Pore modulation via hydrothermal carbonization (HTC) needs investigation due to its crucial effect on surface that influences its multirole utilization of such ultraporous sorbents in applications of energy storage- hydrogen and capacitive- as well as for pollutant abatement- carbon capture and dye removal. Hence, loblolly pine was hydrothermally carbonized followed by KOH activation to synthesize superactivated hydrochars (SAH). The resulting SAHs had specific surface area (SSA) 1462–1703 m²/g, total pore (TPV) and micropore volume (MPV) of 0.62–0.78 cm³/g and 0.33–0.49 cm³/g, respectively. The SAHs exhibit excellent multifunctional performance with remarkably high atmospheric CO₂ capture of 145.2 mg/g and high pressure cryogenic H₂ storage of 54.9 mg/g. The fabricated supercapacitor displayed substantial specific capacitance value of maximum 47.2 Fg⁻¹ at 1 A g⁻¹ in 6 M KOH and highest MB dye removal of 719.4 mg/g. Higher HTC temperature resulted in increased surface porosity as higher SSA, TPV benefitted H₂ storage and MB dye removal while superior MPV favored CO₂ capture. Moderate HTC temperature ensured higher mesopore-to-macropore volume ratio favoring electrochemical performance. Isotherm modelling of the adsorbates was compared using models: Langmuir, Freundlich, Langmuir- Freundlich and Temkin.

Heavy metal contamination and its detrimental health effects are a growing concern globally. Several metal mitigation systems and regulatory approaches have been implemented to minimize the negative impacts on human health. However, none of these function at maximum efficiency, mainly due to the lack of accurate information about metal speciation. Therefore, there is a critical need to develop novel, cheap, efficient, and robust metal detecting sensors. In this study, we describe the application of a nanopipet based electrochemical sensor to detect aqueous Cd(II) ions. The inner radius of our nanopipets is ∼300 nm, and the fundamental mechanism behind our sensor's response is ion transfer between two immiscible electrolyte solutions (ITIES). The absence of redox behavior makes ITIES an excellent, attractive electrochemical tool to study various ions in aqueous solutions. In this study, we used 1,10-phenanthroline as our ionophore in the organic phase (dichloroethane) to facilitate the transfer of Cd(II) ions from the polar aqueous phase to the less polar organic phase. Unlike previous studies, we characterized our nanopipet in complicated matrices, including, but not limited to, tris buffer and artificial seawater. We performed quantitative assessments to determine our sensor's limit of detection, stability, sensitivity, and selectivity. We further show that our nanosensor can detect free Cd(II) ions in the presence of strong complexing agents such as ethylenediaminetetraacetic acid, 2,3-dimercaptosuccinic acid, etc. We quantified the concentration of free Cd(II) ions in a water sample collected from a local lagoon. Thus, we showcased the power of our nanopipets to act as a robust, accurate, and efficient speciation sensor to detect Cd(II) ions in environmental samples.

Electrochemical sensors, particularly those based on ion transfer at the interface between two immiscible electrolyte solutions (ITIES), offer significant advantages such as high selectivity, ease of fabrication, and cost effectiveness for toxic metal ion detection. However, distinguishing between cyclic voltammograms (CVs) of analytes with closely spaced half-wave potentials, such as Cd²⁺ and Cu²⁺, remains a challenge, especially for non-expert users. In this work, we present a novel methodology that integrates advanced artificial intelligence (AI) models with ITIES-based sensing to automate and enhance metal ion detection. Our approach first employed a convolutional neural network to classify CVs as either ideal or faulty with an accuracy exceeding 95 percent. Ideal CVs were then further analyzed for metal ion identification, achieving a classification accuracy of 99.15 percent between Cd²⁺ and Cu²⁺ responses. Following classification, an artificial neural network was used to quantitatively predict metal ion concentrations, yielding low mean absolute errors of 0.0158 for Cd²⁺ and 0.0127 for Cu²⁺. This integrated AI–ITIES system not only provides a scientific methodology for differentiating analyte responses based on electrochemical signatures but also substantially lowers the expertise barrier for sensor signal interpretation. To our knowledge, this is the first report of the AI-assisted differentiation and quantification of metal ions from ITIES-based CVs, establishing a robust framework for the future development of user-friendly, automated electrochemical sensing platforms for environmental and biological applications.