Isoquercitrin is a bioactive flavonoid with significant pharmaceutical and nutraceutical importance, creating a growing demand for rapid, sensitive, and label-free detection techniques. This work proposes a terahertz plasmonic metasurface employing a graphene–black phosphorus hybrid resonant architecture for high-sensitivity refractive-index sensing toward isoquercitrin detection. The unit cell comprises a graphene-coated circular resonator coupled with a black phosphorus-functionalized plus-shaped cross resonator and four symmetrically arranged L-shaped auxiliary resonators on a SiO₂ substrate. The sensor performance was optimized using full-wave COMSOL Multiphysics simulations by varying the L-shaped resonator dimensions, plus-shaped resonator geometry, incidence angle, and graphene chemical potential. The optimized design achieves a sensitivity of 917 GHz/RIU, a figure of merit of 11.179 RIU−1, and a quality factor of 8.122 over a refractive index range of 1.333–1.385. Electromagnetic field analysis confirms strong field confinement at the graphene–black phosphorus interface, enabling enhanced light–matter interaction. A machine learning surrogate model accurately predicts sensor responses, achieving R2 > 0.9997, RMSE <0.00235, MAPE <0.11%, and normalized RMSE <0.002. These results demonstrate the proposed biosensor as a reliable and computationally efficient platform for high-performance terahertz biosensing and future biomedical diagnostic applications.
Advancing minimally invasive diabetes monitoring requires the development of sensitive and reliable glucose sensors that can detect glucose in biological fluids such as urine, without the need for blood sampling. Therefore, there is a growing need for glucose sensing devices that provide accurate and reliable detection while avoiding invasive blood collection. In this study, a 2D Interfacially coupled CoS@MoS₂ heterostructure was synthesized using a simple hydrothermal process and evaluated for glucose detection in human urine was investigated. The successful formation of a well-integrated CoS@MoS₂ heterointerface with enhanced electrocatalytic characteristics was confirmed through comprehensive structural, morphological, and compositional analyses using XRD, SEM, EDX, Raman spectroscopy, XPS, TEM and SAED. The CoS@MoS₂ modified GCE was found to have a rapid current response with approximately 3 s to reach a steady-state current reaction for the glucose oxidation in alkaline electrolyte. The sensor was able to show a linear response range of 0.01–0.20 mM, a very low detection limit of 1.10 nM and a high sensitivity of 915.50 μA mM−1 cm−2 under the optimized conditions. High spike-recovery results confirmed the excellent analytical accuracy, and the electrode showed good selectivity toward most urinary interferents. Moreover, glucose was successfully quantified in urine samples obtained from both diabetic and non-diabetic individuals. The CoS@MoS2 sensor demonstrated promising performance for glucose detection in urine; however, further validation with large and more clinically diverse cohorts is necessary to establish its reliability and potential for practical applications.
Bacterial diseases and their different forms of severity affect millions of people worldwide at any given time. Conventional bacterial detection methods, though widely used, are limited by long processing times, reliance on skilled personnel, sophisticated equipment, and poor suitability for point-of-care use. Thus, there is a constant need for advanced bacterial detection methods and techniques that will produce rapid results so that early diagnosis of diseases can be done. It will also help in ensuring the safety of food and environmental samples, which are the most common sources of bacterial diseases. This is where magnetic nanoparticles (MNPs) prove to be valuable and effective. Their properties of superparamagnetism, high surface-to-volume ratio, biocompatibility and the convenience of functionalization make MNPs a reliable and advanced tool for rapid bacterial detection. This review provides an overview of the current bacterial detection mechanisms using MNPs and how different strategies for their compositional or surface modifications can improve sensitivity and optimize the rate of limit-of-detection (LOD). Real-time application of MNPs for bacterial detection in samples such as food, environmental sources, urine, blood, and other body fluids or cells has also been studied in this review. It also discusses the recent developments incorporating MNPs in other detection techniques, such as CRISPR, and explores how artificial intelligence (AI) can be used to build smart detection systems.
Background Contrast media extravasation (CMEX) during CT power injection may cause tissue injury, yet early detection remains difficult with visual inspection. To address single-modality sensing limitations, we developed a contactless multimodal monitoring system integrating uncooled thermography (256 × 192 pixels), dual-band near-infrared (NIR) imaging, edge computing, and AI analysis for CMEX screening. Methods The system was deployed on an NVIDIA Jetson Nano platform, integrating an uncooled thermal sensor and dual CMOS sensors at 850 nm and 940 nm. Homography-based registration using the butterfly needle as reference enabled multimodal fusion. A modified U-Net generated vascular masks from 850 nm images, and Efficient Net performed binary extravasation classification. Validation was conducted exclusively using a temperature-controlled phantom under varying monitoring distances, vessel temperatures, and leakage volumes without human subjects. Results After Tensor RT FP16/INT8 optimization, the system achieved real-time throughput of 15–20 fps with 150–250 ms latency and 2–4 mm mean registration error. At 100 cm, the thermal system maintained a Thermal Diffusion Ratio of 1.42 ± 0.10. Leakage Diffusion Ratio increased significantly with leakage volume, while the 940 nm channel showed 8–15% signal attenuation. Vascular segmentation achieved a Dice Similarity Coefficient of 0.882 ± 0.031 and IoU of 0.789 ± 0.042. Efficient Net achieved 0.9979 recall, 0.7997 precision, 0.6000 specificity, and 0.8451 accuracy. Ablation analysis confirmed multimodal fusion outperformed unimodal sensing (AUC = 0.93 vs. 0.79 for thermal-only, p < 0.01). Conclusions This engineering prototype demonstrates proof-of-concept real-time CMEX monitoring with ultra-high screening sensitivity under controlled phantom conditions, establishing a preclinical foundation for trials.
Lead-halide perovskites have recently emerged as promising interfacial materials for enhancing surface plasmon resonance (SPR) biosensors, but Pb-containing compounds remain undesirable for environmentally sustainable sensing platforms. In this work, we numerically investigate a lead-free perovskite/HfSe2 SPR biosensor based on a CaF2/Ag/perovskite/HfSe2/sensing-medium configuration. Four perovskites are evaluated: MAPBr3 as a Pb-based benchmark and the lead-free candidates Cs2AgBiBr6, Cs2AgBiCl6, and CsSnBr3. The optical response is calculated at 633 nm using a recursive Fresnel transfer-matrix method. Two refractive-index windows are optimized independently: a generic aqueous interval (1.3300–1.3350) and a bio/SARS-like interval (1.3348–1.3398). A multi-metric optimization framework balancing angular sensitivity, figure of merit (FoM), resonance depth, and linewidth is employed to identify practically optimized sensor designs. For the generic sensing interval, the optimal balanced lead-free design uses Cs2AgBiCl6 with Ag/Cs2AgBiCl6/HfSe2 thicknesses of 47/3.0/1.0 nm, achieving a sensitivity of 397.0 deg RIU−1 and a FoM of 103.66 RIU−1. For the bio/SARS-like interval, the optimal balanced design switches to CsSnBr3 (43/2.5/1.0 nm), providing 447.8 deg RIU−1 sensitivity and a FoM of 90.05 RIU−1, while MAPBr3 remains the highest-sensitivity material overall. Monte Carlo robustness analysis, local machine-learning surrogate models, electric-field visualization, and layer-isolation analysis provide additional physical insight into sensor performance. The results show that no single lead-free perovskite is universally optimal; instead, the preferred material depends on the refractive-index regime and optimization objective, providing practical design guidelines for environmentally friendly SPR biosensors.
Multi-parameter sensing has become a popular research topic. However, designing multi-parameter sensors with multiple high quality-factor (Q-factor) resonances remains challenging. In order to address the challenge, we employ quasi-bound states in the continuum (quasi-BICs) to design a silicon-based metasurface multi-parameter sensor with multiple high-Q resonances. The multi-parameter sensor can support three quasi-BICs resonances with a Q-factor exceeding 104, and can detect three parameters of magnetic induction intensity (B), refractive index (RI) and temperature (T) simultaneously. In order to detect the three parameters using the proposed sensor, the sensitivities for B, RI and T of the three quasi-BICs resonances are calculated, respectively. Then we exploit a three-dimensional sensitivity matrix, and obtain a matrix equation to describe the relationship between the changes of the three parameters and the resonant wavelength shifts of the three quasi-BICs resonances. By solving the matrix equation, the three parameters can be simultaneously demodulated. The proposed sensor employs quasi-BICs to obtain resonances with high Q-factors, and can simultaneously detect B, RI and T. This work provides a feasible way for designing high-Q multi-parameter sensors.
Accurate skin-layer thickness quantification is needed for depth calibration in multiple μ-spatially offset Raman spectroscopy (mμSORS)-based noninvasive glucose sensing. Existing OCT-based analyses often estimate skin thickness from depth-intensity profiles generated by large-scale A-line averaging. However, this averaging can attenuate the OCT-visible lower stratum corneum (SC) boundary and make SC thickness estimation unstable in some volumes. Here, we present a dual-path SC thickness quantification framework. The framework uses the conventional profile-based pathway when the SC-related profile feature is preserved, and uses a segmentation-based recovery pathway when profile cues are degraded. In the recovery pathway, SC thickness is computed from spatially explicit SC boundaries delineated on individual B-scans, thereby reducing information loss caused by global averaging. On an independent thickness-evaluation cohort of 26 participants with 156 manually annotated B-scans, the framework showed strong agreement with manual reference measurements, with a participant-level MAE of 5.17 μm (0.6466 pixels) and RMSE of 6.58 μm (0.8225 pixels). These results suggest that OCT-derived SC thickness can provide useful geometric information for mμSORS depth calibration, especially when conventional profile-based estimation is unstable.
Gold-decorated carbon-based composites have emerged as a promising class of hybrid nanomaterials for next-generation electrochemical immunosensors by combining the excellent biocompatibility and plasmonic properties of gold nanoparticles with the superior electrical conductivity, large surface area, and structural versatility of carbon nanomaterials. Despite significant progress, a critical understanding of how composite architecture, synthesis strategy, and interfacial engineering govern immunosensing performance remains limited. This review comprehensively discussed gold-decorated carbon-based composites, encompassing graphene-, carbon nanotube-, quantum dot-, graphitic carbon nitride-, and chitosan-based platforms with particular emphasis on the synthesis approaches, structure–property relationships, signal amplification mechanisms, and analytical performance in the sensitive detection of clinically and environmentally relevant biomarkers, including cancer, viral, inflammatory, and food-safety targets. Finally, this review critically examines the current challenges, emerging opportunities, and future research directions for gold-decorated carbon-based composites, while highlighting strategic approaches to enhance their immunosensing performance and facilitate their translation into practical diagnostic applications.
This study reports a citrate-capped gold nanoparticle (AuNP)-based colorimetric sensor for the sensitive and selective detection and quantification of Pb(II) in real industrial wastewater effluent collected over ten consecutive operational days. Pb(II)-induced aggregation of AuNPs, confirmed by UV–Vis spectroscopy and transmission electron microscopy, produced a colour transition from wine-red to blue, accompanied by a decrease in the localised surface plasmon resonance (LSPR) peak at 520 nm and emergence of a new red-shifted peak at 680 nm. Quantification was performed in parallel using UV–Visible (UV–Vis) spectrophotometry and ImageJ digital image analysis, yielding calibration curves of y = 0.0496× + 0.0238 (UV–Vis) and y = 0.79428× + 165.89147 (ImageJ), each with R2 > 0.999. The UV–Vis method achieved a limit of detection (LOD) of 0.053 mg/L and a limit of quantification (LOQ) of 0.178 mg/L, across a linear range of 0.1–20 mg/L. One-way ANOVA confirmed no statistically significant difference between the two quantification methods (Fcalc < Fcrit; p = 0.739) on the calibration standard dataset; however, a validated calibration equation to convert ImageJ intensity to concentration could not be established for the real wastewater matrix, so ImageJ results for these samples are reported as measured intensity rather than concentration (Section 3.8). CIE L*a*b*/Yxy colour space parameters (colour difference, chroma, hue angle) provided perceptually uniform confirmation of the aggregation-induced colour transition, and quantitative regression models fitted to these parameters (linear for ΔE, R2 = 0.958; four-parameter logistic for hue angle and chroma, R2 > 0.98) offered an independent cross-validation channel for Pb(II) quantification. The naked-eye visual detection threshold was 4 mg/L. The sensor showed high selectivity for Pb(II) over six common co-existing cations [Zn(II), Cu(II), Mn(II), Cr(VI), Ni(II), Fe(II)], which produced corrected intensity responses at least four-fold lower than that of Pb(II). Pb(II) was quantitatively detected in seven of the ten wastewater effluent samples (3.52–14.57 mg/L, original wastewater concentration), substantially exceeding both the WHO drinking-water guideline (0.01 mg/L) and the South African industrial discharge limit (0.05–0.10 mg/L), while the remaining three samples showed anomalous (negative) absorbance consistent with assay failure and are reported as not detected. The proposed platform is portable, cost-effective and suitable for on-site heavy metal monitoring in resource-limited environments.
This study presents the design and electrochemical characterization of a highly sensitive and selective sensor for the determination of D−/L- kynurenine (D−/L- KYN), based on a carbon paste electrode (CPE) modified with multi-walled carbon nanotubes (MWCNTs). The electrocatalytic oxidation of D−/L-KYN at the MWCNT-modified CPE was systematically investigated using cyclic voltammetry (CV) and differential pulse voltammetry (DPV). The Structural and morphological characteristics of the MWCNTs were characterized by Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and X-ray diffraction (XRD). The combined characterization results confirmed the successful functionalization, morphological integrity, and crystalline structure of the MWCNTs, supporting their suitability as an efficient electrode modifier. At physiological pH (7.0) in phosphate buffer solution (PBS), D−/L-KYN exhibited a well-defined oxidation peak at +0.85 V (vs. Ag/AgCl). The electrochemical behavior of the analyte followed a diffusion-controlled mechanism involving a two-electron/two-proton transfer process. Key analytical parameters including solution pH and scan rate were optimized to enhance sensor performance. The fabricated sensor exhibited a linear concentration range of 0.06–40.0 μM, with an impressive limit of detection 3 nM (S/N = 3). Furthermore, the sensor demonstrated excellent reproducibility and repeatability, with relative standard deviations (RSDs) of 3.8% and 3.3%, respectively. The developed platform was successfully applied to the quantification of D−/L-KYN in spiked human plasma samples, demonstrating its potential for clinical and biomedical applications.
The increasing demand for rapid and reliable fuel quality assessment has created a need for compact sensing platforms capable of accurately detecting fuel adulteration. This work presents a compact 6 × 6 mm2 hexagonal metamaterial structure operating at 2.7578 GHz for the detection of water adulteration in petrol through dielectric characterization. The fabricated design comprises dual hexagonal split-ring resonators, a central hexagonal patch, and a connecting metallic strip printed on an FR-4 substrate. An equivalent circuit representation is developed to characterize the resonance mechanism and is validated through full-wave electromagnetic simulations. The sensing performance is investigated by varying the dielectric constant from 1 to 4, resulting in a resonance frequency shift of 1.805 GHz, a maximum sensitivity of 3.61 GHz/εr, and a quality factor of 137.89. The proposed metastructure is fabricated utilizing standard PCB technology and experimentally characterized using a free-space measurement setup. The measured resonance responses exhibit good correlation with the simulated responses, with a maximum frequency error of only 6.4 MHz and a relative error below 0.5%. Furthermore, a machine learning framework is developed to predict the dielectric constant directly from the measured resonance characteristics. Among the evaluated models, XGBoost demonstrates the highest prediction accuracy and the lowest prediction error, enabling rapid estimation of the dielectric properties of unknown fuel samples without repeated electromagnetic simulations. The proposed methodology combines compact sensor design, experimental validation, and intelligent data-driven analysis, providing an accurate and practical solution for real-time detection of water adulteration in petrol.
Label-free nucleic acid sensing with high sensitivity and compatibility with electronic compatibility remains a major challenge in biosensor development. This work presents a proof-of-concept microwave biosensing platform based on a complementary split-ring resonator (CSRR)-integrated metamaterial patch antenna functionalized with a single-walled carbon nanotube–single-stranded DNA (SWCNT–ssDNA) nanocomposite. The study investigates the electromagnetic sensing mechanism by correlating changes in the dielectric properties of the CNT–DNA nanocomposite with resonance-frequency and phase variations. A metamaterial patch antenna incorporating three CSRRs into the radiating patch and exhibiting X- and Ku-band resonances was fabricated, with the Ku-band resonance selected for DNA sensing. SWCNT–ssDNA nanocomposite films with different DNA concentrations were selectively deposited over the high-electric-field CSRR region to maximize electromagnetic interaction. DNA sensing was performed by simultaneously monitoring the resonance frequency and phase of the reflection coefficient, enabling dual-parameter interrogation. Compared with the CNT-coated reference device, the CNT–DNA-functionalized biosensor exhibited maximum frequency and phase shifts of approximately 1 GHz and 126°, respectively. Within the experimentally investigated DNA concentration range of 50–66.7 nM (1.98–2.64 ng/mL), frequency and phase variations of approximately 540 MHz and 33° were observed. A theoretical detection limit of 0.6 pM (24 pg/mL) was estimated from the measured sensitivity and standard deviation of repeated Vector Network Analyzer measurements using the 3σ criterion. TEM and SEM analyses confirmed DNA incorporation within the CNT nanocomposite. These results demonstrate the feasibility of CSRR-confined near-field microwave sensing and provide a foundation for future sequence-specific DNA recognition and hybridization assays.
Ticagrelor (TIC) is an antiplatelet drug prescribed for patients with stroke and heart attack. In this research, a selective and novel electrochemical sensor was designed based on glassy carbon electrode modified with ketjen black-multi walled carbon nanotubes nanocomposite and molecularly imprinted polymer (MIP/KB-MWCNTs/GCE) to detect TIC. MIP has been used for the first time to assay TIC drug. The proposed sensor is fabricated through electropolymerization of dopamine (DA) as a monomer and TIC as a template on a substrate of KB-MWCNTs nanocomposite. Electrochemical data were taken on the designed sensor via cyclic voltammetry (CV) and differential pulse voltammetry (DPV) techniques. The modified electrode obtained low limit of detection (LOD) 0.016 nM, linear range 0.05 to 2300 nM, acceptable selectivity, repeatability, reproducibility, and stability. The applicability of the sensor was investigated in the analysis of real samples such as serum and urine, with a recovery range of 98.4 to 107.0% and a relative standard deviation (RSD) of less than 2.3%. For validation, the results from the real sample were compared with the spectrophotometric reference method.
Three-dimensional tumor spheroids (3D-TS) provide a relevant in vitro model that more closely mimics human tumor biology than traditional cell cultures. However, current assessment techniques, including bright-field microscopy and endpoint assays, are limited by their inability to continuously monitor spheroid dynamics. Here, we present a noninvasive, label-free platform that combines electrical impedance spectroscopy with microelectrode arrays (MEAs) to enable real-time monitoring of 3D-TS.We evaluated several clustering approaches and developed an automated threshold-detection algorithm to distinguish electrodes covered by spheroids from uncovered electrodes based on impedance changes. This approach eliminates the need for manual intervention and optical confirmation, providing a robust solution for automated investigations.Using human colorectal cancer spheroids at different developmental stages, we demonstrated that impedance measurements obtained with two MEA types (rigid, glass-embedded ones and flexible, mesh - MEAs) capture spatial and temporal changes in spheroid behavior. Application of our automated algorithm enabled streamlined, high-throughput recording of 3D-TS under control condition and after application of the chemotherapeutic drug Navitoclax. Across both MEA platforms, adherent spheroids maintained stable impedance profiles under control conditions over several days in culture, whereas treatment with Navitoclax induced a significant gradual decline. Notably, the mesh-MEA configuration facilitated long-term measurements by maintaining stable spheroid positioning. Our findings demonstrate that MEA-based impedance sensing, combined with automated analysis, enables continuous monitoring of spheroid physiology and drug responses. The platform may become a powerful tool for preclinical cancer research and drug screening applications.
Organic-transistor biosensors are a promising platform for early diagnostics, and their reported detection limits now dwell in the femto- to attomolar range. However, the metric that dominates the literature, namely the static limit of detection (LoD), captures only one half of what governs the usable performance, and the second half is still not clearly addressed. Through a comparative analysis of organic charge-modulated field-effect transistors (OCM-FETs) and their interfacial (ISOFET) and volumetric (OECT) counterparts, we show that the achievable detection limit factorizes, to first order, into two engineering quantities, namely a capacitive gain term and a dielectric drift-floor term, so that the LoD is governed by their product. We find that the first term has been driven to its geometric minimum through deliberate capacitive-divider and channel down-scaling, whereas the second, the device-side drift floor – which in the OCM-FET reduces to the bulk Maxwell–Wagner relaxation time – has, for the affinity OCM-FET sensors in which the architecture’s advantage should matter most, scarcely been isolated as an independent quantity, even though the coupled foils quantify it routinely. Hence, the defining claim of this architecture, that its detection ceiling is an engineering rather than a thermodynamic limit, remains only half-demonstrated. We make the drift factor explicit through an effective-sensitivity decomposition, place the architectures on a single degree-of-coupling axis, and specify the time-domain measurement required to render the engineering-ceiling claim testable. We further show that, although the response time and the drift floor are set by the same bulk relaxation, they separate by orders of magnitude in affinity sensors, so that the operability number applies there without modification while saturating on a measurable capacitance ratio in the fast-transduction limit.
We present a comprehensive theoretical and numerical framework for the simultaneous detection of multiple toxic gases (SO2, NO2, CO, NH3, H2S) using a graphene ribbon-based terahertz metamaterial absorber. The structure is designed to exhibit multiple resonant modes that exactly match the characteristic rotational-vibrational fingerprints of the target gases in the 0.5–6 THz range. A rigorous multi-physics model is developed, combining quantum-mechanical molecular response via Voigt profiles, a multi-resonance Lorentzian metamaterial model, competitive Langmuir-Hill adsorption isotherms, Maxwell-Garnett effective medium theory for gas-metamaterial composites, and noise analysis for limit of detection (LOD) determination. Closed-form expressions for sensitivity, selectivity, response time, and cross-sensitivity are derived. Numerical simulations predict sub-ppm LODs (0.8 ppm for SO2, 0.6 ppm for NO2), response times below 0.5 s, and selectivity ratios exceeding 20:1 against common interferents such as H2O and CO2. A random forest classifier achieves 98.2% accuracy A random forest classifier achieves 98.2% accuracy on synthetic spectral datasets, with a detailed analysis of accuracy versus signal-to-noise ratio. Environmental stability is analyzed over temperature (250–350 K), humidity (0–100% RH), and long-term operation (3 years) using theoretical degradation models. Adaptive sensing and sensor fusion algorithms are shown to improve measurement efficiency and accuracy. The theoretical predictions are presented with clear discussion of model limitations, including effective medium theory applicability, surface adsorption effects, and the need for experimental validation. This work establishes a solid theoretical foundation for the development of low-cost, real-time, multi-gas sensors for environmental monitoring, industrial safety, and healthcare.
Prostate specific antigen (PSA) is widely recognized as a critical biomarker for diagnosis and clinical surveillance of prostate cancer. In this work, a novel sensitive immunosensor for quantifying of the PSA has been described based on the gold (Au) nanoparticles decorated on/in the BiFeO3 perovskite and modified with PSA biomarker antibodies (Ab) and bovine serum albumin (BSA) via incubation on the nanocomposite (BiFeO3/Au) platform; GCE/BiFeO3/Au/Ab/BSA. The nanocomposite was thoroughly characterized using X-ray diffraction, scanning electron microscopy, energy-dispersive X-ray spectroscopy, transmission electron microscopy, and electrochemical techniques. The obtained results show that the BiFeO3/Au nanocomposite has large surface area and good biocompatible, conductivity and excellent electrocatalytic properties. The analytical performance of the immunosensor; GCE/BiFeO3/Au/Ab/BSA, was evaluated using differential pulse voltammetry (DPV) method with [Fe(CN)6]3−/4− as a probe ion to reflect the PSA concentrations. Under the optimized conditions, the immunosensor exhibited a good linear relationship between the PSA concentrations and DPV signals; 0.1–100 ng/mL of PSA with excellent correlation coefficient (R2 = 0.9997) and low detection limit (47.06 pg/mL). The fabricated perovskite-based immunosensor demonstrated high selectivity, superior sensitivity, good reproducibility, and long-term stability. Finally, the designed immunosensor was successfully applied to PSA detection in real samples, and the results were consistent with those obtained from a commercial enzyme-linked immunosorbent assay (ELISA) method, confirming its analytical reliability.
Over-humidification and condensation in polymer electrolyte fuel cells (PEFCs) can promote local flooding and performance losses, creating a need for simple and localized liquid water detection. In this study, copper interdigitated electrode (IDE) sensors on polyimide substrates are modified with electrochemically deposited polyaniline (PANI) and poly(3,4-ethylenedioxythiophene) (PEDOT) coatings. The sensors are characterized using FTIR, contact angle measurements, optical microscopy, SEM/EDX, XPS, and defined droplet tests. PANI produces a comparatively compact and hydrophobic surface, whereas PEDOT forms a finer, more hydrophilic structure that promotes droplet spreading and water retention. All sensor types show rapid voltage responses to liquid water bridging, with bare copper providing the highest absolute sensitivity. Wet-dry transitions and an 24 h ex-situ PEFC-relevant humid gas protocol, comprising replicated cathode and anode side conditions, reveals distinct response characteristics. Bare copper exhibits a delayed, threshold-like voltage decrease and comparatively unstable baseline behaviour. PEDOT responds strongly to humidification but shows persistent low voltage states and slower recovery, consistent with greater water retention. PANI provides the most balanced performance, combining rapid response, controlled wetting, gradual and reversible signal evolution, and the most stable voltage behaviour within the investigated exposure window. Post-test XPS confirms the presence of oxidized copper species and coating-specific elemental signals, while also indicating that the polymer layers remain detectable after exposure. These results identify PANI as the more promising coating for adapting copper IDE sensors to localized condensation and over-humidification detection under the studied PEFC-relevant conditions.
Folic acid (FA, vitamin B9) is an essential micronutrient widely used in pharmaceutical formulations and dietary supplements. A simple, rapid and cost-effective electrochemical sensing method based on batch injection analysis with amperometric detection (BIA-AMP) was developed for FA determination using an unmodified screen-printed carbon electrode (SPCE). The electrochemical behaviour of FA was investigated by cyclic voltammetry in Britton–Robinson buffer solution, showing an irreversible oxidation process mainly influenced by diffusion, with the highest response at pH 7. Under optimised BIA-AMP conditions (+0.8 V, 80 μL, 204 μL s−1 and 1500 r.p.m.), the method showed a linear response from 0.3 to 8.0 μM, with an average sensitivity of 0.207 μA μM−1 and limits of detection and quantification of 0.09 and 0.29 μM, respectively. A maximum estimated analytical frequency of 350 injections h−1 was calculated from the average duration of one injection cycle, and satisfactory repeatability was achieved with RSD values below 7%. Interference studies showed significant positive interference from ascorbic acid and pyridoxine, whereas thiamine and glucose had negligible effects on the FA response, indicating that the method is most suitable for simple FA formulations without strongly electroactive co-formulated compounds. The method was applied to four pharmaceutical formulations. Quantification by standard addition provided relative errors between −1% and + 2% compared with the declared FA contents, confirming its suitability for FA determination in simple pharmaceutical formulations containing FA as the only declared active ingredient. These results demonstrate the practical potential of coupling disposable unmodified SPCEs with BIA-AMP for rapid FA determination.
