon dioxide (CO2) is a greenhouse gas in the atmosphere and scientists are working on converting it to useful products, thereby reducing its quantity in the atmosphere. For converting CO2, different approaches are used, and among them, electrochemistry is found to be the most common and more efficient technique. Current methods for detecting the products of electrochemical CO2 conversion are time-consuming and complex. To combat this, a simple, cost-effective colorimetric method has been developed to detect methanol, ethanol, and formic acid, which are formed electrochemically from CO2. In the present work, the highly efficient sensitive dyes were successfully established to detect these three compounds under optimized conditions. These dyes demonstrated excellent selectivity and showed no cross-reaction with other products generated in the CO2 conversion system. In the analysis using these three compounds, this strategy shows good specificity and limit of detection (LOD, ~0.03-0.06 ppm). A cost-effective and sensitive Internet of Things (IoT) colorimetric sensor prototype was developed to implement these dyes systems for practical and real-time application. Employing the dyes as sensing elements, the prototype exhibits unique red, green, and blue (RGB) values upon exposure to test solutions with a short response time of 2 s. Detection of these compounds via this new approach has been proven effective by comparing them with nuclear magnetic resonance (NMR). This novel approach can replace heavy-duty instruments such as high-pressure liquid chromatography (HPLC), gas chromatography (G.C.), and NMR due to its extraordinary selectivity and rapidity.

A review of smart sensors coupled with Internet of Things and Artificial Intelligence approach for heart failure monitoring.

Over the last decade, there has been a huge demand for health care technologies such as sensors-based prediction using digital health. With the continuous rise in the human population, these technologies showed to be potentially effective solutions to life-threatening diseases such as heart failure (HF). Besides being a potential for early death, HF has a significantly reduced quality of life (QoL). Heart failure has no cure. However, treatment can help you live a longer and more active life with fewer symptoms. Thus, it is essential to develop technological aid solutions allowing early diagnosis and consequently, effective treatment with possibly delayed mortality. Commonly, forecasts of HF are based on the generation of vast volumes of data usually collected from an individual patient by different components of the family history, physical examination, basic laboratory results, and other medical records. Though, these data are not effectively useful for predicting this failure, nevertheless, with the aid of advanced medical technology such as interconnected multi-sensory-based devices, and based on several medical history characteristics, the broad data provided machine learning algorithms to predict risk factors for heart disease of an individual is beneficial. There will be many challenges for the next decade of advancements in HF care: exploiting an increasingly growing repertoire of interconnected internal and external sensors for the benefit of patients and processing large, multimodal datasets with new Artificial Intelligence (AI) software. Various methods for predicting heart failure and, primarily the significance of invasive and non-invasive sensors along with different strategies for machine learning to predict heart failure are presented and summarized in the present study.

Colorimetry-Based Detection of Nitric Oxide from Exhaled Breath for Quantification of Oxidative Stress in Human Body.

Monitoring exhaled breath is a safe, noninvasive method for determining the health status of the human body. Most of the components in our exhaled breath can act as health biomarkers, and they help in providing information about various diseases. Nitric oxide (NO) is one such important biomarker in exhaled breath that indicates oxidative stress in our body. This work presents a simple and noninvasive quantitative analysis approach for detecting NO from exhaled breath. The sensing is based on the colorimetric assisted detection of NO by m-Cresol Purple, Bromophenol Blue, and Alizaringelb dye. The sensing performance of the dye was analyzed by ultraviolet-visible (UV-Vis) spectroscopy. The study covers various sampling conditions like the pH effect, temperature effect, concentration effect, and selective nature of the dye. The m-Cresol Purple dye exhibited a high sensitivity towards NO with a detection limit of ~0.082 ppm in the linear range of 0.002-0.5 ppm. Moreover, the dye apprehended a high degree of selectivity towards other biocompounds present in the breath, and no possible interfering cross-reaction from these species was observed. The dye offered a high sensitivity, selectivity, fast response, and stability, which benchmark its potential for NO sensing. Further, m-Cresol Purple dye is suitable for NO sensing from the exhaled breath and can assist in quantifying oxidative stress levels in the body for the possible detection of COVID-19.

Smart technologies driven approaches to tackle COVID-19 pandemic: a review.

The novel coronavirus infection (COVID-19) is not diminishing without vaccine, but it impinges on human safety and economy can be minimized by adopting smart technology to combat pandemic situation. The implementation of new innovations and novel tactics has proven to be effective in curbing the risk of COVID-19. The present study covers the role of smart technology in mitigating the spread of COVID-19 with specific focus on advancement in the field of drone, robotics, artificial intelligence (AI), mask, and sensor technology. The findings shed light on the robotics and drone technology-driven approaches that have been applied for assisting health system, surveillance, and disinfection process, etc. The AI technology strategies and framework is highlighted in terms of bulk data computing, predicting infection threats, providing medical assistance, and analyzing diagnosis results. Besides this, the technological shift in mask and sensor technology during the pandemic have been illustrated, which includes fabrication method like 3D printing and optical sensing, respectively. Furthermore, the strength, weakness, opportunities, and possible threats that have been shaped by the rigorous implementation of these technologies are also covered in detail.

Wide spectral photoresponse of template assisted out of plane grown ZnO/NiO composite nanowire photodetector.

Zinc oxide (ZnO) one-dimensional nanostructures are extensively used in ultra-violet (UV) detection. To improve the optical sensing capability of ZnO, various nickel oxide (NiO) based p-n junctions have been employed. ZnO/NiO heterojunction based sensing has been limited to UV detection and not been extended to the visible region. In the present work, p-NiO/n-ZnO composite nanowire (NW) heterojunction based UV-visible photodetector is fabricated. A porous anodic aluminum oxide template based electrochemical deposition method is adopted for well separated and vertically aligned growth of composite NWs. The photoresponse is studied in an out of plane contact configuration. The fabricated photodetector shows fast response under UV-visible light with a rise and decay time of tens of ms. The wide spectral photoresponse is analyzed in terms of conduction from defect states of ZnO and interfacial defects during p-n junction formation. Light interaction with heterojunction along the length of the composite NW results in enhanced visible photoresponse of the detector and is further supported by simulation.

Fast response UV detection based on waveguide characteristics of vertically grown ZnO nanorods partially embedded in anodic alumina template.

Zinc oxide (ZnO)-based ultraviolet (UV) detector has been fabricated and its photoresponse is studied in an out-of-plane contact configuration. Porous anodic aluminum oxide (AAO) template-based deposition method is adopted for the aligned and well-separated growth of ZnO nanorods (NRs). Through-hole in silicon (Si) by modified metal assisted chemical etching is used as a window for the electrochemical deposition of ZnO in the template and for out-of-plane electrical contacts during device analysis. The fabricated photodetector shows a fast response under UV (365 nm) light illumination, with rise and decay times of 31 ± 2 ms and 85 ± 3 ms, respectively. This fast response is analysed in terms of vertical growth and the waveguide nature of ZnO NRs embedded in anodic alumina. These results are further supported by a simulation comparing the electric field distribution of ZnO NR embedded in AAO with that of bare ZnO NR.

Comparative Study of Photoresponse from Vertically Grown ZnO Nanorod and Nanoflake Films.

Zinc oxide (ZnO) based ultraviolet (UV) photodetectors have been fabricated and their photoresponse is studied in Schottky diode configuration. A cost-effective single-step electrochemical deposition method is adopted for the growth of ZnO film with nanorod (NR) and nanoflake morphology. A comparative study of the photodetection parameters based on surface trap states, crystallinity, and strain is done for two different morphology films. Significant photocurrent enhancement is observed for the nanorods under UV light, with appreciable photoresponse in the blue region. A template-assisted growth of ZnO NR film is proposed for better photoresponse and sensitivity of the device, useful for various optoelectronic applications.