Contaminated water is a serious concern in many developing countries with severe health consequences particularly for children. Current methods for monitoring waterborne pathogens are often time consuming, expensive, and labor intensive, making them not suitable for these regions. Electrochemical detection in a microfluidic platform offers many advantages such as portability, minimal use of instrumentation, and easy integration with electronics. In many parts of the world, however, the required equipment for pathogen detection through electrochemical sensors is either not available or insufficiently portable, and operators may not be trained to use these sensors and interpret results, ultimately preventing its wide adoption. Counterintuitively, these same regions often have an extensive mobile phone infrastructure, suggesting the possibility of integrating electrochemical detection of bacterial pathogens with a mobile platform. Toward a solution to water quality interventions, we demonstrate a microfluidic electrochemical sensor combined with a mobile interface that detects the sequences from bacterial pathogens, suitable for rapid, affordable, and point-of-care water monitoring. We employ the transduction of DNA hybridization into a readily detectable electric signal by means of a conformational change of DNA stem-loop structure. Using this platform, we successfully demonstrate the detection of as low as 100 nM E. coli sequences and the automatic interpretation and mapping of the detection results via a mobile application.

There are no existing affordable diagnostics for sensitive, rapid, and on-site detection of pathogens in milk. To this end, an on-site colorimetric-based sustainable assay has been developed and optimized using an L₁₆ (5⁴) Taguchi design to obtain results in hours without PCR amplification. To determine the level of Escherichia coli (E. coli) contamination, after induction with 150 µL of breast milk, the B-Per bacterial protein extraction kit was added to a solution containing an alginate-based microcapsule assay. Within this 3 mm spherical novel sensor design, X-Gal (5-Bromo-4-Chloro-3-Indolyl β-d-Galactopyranoside) was entrapped at a concentration of 2 mg/mL. The outward diffusing X-Gal was cleaved by β-galactosidase from E. coli and dimerized in the solution to yield a blue color after incubation at 40 °C. Color intensity was correlated with the level of E. coli contamination using a categorical scale. After an 8 h incubation period, a continuous imaging scale based on intensity normalization was used to determine a binary lower limit of detection (LOD), which corresponded to 10² colony forming unit per mL (CFU/mL) and above. The cost of the overall assay was estimated to be $0.81 per sample, well under the $3 benchmark for state-of-the-art immune-based test kits for pathogen detection in biofluids. Considering the reported binary LOD cutoff of 10² CFU/mL and above, this proposed hydrogel-based assay is suited to meet global requirements for screening breast milk or milk for pathogenic organisms of 10⁴ CFU/mL, with a percentage of false positives to be determined in future efforts.

Cross-linked hydrogel substrates have garnered attention as they simultaneously enable oxidoreductase reactions in a control volume extended to adsorption of redox capacitors for amplification of electrochemical signals. In this study, the effect of catalase immobilization in mold-casted alginate-based thin films (1 mm × 6 mm × 10 mm) containing multi walled carbon nanotubes (MWCNT) coated with chitosan has been studied via amperometry. The amperometric response was measured as a function of peroxide concentration, at a fixed potential of −0.4 V vs. SPCE in phosphate-buffered saline (pH = 7.4). Results indicate substrate detection is not diffusion-limited by the 100 μm thick chitosan layer, if the cationic polyelectrolyte is in contact with the sensing carbon electrode, and the linear detection of the enzyme absent in solution is enabled by immobilization (R² = 0.9615). The ferricyanide-mediated biosensor exhibited a sensitivity of 4.55 μA/mM for the optimal formulation at room temperature comparable to other nanomaterial hybrid sensing solution namely amine-functionalized graphene with an average response time of 5 s for the optimal formulation. The suitability of the optimized chitosan-coated alginate slabs nano-environment for co-encapsulation of catalase and carbon nanotubes was confirmed by cyclic voltammetry.

Nanoporous dialysis membranes made of regenerated cellulose are used as molecular weight cutoff standards in bioseparations. In this study, mesoporous standards with Stokes’ radii (50 kDa/2.7 nm, 100 kDa/3.4 nm and 1000 kDa/7.3 nm) and overlapping skewed distributions were characterized using AFM, with the specific aim of generating pore size classifiers for biomimetic membranes using supervised learning. Gamma transformation was used prior to conducting discriminant analysis in terms of the area under the receiver operating curve (AUC) and classification accuracy (Acc). Monte Carlo simulations were run to generate datasets (n = 10) on which logistic regression was conducted using a constant ratio of 80:20 (measurement:algorithm training), followed by algorithm validation by WEKA. The proposed algorithm can classify the 1000 kDa vs. 100 kDa (AUC > 0.8) correctly, but discrimination is weak for the 100 kDa vs. 50 kDa (AUC < 0.7), the latter being attributed to the instrument accuracy errors below 5 nm. As indicated by the results of the cross-validation study, a test size equivalent to 70% (AUC_tapping = 0.8341 ± 0.0519, Acc_tapping = 76.8% ± 5.9%) and 80% (AUC_fluid = 0.7614 ± 0.0314, Acc_tfluid = 76.2% ± 1.0%) of the training sets for the tapping and fluid modes are needed for correct classification, resulting in predicted reduction of scan times.