This study presents the design and application of an electrochemical sensor for selective detection of lead ions (Pb²⁺) based on ionophore-modified raspberry-like Fe₃O₄–Au nanostructures. The material was engineered with a magnetic Fe₃O₄ core, coated with polyethyleneimine (PEI) to facilitate nucleation, and subsequently decorated with Au nanoparticles, providing a raspberry-like (Fe₃O₄@PEI@AuNPs) nanostructure with high surface area and excellent electrochemical conductivity. Surface functionalization with Lead Ionophore IV (ionophore thiol) introduced Pb²⁺-selective binding sites, whose presence and structural evolution were verified by TEM and Raman spectroscopy. The Fe₃O₄ core endowed strong magnetic properties, enabling facile manipulation and immobilization onto screen-printed carbon electrodes (SPCEs) via physical adsorption, while the Au nanoparticles enhanced electron transfer, supplied thiol-binding sites for stable ionophore anchoring, and increased the effective electroactive surface area. Operational conditions were systematically optimized, with acetate buffer (HAc/NaAc, pH 5.7) and chronoamperometric preconcentration (CA) at −1.0 V for 175 s identified as optimal for differential pulse voltammetry (DPV) measurements. Under these conditions, the sensor exhibited a linear response toward Pb²⁺ from 0.025 mM to 2.00 mM with superior sensitivity and reproducibility compared to conventional AuNP-modified SPCEs. Furthermore, the ionophore-modified Fe₃O₄–Au nanostructure-based sensor demonstrated outstanding selectivity for Pb²⁺ over competing heavy metal ions (Cd²⁺, Hg²⁺, Cr³⁺), owing to the specific coordination interaction of Lead Ionophore IV with target ions. These findings highlight the potential of raspberry-like Fe₃O₄@PEI@AuNP nanostructures as a robust and efficient electrochemical platform for the sensitive and selective detection of toxic heavy metal ions.

Multivariate structural-response classification supports bridge monitoring by integrating synchronized measurements from multiple sensors and assigning an observation window to a predefined structural or loading condition. The main technical challenge is to compress temporally and spatially distributed sensor information into a fixed-length representation without losing transient or localized condition-sensitive features. However, previous studies have mainly focused on backbone architecture design, whereas the influence of sequence-level aggregation has rarely been isolated under a controlled backbone and training configuration. In this study, xLSTM-m is proposed as a modified variant of the extended Long Short-Term Memory (xLSTM) network for multivariate bridge-response classification. The conventional last-hidden-state readout is replaced with masked mean pooling over all valid hidden states, while the hybrid sLSTM/mLSTM backbone and the remaining training configuration are kept unchanged. This controlled design isolates the sequence-level aggregation strategy as the only architectural variable. A comparative evaluation is conducted using eight neural models. These models include xLSTM-m, the baseline xLSTM, and six Transformer-based architectures. Three bridge-monitoring datasets are considered: an FBG strain-response dataset and a PCB Piezotronics acceleration-response dataset acquired from a laboratory-scale cable-stayed bridge model, together with the field-scale Z24-9Setup acceleration benchmark. A strict five-fold cross-validation protocol is adopted. The proposed xLSTM-m ranks first on all three datasets. It achieves a mean accuracy of 95.29%, a mean F1-score of 95.57%, and the lowest inter-fold standard deviation of 0.84. Relative to the strongest Transformer baseline for each dataset, the corresponding accuracy gains are 6.30 percentage points for FBG, 3.66 percentage points for PCB, and 31.76 percentage points for Z24-9Setup. These results indicate that, for the evaluated bridge strain and acceleration datasets, sequence-level aggregation substantially affects both classification accuracy and inter-fold stability. Further validation using quasi-static responses, environmental variables, other structural systems, and unseen operating conditions is required before broader applicability across the SHM domain can be established.