Abstract Simultaneous acquisition of elemental and isotopic data is essential for resolving complex geochemical and biogeochemical processes but is often limited by separate analytical workflows. Here we present a laser ablation split-stream (LASS) MC-ICP-MS/ICP-ToF-MS approach for the simultaneous determination of Fe isotope ratios and multi-element concentrations from a single ablated volume. Method performance was validated using basaltic glass reference materials (BHVO-2G, BCR-2G) and in-house gelatin samples mimicking organic matrices. The obtained δ56Fe values for BHVO-2G (0.14 ± 0.27 ‰, 2s, n = 19) agree with solution-based measurements within analytical uncertainty. Elemental quantification by LASS-ICP-ToF-MS yields recoveries predominantly within 80−120%, demonstrating reliable multi-element performance under split-stream conditions. Calibration in gelatin matrices shows linear responses (R2 = 0.89−0.98) accurately reproducing validation samples for concentrations of 250 and 400 mg kg−1. The reduced linearity is attributed to variability in ablation behavior related to gelatin film heterogeneity, elemental distribution and transient signal instability during laser ablation. The δ56Fe values for the 500 mg kg−1 gelatin sample (0.12 ± 0.31 ‰, 2s, n = 19) was within the analytical uncertainty of conventional (ns)LA-MC-ICP-MS measurements (0.17 ± 0.26 ‰, 2s, n = 9). Overall, the LASS MC-ICP-MS/ICP-ToF-MS approach enables accurate simultaneous isotopic and elemental analysis across both inorganic and organic matrices, providing an effective platform for integrated isotope−element investigations in diverse fields.
Abstract Surface-enhanced Raman spectroscopy (SERS) has emerged as a promising label-free liquid biopsy approach for cancer detection because of its ability to generate molecular fingerprints from complex biofluids. Although numerous studies report high diagnostic accuracies, the analytical and biological origins of the measured spectral signatures remain poorly understood. Most label-free SERS liquid biopsy studies employ colloidal silver nanoparticles (AgNPs) or gold nanoparticles (AuNPs), whereas nanostructured SERS-active surfaces are used less frequently. Accordingly, this perspective focuses on colloidal NP-based SERS platforms, in which biomolecules interact dynamically with NP surfaces and SERS-active sites formed during NP aggregation. The resulting spectra represent only a chemically and spatially selected subset of the biofluid constituents rather than their bulk molecular composition. This interfacial molecular ensemble is governed by NP physicochemical properties, aggregation and formation of SERS-active sites, biomolecular corona development, competitive bio−nano interface interactions, and sample preparation conditions. Furthermore, many studies rely on relatively small and clinically narrow cohorts, heterogeneous experimental protocols, and limited independent validation, raising concerns regarding reproducibility, molecular attribution, and clinical generalizability. This perspective argues that successful clinical translation of label-free, colloidal NP-based SERS liquid biopsy requires a stronger mechanistic understanding of nano−bio interfacial processes, standardized methodologies, rigorous reproducibility assessment, and robust molecular attribution.
Abstract Natural dyes offer biocompatibility and vivid color but are hindered in lateral flow immunoassays (LFIA) by weak signal, poor stability, and single-mode readout. Herein, we propose a Zr-MOF confinement strategy by encapsulating CA within spherical mesoporous UiO-66-NH2 via hydrogen bonding, π−π stacking, and electrostatic interactions to enable intelligent dual-mode LFIA. This porous scaffold, with an encapsulation efficiency of 66.91%, not only concentrates the chromophore and protects it from environmental stress, but also enhances antibody conjugation and creates strong spectral overlap with Arg/ATT-Au NCs, triggering an inner-filter effect (IFE) that enables highly efficient fluorescence quenching. The resulting dual-mode LFIA delivers LOD (limit of detection) of 0.399 ng mL−1 (colorimetric) and 0.298 ng mL−1 (fluorescent), which are approximately 9.95- and 13.33-fold more sensitive than conventional Au NPs-based assays, with recoveries of 85.5−110.3% in food matrices. To fully harness the complementary signals, a hybrid KNN-RF model processes the dual-channel data, achieving 100% classification accuracy and an R2 of 0.9975 for concentration prediction, thus eliminating operator subjectivity. This work demonstrates that MOF encapsulation transforms a fragile natural dye into a robust bifunctional probe, and when paired with machine learning, offers a reliable, intelligent pathway for point-of-care mycotoxin monitoring.
Protein, RNA, and DNA are normally quantified by separate methods, each needing its own sample preparation, reagents, and instrument, which limits multiclass biomarker analysis from small, crude samples. Methods: We developed the GPC3-Chip, a monolithic device fabricated by masked stereolithography (MSLA) 3D printing on a desktop printer. A single 20−60 μL sample is routed through three lanes that combine EDC/NHS amine-coupled antibody immunocapture of glypican 3 (GPC3), an on-chip UV photometric channel at 260 and 280 nm for RNA and DNA, and a pillar mesh architecture proposed to route components by size-dependent diffusional residence time. Results: The GPC3 channel gave a concentration-dependent optical response with an empirical blank-based detection limit of 0.012 ng/mL and 97.2−103.4% recovery in spiked, diluted commercial serum; because antibody-free and isotype control surfaces were not tested, this response is not yet shown to be specific to GPC3 recognition. The RNA and DNA lanes agreed with Qubit measurements (r2 = 0.994 and 0.997; LOD 1.8 ng/mL), and fluorescent dextran tracers showed less than 8% overlap between lanes in buffer. In two murine hepatocellular carcinoma models, the GPC3 channel response exceeded the detection threshold about eight days before tumors were palpable, and the combined GPC3, cfDNA, and RNA integrity readout gave an apparent AUC of 0.987 versus 0.922 for GPC3 alone (estimated in the same samples used to build the model, without cross-validation or an independent validation set). Conclusions: The chip is a proof of concept, low-cost route to concentration-dependent protein and nucleic acid readouts in a single device. Specificity of the GPC3 response, independent validation of the diagnostic model, separation of real biomolecules in complex matrices, RNA and DNA selectivity in mixtures, and performance in human samples remain to be validated.
