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Abstract Wearable devices can non-invasively monitor patients with chronic diseases. Sweat is an easily accessible biofluid for continuous sampling of analytes, including inflammatory markers and cytokines. We evaluated a sweat sensing wearable device in subjects with and without inflammatory bowel disease (IBD), a chronic inflammatory condition of the gastrointestinal tract. Participants with an IBD related hospital admission and a C-reactive protein level above 5 mg/L wore a sweat sensing wearable device for up to 5 days. Tumor necrosis factor-alpha (TNF-α) levels were continually assessed in the sweat via the sensor, and daily in the blood. A second cohort of healthy subjects without chronic diseases wore the device for up to 48 h. Twenty-eight subjects were enrolled. In the 16 subjects with IBD, a moderate linear relationship between serum and sweat TNF-α levels was observed (R2 = 0.72). Subjects with IBD were found to have a mean sweat TNF-α level of 2.11 pg/mL, compared to a mean value of 0.19 pg/mL in 12 healthy controls (p

Soil Health parameters serve as excellent surrogate measures towards assessing environmental quality and understanding effects of climate change mitigation via carbon sequestration. Soil Organic Matter (SOM) is a parameter that is synonymous to soil health and understanding SOM is a key metric to building and influencing good soil and agronomic practices by impacting soil aggregation and water withholding capacity. It is a vital regulator of soil nutrient cycling and uptake as well as a factor in the global carbon cycle and is hence more advantageous than just carbon monitoring. While it is understood that soil health cannot be analyzed directly, the use of an efficient indicator that can relay information about the soil physico-chemical and biological characteristics is highly desirable since it offers the ability to analyze soil information over time and build patterns in terms of geographical location.The proposed sensing system offers an in-situ electroanalytical approach to survey various electroactive substances present in the soil matrix. Utilizing this experimental framework- A mechanism of interaction between the RTIL (Room Temperature Ionic-Liquid) modified electrode and the OM functional moieties based on hydrogen bonding and pi-pi interactions captured using electrochemical impedance spectroscopy method is utilized to build a first-of-a-kind electrochemical SOM sensor.

There are limitations to monitoring modalities for chronic inflammatory conditions, including inflammatory bowel disease (IBD). Wearable devices are scalable mobile health technology that present an opportunity to monitor markers that have been linked to worsening, chronic inflammatory conditions and enable remote monitoring. In this research article, we evaluate and demonstrate a proof-of-concept wearable device to longitudinally monitor inflammatory and immune markers linked to IBD disease activity in sweat compared to expression in serum. Sixteen participants with an IBD-related hospital admission and a C-reactive protein (CRP) > 5 μg/mL were followed for up to 5 days. The sweat sensing device also known as IBD AWARE was worn to continuously measure CRP and interleukin-6 (IL-6) in the sweat of participants via electrochemical impedance spectroscopy. Serum samples were collected daily. A linear relationship between serum and sweat readings for CRP and IL-6 was demonstrated based on individual linear correlation coefficients. Pooled CRP and IL-6 serum-to-sweat ratios demonstrated improving correlation coefficients as serum cutoffs decreased. Between the first and last day of observation, significant and non-significant trends in serum CRP and IL-6 were observed in the sweat. Comparison of sweat measurements between the subjects with active IBD and 10 healthy subjects distinguished an inflamed and uninflamed state with an AUC of 0.85 (95% CI: 0.68–1.00) and a sensitivity and specificity of 82% and 70% at a CRP cutoff of 938.9 pg/mL. IBD AWARE wearable device holds promise in longitudinally monitoring individuals with IBD and other inflammatory diseases.

There is a pressing need to enhance early detection methods of E. coli O157:H7 to mitigate the occurrence and consequences of pathogenic contamination and associated outbreaks. This study highlights the efficacy of a portable electrochemical sensing platform that operates without faradaic processes towards detecting and quantifying E. coli O157:H7. It is specifically tailored for quick identification in potable water. The assay processing time is approximately 5 min, addressing the need for swift and efficient pathogen detection. The sensing platform was constructed utilizing specific, monoclonal E. coli antibodies, based on single-capture, non-faradaic, electrochemical immunoassay principles. The E. coli sensor assay underwent testing over a wide concentration range, spanning from 10 to 105 CFU/mL, and a limit of detection (LoD) of 1 CFU/mL was demonstrated. Significantly, the sensor’s performance remained consistent across studies, with both inter- and intra-study coefficients of variation consistently below 20%. To evaluate real-world feasibility, a comparative examination was performed between laboratory-based benchtop data and data obtained from the portable device. The proposed sensing platform exhibited remarkable sensitivity and selectivity, enabling the detection of minimal E. coli concentrations in potable water. This successful advancement positions it as a promising solution for prompt on-site detection, characterized by its portability and user-friendly operation. This study presents electrochemical-based sensors as significant contributors to ensuring food safety and public health. They play a crucial role in preventing the occurrence of epidemics and enhancing the supervision of water quality.

Soil is a vital component of the ecosystem that drives the holistic homeostasis of the environment. Directly, soil quality and health by means of sufficient levels of soil nutrients are required for sustainable agricultural practices for ideal crop yield. Among these groups of nutrients, soil carbon is a factor which has a dominating effect on greenhouse carbon phenomena and thereby the climate change rate and its influence on the planet. It influences the fertility of soil and other conditions like enriched nutrient cycling and water retention that forms the basis for modern ‘regenerative agriculture’. Implementation of soil sensors would be fundamentally beneficial to characterize the soil parameters in a local as well as global environmental impact standpoint, and electrochemistry as a transduction mode is very apt due to its feasibility and ease of applicability. Organic Matter present in soil (SOM) changes the electroanalytical behavior of moieties present that are carbon-derived. Hence, an electrochemical-based ‘bottom-up’ approach is evaluated in this study to track soil organic carbon (SOC). As part of this setup, soil as a solid-phase electrolyte as in a standard electrochemical cell and electrode probes functionalized with correlated ionic species on top of the metalized electrodes are utilized. The surficial interface is biased using a square pulsed charge, thereby studying the effect of the polar current as a function of the SOC profile. The sensor formulation composite used is such that materials have higher capacity to interact with organic carbon pools in soil. The proposed sensor platform is then compared against the standard combustion method for SOC analysis and its merit is evaluated as a potential in situ, on-demand electrochemical soil analysis platform.

Abstract With the evolution of the COVID‐19 pandemic, there is now a need for point‐of‐care devices for the quantification of disease biomarkers toward disease severity assessment. Disease progression has been determined as a multifactor phenomenon and can be treated based on the host immune response within each individual. CoST is an electrochemical immunosensor point‐of‐care device that can determine disease severity through multiplex measurement and quantification of spike protein, nucleocapsid protein, D‐dimer, and IL‐2R from 100 μL of plasma samples within a few minutes. The limit of detection was found to be 3 ng/mL and 21 ng/mL for S and N proteins whereas for D‐dimer and IL‐2R it was 0.0006 ng/mL and 0.242 ng/mL, respectively. Cross‐reactivity of all the biomarkers was studied and it was found to be

A sedentary lifestyle is one of the leading risk factors for developing obesity, increasing the risk of cardiovascular disease. In this work, we developed a novel approach to tracking glucose and cortisol to observe the dynamic relationship between the target biomarkers due to physical activity, nutrition, and the circadian cycle, via a machine learning-assisted wearable sweat-based electrochemical sensor. The machine learning approach helps to provide real-time biomarker value and a basis for actionable insight. Through this observational study, we illustrate the real-world performance of our sensing platform by examining the glucose and cortisol levels in passive sweat (

Metabolic syndrome conditions, diabetes, prediabetes, and non-alcoholic fatty liver disease (NAFLD) can be observed by monitoring the metabolic biomarkers glucose and galactose. These biomarkers modulate dynamically within human physiology, mainly based on nutritional intake. Traditionally, these biomarkers are measured in blood, which does not lend itself to dynamic monitoring. TAGG is a sweat-based electrochemical platform developed for continuous tracking. This work is the first demonstration of a point-of-care, non-invasive design to detect the dynamic interplay between two biomarkers, glucose and galactose, in human sweat. The TAGG platform detection range for both glucose and galactose is 0.05–32 mg/dL and 0.05 mg/dL limit of detection. Sweat samples were collected from four human subjects. The glucose and galactose data strongly correlated (r = 0.9864 and r = 0.9641) with ELISA standard reference method. By monitoring the dynamics of these metabolic biomarkers, we can gain greater insight into the complex interactions between nutrition and metabolic syndrome. This work demonstrates proof of concept of the non-invasive TAGG detection platform for tracking glucose and galactose dynamics in the low, high, and normal physiological ranges in human sweat.

Inflammatory bowel diseases (IBD) comprising Crohn's disease (CD) and ulcerative colitis (UC) are typically characterized by chronic episodes of inflammation within and through the gastrointestinal (GI) tract. Calprotectin (CP) is a biomarker employed by clinicians effectively guide diagnostic and therapeutic decisions. However, there exists several challenges in sample collection and fecal CP testing for patient-use and patient enablement. Hence, there is a current unmet need to access CP in a completely non-invasive manner. This work is the first demonstration of CP in sweat using a wearable sweat sensor device. The sweat sensor device demonstrated a wide dynamic range of 0.1–10 μg/mL with a limit of detection of 0.1 μg/mL. The sensor was also specific to CP and demonstrated negligible to no response for non-specific inflammatory markers such as C-reactive protein, interleukin-6. Excellent correlation was achieved between ELISA and the sweat sensor device with Pearson's r > 0.95. Human subject studies demonstrated that elevated basal levels in IBD subject (500 ng/mL) as compared to healthy cohort (∼350 ng/mL). Further, 3 times increase in CP levels was observed due to flare-up in the IBD subject with a mean concentration ∼1300 ng/mL as compared to the basal levels. The pre-clinical results of elevated CP levels will be of significant interest to clinicians as it provides a non-invasive and non-intrusive tracking flare-ups in IBD patients and can aid in better management of the disease.

Early disease detection and diagnosis through breath-based chemical assessment is broadly studied as a non-invasive tool and used as a cutting-edge opportunity in health care. Breath analytics or Breathomics is based on the recognition of levels of metabolites such as Volatile Organic Compounds (VOCs) and inorganic gases in an exhaled human breath. Lung cancer, one such disease state, alters the concentrations of hydrocarbons released in breath due to oxidative stress and lipid pre-oxidation. Heptane can be utilized as a VOC biomarker for the non-invasive diagnosis of lung cancer. This work outlines the fabrication of a graphitic carbon nitride-based electrochemical sensor platform that possesses increased catalytic activity and can be used for the screening of heptane vapors in the range of 0.45–5 ppm limit of detection of 0.45 ppm with 95% confidence interval. The synthesized material is characterized and validated using various standard analytical methods. Chronoamperometry is employed as an electrochemical technique to examine the diffusion dynamics of the target analyte. We demonstrated the specific sensing responses of the system in the presence of interferants by executing a cross-reactivity study with respect to other commonly found interferants in breath. We effectively established the use of a graphitic carbon nitride-based electrochemical sensor for point-of-care screening (qualitative analysis with p

Here we present a sensitive method for the detection and quantification of two (IL -10 and CRP) immuno-responsive biomarkers in various biofluids. The significance of these immune response biomarkers lies in them displaying elevated levels in critically ill COVID -19 patients. The developed electrochemical sensor contains a gold film electrode with ZnO nanoparticles deposited on its surface to increase the surface area of the working electrode while integrating antibody-antigen interactions into the detection system. This multiplex biosensor has a wide linear range from 0.01 μg/mL to 100 μg/mL and 0.1 pg/mL to 1000 pg/mL for CRP and IL10, respectively. The cross-reactivity of this multiplex sensor platform was evaluated between these two proteins and was

We present a first-of-its-kind electrochemical sensor that demonstrates direct real-time continuous soil pH measurement without any soil pre-treatment. The sensor functionality, performance, and in-soil dynamics have been reported. The sensor coating is a composite matrix of alizarin and Nafion applied by drop casting onto the working electrode. Electrochemical impedance spectroscopy (EIS) and squarewave voltammetry (SWV) studies were conducted to demonstrate the functionality of each method in accurately detecting soil pH. The studies were conducted on three different soil textures (clay, sandy loam, and loamy clay) to cover the range of the soil texture triangle. Squarewave voltammetry showed pH-dependent responses regardless of soil texture (while electrochemical impedance spectroscopy’s pH detection range was limited and dependent on soil texture). The linear models showed a sensitivity range from −50 mV/pH up to −66 mV/pH with R2 > 0.97 for the various soil textures in the pH range 3–9. The validation of the sensor showed less than a 10% error rate between the measured pH and reference pH for multiple different soil textures including ones that were not used in the calibration of the sensor. A 7-day in situ soil study showed the capability of the sensor to measure soil pH in a temporally dynamic manner with an error rate of less than 10%. The test was conducted using acidic and alkaline soils with pH values of 5.05 and 8.36, respectively.

In our daily life, as consumers we are constantly made aware of the impact of pesticides and other modifications to food products derived from genetically modified organisms (GMO’s) that have an impact on human health. In our connected world, there is an immense interest for on-demand information about food quality prior to consumption. The gold standard method to detect pesticides or GMOs residues in food is complex and is not amenable to rapid consumer use. In this study, we demonstrate the feasibility of an electrochemical portable sensing approach for the simultaneous direct detection of spiked pesticides chlorpyrifos (Chlp) and GMOs protein Cry1Ab in real edamame soy matrix. The immunoassay based two-plex sensing platform was fabricated using respective antibody’s Chlp on one side and Cry1Ab on other side. A simple lab-on-kitchen level preparation of matrix has been demonstrated and sensor response was tested using non-faradaic electrochemical impedance spectroscopy (EIS), which showed a linear response in Cry1Ab/Chlp concentrations from 0.3 ng/mL to 243 ng/mL with limit of detection 0.3 ng /mL for both the target antigens (Cry1Ab and Chlp) respectively. The spiked and recovery test results fall within ± 20% error in real sample matrix which demonstrates the performance of the our platform with maximum residue limit (MRL) for the given targets. Such electrochemical portable multi-analyte direct sensing tool with simple matrix processing protocol can be a future commercial field-testing tool for use at everyday consumer level.

Abstract Diabetes is a chronic endocrine disease that occurs due to an imbalance in glucose levels and altering carbohydrate metabolism. It is a leading cause of morbidity, resulting in a reduced quality of life even in developed societies, primarily affected by a sedentary lifestyle and often leading to mortality. Keeping track of blood glucose levels noninvasively has been made possible due to diverse breakthroughs in wearable sensor technology coupled with holistic digital healthcare. Efficient glucose management has been revolutionized by the development of continuous glucose monitoring sensors and wearable, non/minimally invasive devices that measure glucose concentration by exploiting different physical principles, e.g., glucose oxidase, fluorescence, or skin dielectric properties, and provide real-time measurements every 1–5 min. This paper presents a highly novel and completely non-invasive sweat sensor platform technology that can measure and report glucose concentrations from passively expressed human eccrine sweat using electrochemical impedance spectroscopy and affinity capture probe functionalized sensor surfaces. The sensor samples 1–5 µL of sweat from the wearer every 1–5 min and reports sweat glucose from a machine learning algorithm that samples the analytical reference values from the electrochemical sweat sensor. These values are then converted to continuous time-varying signals using the interpolation methodology. Supervised machine learning, the decision tree regression algorithm, shows the goodness of fit R2 of 0.94 was achieved with an RMSE value of 0.1 mg/dL. The output of the model was tested on three human subject datasets. The results were able to capture the glucose progression trend correctly. Sweet sensor platform technology demonstrates a dynamic response over the physiological sweat glucose range of 1–4 mg/dL measured from 3 human subjects. The technology described in the manuscript shows promise for real-time biomarkers such as glucose reporting from passively expressed human eccrine sweat.

Abstract Sepsis is a life-threatening condition and understanding the disease pathophysiology through the use of host immune response biomarkers is critical for patient stratification. Lack of accurate sepsis endotyping impedes clinicians from making timely decisions alongside insufficiencies in appropriate sepsis management. This work aims to demonstrate the potential feasibility of a data-driven validation model for supporting clinical decisions to predict sepsis host-immune response. Herein, we used a machine learning approach to determine the predictive potential of identifying sepsis host immune response for patient stratification by combining multiple biomarker measurements from a single plasma sample. Results were obtained using the following cytokines and chemokines IL-6, IL-8, IL-10, IP-10 and TRAIL where the test dataset was 70%. Supervised machine learning algorithm naïve Bayes and decision tree algorithm showed good accuracy of 96.64% and 94.64%. These promising findings indicate the proposed AI approach could be a valuable testing resource for promoting clinical decision making.

Abstract Sepsis is a silent killer, caused by a syndromic reaction of the body's immune system to an infection that is typically the ultimate pathway to mortality due to numerous infectious diseases, including COVID‐19 across the world. In the United States alone, sepsis claims 220,000 lives, with a dangerously high fatality rate between 25% and 50%. Early detection and treatment can avert 80% of sepsis mortality which is currently unavailable in most healthcare institutions. The novelty in this work is the ability to simultaneously detect eight (IL‐6, IL‐8, IL‐10, IP‐10, TRAIL, d‐dimer, CRP, and G‐CSF) heterogeneous immune response biomarkers directly in whole blood without the need for dilution or sample processing. The DETecT sepsis (Direct Electrochemical Technique Targeting Sepsis) 2.0 sensor device leverages electrochemical impedance spectroscopy as a technique to detect subtle binding interactions at the metal/semi‐conductor sensor interface and reports results within 5 min using only two drops (~100 μl) of blood. The device positively (r >0.87) correlated with lab reference standard LUMINEX for clinical translation using 40 patient samples. The developed device showed diagnostic accuracy greater than 80% (AUC >0.8) establishing excellent specific and sensitive response. Portable handheld user‐friendly feature coupled with precise quantification of immune biomarkers makes the device amenable in a versatile setting providing insights on patient's immune response. This work highlights an innovative solution of enhancing sepsis care and management in the absence of a decision support device in the continuum of sepsis care.

Sustainable agriculture is the answer to the rapid rise in food demand which is straining our soil, leading to desertification, food insecurity, and ecosystem imbalance. Sustainable agriculture revolves around having real-time soil health information to allow farmers to make the correct decisions. We present an ion-selective electrode (ISE) electrochemical soil nitrate sensor that utilizes electrochemical impedance spectroscopy (EIS) for direct real-time continuous soil nitrate measurement without any soil pretreatment. The sensor functionality, performance, and in-soil dynamics have been reported. The ion-selective electrode (ISE) is applied by drop casting onto the working electrode. The study was conducted on three different soil textures (clay, sandy loam, and loamy clay) to cover the range of the soil texture triangle. The non-linear regression models showed a nitrate-dependent response with R2 > 0.97 for the various soil textures in the nitrate range of 5–512 ppm. The validation of the sensor showed an error rate of less than 20% between the measured nitrate and reference nitrate for multiple different soil textures, including ones that were not used in the calibration of the sensor. A 7-day-long in situ soil study showed the capability of the sensor to measure soil nitrate in a temporally dynamic manner with an error rate of less than 20%.

Breathomics is being explored as a non-invasive pathway for disease diagnosis. In this paper we describe the design and fabrication of an electrochemical transduction-based sensing scheme that can be used for the non-invasive diagnosis of respiratory inflammation by monitoring hydrogen peroxide (H2O2) vapors in breath. AuNP-nafion@[BMIM]BF4 modified electrode forms the backbone of the electrochemical sensor platform. This is the first report of an electrochemical sensor platform for assessing airway inflammation by monitoring H2O2 levels in the lower parts-per billion (50 ppb) range. We engineered the RTIL interaction with the target analyte through computational and chemical modeling. The results obtained via computational and electrochemical modeling helped in strengthening our premise and serve as a proof-of-concept for the advancement of a breathomics-aided electrochemical detection approach.

This work demonstrates the quantification and detection of interferon- γ (IFN-γ), a key inflammatory biomarker in sweat using a flexible, nanoporous sweat sensor. IFN-γ is a pro-inflammatory cytokine released during the innate and adaptive response to enable antibacterial/antiviral immunity to an infection. Further, IFN-γ is critical marker to combat cancer through anti-tumor and pro-apoptotic mechanism. Therefore, real-time, continuous monitoring of pro-inflammatory cytokines can aid clinicians in active feedback on the administered therapeutic regimen. However, conventional blood-based laboratory methods are not feasible for continuous monitoring while sweat-based monitoring allows to continuously track the biomarker level in a point-of-need setting. This work demonstrates a sweat sensor device for rapid and accurate detection of IFN-γ in human sweat with less than 10% of variation. Highly sensitive and selective detection of IFN-γ with a limit of detection of 1 pg/mL and dynamic range of 1–512 pg/mL using impedance spectroscopy. The sensor demonstrated minimal to no signal response to non-specific cytokines. The performance of sweat sensor was further validated by comparing to state-of-art reference method and a Pearson's correlation of r > 0.95 was achieved in an observational study comprising of human subject samples confirming the reliability of the sweat sensor to report sweat IFN-γ levels. The promising findings from this work demonstrate the viability of utilizing the detection of IFN-γ in sweat for several inflammatory diseases such as cancer. Detection of IFN-γ in sweat will be pivotal in monitoring and aiding physicians in administering precision treatment strategies for patients diagnosed with cancer through continuous monitoring of immune status.

Disease progression of sepsis has been perceived as a multifaceted phenomenon, considering the temporal host inflammatory response within individuals which requires early diagnosis. Herein, we present a multicohort analysis through temporal inflammatory biomarker profiling using Direct Electrochemical Technique Targeting (DETecT) sepsis device that measures and quantify cytokines (IL-6, IL-8, IL-10), chemokines (TRAIL, IP-10), and well-established inflammatory biomarkers (PCT, CRP) with a sample turnaround time of 0.97) with the Luminex reference standard during clinical evaluation for a total of 124 sepsis patient samples. Low mean bias for all the biomarkers in Bland- Altman analysis indicated good agreement between the standard LUMINEX method and the developed DETecT sepsis device. We used the combinatorial power of rapidly measuring a panel of seven biomarkers, paired with a machine learning model, to effectively predict the patient outcomes when given two-time points in the early stages of sepsis. The device could predict patient mortality and recovery with over 92% accuracy by applying decision tree analysis. We envision this work would facilitate personalized treatment based on biomarker stratification to represent exactly where the patient belongs within the sepsis continuum. Measurable empirical data with a fast turnaround time would facilitate the DETecT sepsis device as a potential enabling technology that can play a crucial role in understanding sepsis prognosis and be leveraged for personalized therapeutics anywhere.

Since December 2019, the SARS-CoV-2 outbreak that began in Wuhan, China has spread to nearly every continent and become a global health concern. Although much has been discovered about COVID-19 and its pathogenesis, the WHO has identified an immediate need to increase the levels of testing for COVID-19 and identify the stages of the disease accurately for appropriate action to be taken by clinicians and emergency care units. Harnessing technology for accurate diagnosis and staging will improve patient outcomes and minimize serious consequences of false-positive test results. Point-of-care technologies aim to intervene at every stage of the disease to quickly identify infected patients and asymptomatic carriers and stratify them for timely treatment. This requires the tests to be rapid, accurate, sensitive, simple to use and compatible with many body fluids. Mobile platforms are optimal for remote, small-scale deployment, whereas facility-based platforms at hospital centers and laboratory settings offer higher throughput. Here we review evidence-based point-of-care technologies in the context of the entire continuum of COVID-19, from early screening to treatment, and discuss their impact on improving patient outcomes.

Climate change directly affects all aspects of the environment and accounts for changes in our surroundings seen or unseen. With the growing need for accountability in the agricultural commercial and regulatory spheres, there is a widely accepted consensus that there needs to be quantitative information on the different environmental entities to understand and solve challenges posed towards food production and agricultural activities. The main purpose of the creation of this article is to propagate an era of quantitative metrics to better assess environmental health which can adversely impact human health. This review assesses 3 different environmental vectors prone to pollution and in high contact with human entities. This review also discusses different electrochemical sensing platforms used as biosensors for the detection of a wide myriad of pollutants with an adverse effect on human health. Furthermore, the breakdown of each section includes a survey of the evolution and design of various sensor platforms that are aimed towards a comprehensive monitoring platform for pollutant screening with an emphasis on agro-safety and environmental health. Detailed pesticide screening case-studies are also explored for a better understanding of the current electrochemical biosensors that exist for the sole purpose of environmental monitoring.

Using pesticides is a common agricultural and horticultural practice to serve as a control against weeds, fungi, and insects in plant systems. The application of these chemical agents is usually by spraying them on the crop or plant. However, this methodology is not highly directional, and so only a fraction of the pesticide actually adsorbs onto the plant, and the rest seeps through into the soil base contaminating its composition and eventually leaching into groundwater sources. Electrochemical sensors which are more practical for in situ analysis used for pesticide detection in soil runoff systems are still in dearth, while the ones published in the literature are attributed with complex sensor modification/functionalization and preprocessing of samples. Hence, in this work, we present a highly intuitive electroanalytical sensor approach toward rapid (10 min), on-demand screening of commonly used pesticides—glyphosate and atrazine—in soil runoff. The proposed sensor functions based on the affinity biosensing mechanism driven via thiol cross-linker and antibody receptors that holistically behaves as a recognition immunoassay stack that is specific and sensitive to track test pesticide analytes. Then, this developed sensor is integrated further to create a pesticide-sensing ecosystem using a front-end field-deployable smart device. The method put forward in this work is compared and validated against a standard laboratory potentiostat instrument to determine efficacy, feasibility, and robustness for a point-of-use (PoU) setting yielding LoD levels of 0.001 ng/ml for atrazine and 1 ng/ml for glyphosate. Also, the ML model integration resulted in an accurate prediction rate of ≈80% in real soil samples. Therefore, a universal pesticide screening analytical device is designed, fabricated, and tested for pesticide assessment in real soil runoff samples.

This work demonstrates the use of a noninvasive, sweat-based dual biomarker electrochemical sensor for continuous, prognostic monitoring of a Traumatic Brain Injury (TBI) with the aim of enhancing patient outcomes and reducing the time to treatment after injury. A multiplexed SWEATSENSER was used for noninvasive continuous monitoring of glial fibrillary acidic protein (GFAP) and Interleukin-6 (IL-6) in a human sweat analog and in human sweat. Electrochemical impedance spectroscopy (EIS) and chronoamperometry (CA) were used to measure the sensor response. The assay chemistry was characterized using Fourier Transform Infrared Spectroscopy (FTIR). The SWEATSENSER was able to detect GFAP and IL-6 in sweat over a dynamic range of 3 log orders for GFAP and 2 log orders for IL-6. The limit of detection (LOD) for GFAP detection in the sweat analog was estimated to be 14 pg/mL using EIS and the LOD for IL-6 was estimated to be 10 pg/mL using EIS. An interference study was performed where the specific signal was significantly higher than the non-specific signal. Finally, the SWEATSENSER was able to distinguish between GFAP and IL-6 in simulated conditions of a TBI in human sweat. This work demonstrates the first proof-of-feasibility of a multiplexed TBI marker combined with cytokine and inflammatory marker detection in passively expressed sweat in a wearable form-factor that can be utilized toward better management of TBIs. This is the first step toward demonstrating a noninvasive enabling technology that can enable baseline tracking of an inflammatory response.

Abstract This work presents the viability of passive eccrine sweat as a functional biofluid toward tracking the human body's inflammatory response. Cytokines are biomarkers that orchestrate the manifestation and progression of an infection/inflammatory event. Hence, noninvasive, real‐time monitoring of cytokines can be pivotal in assessing the progression of infection/inflammatory event, which may be feasible through monitoring of host immune markers in eccrine sweat. This work is the first experimental proof demonstrating the ability to detect inflammation/infection such as fever, FLU directly from passively expressed sweat in human subjects using a wearable “SWEATSENSER” device. The developed SWEATSENSER device demonstrates stable, real‐time monitoring of inflammatory cytokines in passive sweat. An accuracy of >90% and specificity >95% was achieved using SWEATSENSER for a panel of cytokines (interleukin‐6, interleukin‐8, interleukin‐10, and tumor necrosis factor‐α) over an analytical range of 0.2–200 pg mL−1. The SWEATSENSER demonstrated a correlation of Pearson's r > 0.98 for the study biomarkers in a cohort of 26 subjects when correlated with standard reference method. Comparable IL‐8 levels (2–15 pg mL−1) between systemic circulation (serum) and eccrine sweat through clinical studies in a cohort of 15 subjects, and the ability to distinguish healthy and sick (infection) cohort using inflammatory cytokines in sweat provides pioneering evidence of the SWEATSENSER technology for noninvasive tracking of host immune response biomarkers. Such a wearable device can offer significant strides in improving prognosis and provide personalized therapeutic treatment for several inflammatory/infectious diseases.

Abstract Wearable Awareness Through Continuous Hidrosis (WATCH) sensor is a sweat based monitoring platform that tracks cortisol and glucose for the purpose of understanding metabolic responses related to macronutrient consumption. In this research article, we have demonstrated the ability of tracking these two biomarkers in passive human sweat over a workday period (8 h) for 10 human subjects in conjunction with their macronutrient consumption. The validation of the WATCH sensor performance was carried out via standard reference methods such as Luminex and ELISA This is a first demonstration of a passive sweat sensing technology that can detect interrelated dual metabolites, cortisol, and glucose, on a single sensing platform. The significance of detecting the two biomarkers simultaneously is that capturing the body's metabolic and endocrinal responses to dietary triggers can lead to improved lifestyle management. For sweat cortisol, we achieved a detection limit of 1 ng/ml (range ∼1–12.5 ng/ml) with Pearson's “r” of 0.897 in reference studies and 0.868 in WATCH studies. Similarly, for sweat glucose, we achieved a detection limit of 1 mg/dl (range ∼ 1–11 mg/dl) with Pearson's “r” of 0.968 in reference studies and 0.947 in WATCH studies, respectively. The statistical robustness of the WATCH sensor was established through the Bland–Altman analysis, whereby the sweat cortisol and sweat glucose levels are comparable to the standard reference method. The probability distribution (t‐test), power analysis (power 0.82–0.87), α = 0.05. Mean absolute relative difference (MARD) outcome of ˷5.10–5.15% further confirmed the statistical robustness of the sweat sensing WATCH device output.

Abstract Successful commercialization of wearable diagnostic sensors necessitates stability in detection of analytes over prolonged and continuous exposure to sweat. Challenges are primarily in ensuring target disease specific small analytes (i.e. metabolites, proteins, etc.) stability in complex sweat buffer with varying pH levels and composition over time. We present a facile approach to address these challenges using RTILs with antibody functionalized sensors on nanoporous, flexible polymer membranes. Temporal studies were performed using both infrared spectroscopic, dynamic light scattering, and impedimetric spectroscopy to demonstrate stability in detection of analytes, Interleukin-6 (IL-6) and Cortisol, from human sweat in RTILs. Temporal stability in sensor performance was performed as follows: (a) detection of target analytes after 0, 24, 48, 96, and 168 hours post-antibody sensor functionalization; and (b) continuous detection of target analytes post-antibody sensor functionalization. Limit of detection of IL-6 in human sweat was 0.2 pg/mL for 0–24 hours and 2 pg/mL for 24–48 hours post-antibody sensor functionalization. Continuous detection of IL-6 over 0.2–200 pg/mL in human sweat was demonstrated for a period of 10 hours post-antibody sensor functionalization. Furthermore, combinatorial detection of IL-6 and Cortisol in human sweat was established with minimal cross-talk for 0–48 hours post-antibody sensor functionalization.

C-reactive protein (CRP) is considered to be an important biomarker associated with many diseases. During any physiological inflammation, the level of CRP reaches its peak at 48 h, whereas its half-life is around 19 h. Hence, the detection of low-level CRP is an important task for the prognostic management of diseases like cancer, stress, metabolic disorders, cardiovascular diseases, and so on. There are various techniques available in the market to detect low-level CRP like ELISA, Western blot, etc. An electrochemical biosensor is one of the important miniaturized platforms which provides sensitivity along with ease of operation. The most important element of an electrochemical biosensor platform is the electrode which, upon functionalization with a probe, captures the selective antibody−antigen interaction and produces a digital signal in the form of potential/current. Optimization of the electrode design can increase the sensitivity of the sensor by 5−10-fold. Herein, we come up with a new sensor design called the spiral electrochemical notification coupled electrode (SENCE) where the working electrode (WE) is concentric in nature, which shows better response than the market-available standard screen-printed electrode. The sensor is thoroughly characterized using a standard Ferro/Ferri couple. The sensing performance of the fabricated platform is also characterized by the detection of standard H2O2 using a diffusion-driven technique, and a low detection limit of 15 µM was achieved. Furthermore, we utilized the platform to detect a low level (100 ng/mL) of CRP in synthetic sweat. The manuscript provides emphasis on the design of a sensor that can offer good sensitivity in electrochemical biosensing applications.

Over the past few years, room-temperature ionic liquid (RTIL) has evolved as an important solvent-cum-electrolyte because of its high thermal stability and excellent electrochemical activity. Due to these unique properties, RTILs have been used as a solvent/electrolyte/mediator in many applications. There are many RTILs, which possess good conductivity as well as an optimal electrochemical window, thus enabling their application as a transducer for electrochemical sensors. Nitroaromatics are a class of organic compounds with significant industrial applications; however, due to their excess use, detection is a major concern. The electrochemical performance of a glassy carbon electrode modified with three different RTILs, [EMIM][BF4], [BMIM][BF4] and [EMIM][TF2N], has been evaluated for the sensing of two different nitroaromatic analytes: 2,6-dinitrotoluene (2,6 DNT) and ethylnitrobenzene (ENB). Three RTILs have been chosen such that they have either a common anion or cation amongst them. The sensory response has been measured using square wave voltammetry (SQWV). We found the transducing ability of [EMIM][BF4] to be superior compared to the other two RTILs. A low limit of detection (LOD) of 1 ppm has been achieved with a 95% confidence interval for both the analytes. The efficacy of varying the cationic and anionic species of RTIL to obtain a perfect combination has been thoroughly investigated in this work, which shows a novel selection process of RTILs for specific applications. Moreover, the results obtained from testing with a glassy carbon electrode (GCE) have been replicated using a miniaturized sensor platform that can be deployed easily for on-site sensing applications.

Simultaneous detection of correlated multi-biomarkers on a single low-cost platform in ultra-low fluid volumes with robustness is in growing demand for the development of wearable diagnostics. A non-faradaic biosensor for the simultaneous detection of alcohol, glucose, and lactate utilizing low volumes (1–5 μL) of sweat is demonstrated. Biosensing is implemented using nanotextured ZnO films integrated on a flexible porous membrane to achieve enhanced sensor performance. The ZnO sensing region is functionalized with enzymes specific for the detection of alcohol, glucose, and lactate in the ranges encompassing their physiologically relevant levels. A non-faradaic chronoamperometry technique is used to measure the current changes associated with interactions of the target biomarkers with their specific enzyme. The specificity performance of the biosensing platform was established in the presence of cortisol as the non-specific molecule. Biosensing performance of the platform in a continuous mode performed over a 1.5-h duration showed a stable current response to cumulative lifestyle biomarker concentrations with capability to distinguish reliably between low, mid, and high concentration ranges of alcohol (0.1, 25, 100 mg/dL), glucose (0.1, 10, 50 mg/dL), and lactate (1, 50, 100 mM). The low detection limits and a broader dynamic range for the lifestyle biomarker detection are quantified in this research demonstrating its suitability for translation into a wearable device.

Molybdenum electrode based flexible biosensor on porous polyamide substrates has been fabricated and tested for its functionality as a protein affinity based biosensor. The biosensor performance was evaluated using a key cardiac biomarker; cardiac Troponin-I (cTnI). Molybdenum is a transition metal and demonstrates electrochemical behavior upon interaction with an electrolyte. We have leveraged this property of molybdenum for designing an affinity based biosensor using electrochemical impedance spectroscopy. We have evaluated the feasibility of detection of cTnI in phosphate-buffered saline (PBS) and human serum (HS) by measuring impedance changes over a frequency window from 100 mHz to 1 MHz. Increasing changes to the measured impedance was correlated to the increased dose of cTnI molecules binding to the cTnI antibody functionalized molybdenum surface. We achieved cTnI detection limit of 10 pg/mL in PBS and 1 ng/mL in HS medium. The use of flexible substrates for designing the biosensor demonstrates promise for integration with a large-scale batch manufacturing process.