Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers worldwide, with a five year survival rate below 10%. The high mortality rate of PDAC is primarily due to its aggressive metastatic nature, late diagnosis, and resistance to available therapies. A strategic research direction that demands significantly more attention is the identification of novel tumor markers and the development of tools for rapid, noninvasive diagnosis of this type of cancer. Circulating tumor cells (CTCs) are a promising target for early cancer detection via liquid biopsy, but their clinical application in PDAC is limited due to their rarity and technical challenges in reliable isolation and analysis. CTCs are typically isolated based on physical properties (size, density, deformability, charge, shape, hydrodynamics) or antigenic markers using specific antibodies. Although numerous cancer-specific antibodies have been identified, a marker uniquely distinguishing PDAC has yet to be found. In this work, we present the latest results concerning the development of a microfluidic platform that enables effective immunomagnetic isolation of PDAC cells from a complex biological sample using two potential PDAC markers: Plectin-1 and EpCAM. Here, we developed a low-cost, easy-to-manufacture, and user-friendly 3D-printed microfluidic platform that integrates a micromixer and a separation chamber for rapid cell isolation. Optimization tests showed that Plectin-1 at a concentration of 0.485 μg/mL captured 102 cells/ml with a recovery of 80% ± 13.3%, EpCAM at a concentration of 0.625 μg/mL captured 103 cells/ml with a recovery of 47% ± 13.3%, and the mixture of EpCAM and Plectin-1 at concentrations of 0.3125 μg/mL and 0.242 μg/mL, respectively, achieved a recovery of 91.3% ± 4.4%. To demonstrate the applicability of the method in biological samples, blood with different anticoagulants was tested. The highest detection sensitivity was achieved in the blood sample with citrate: Plectin-1 at 0.485 μg/mL enabled detection at a concentration of 102 cells/ml (47 ± 9% recovery), and EpCAM at 1.25 μg/mL achieved a 92 ± 8% recovery. The entire isolation process from biological samples was performed in less than 1 min. Using this system combined with antibody-functionalized magnetic nanoparticles targeting EpCAM and Plectin-1, we demonstrated the efficient capture of PDAC cells from biological samples, highlighting Plectin-1 as a promising PDAC marker and the potential of immunomagnetic microfluidic separation for improved diagnostics.

High-temperature, high-pressure (HPHT) nanodiamond (ND) hosts nitrogen-vacancy (NV) centers, solid-state qubits that enable room-temperature quantum sensing by all-optical magnetometry, electrometry, and thermometry. However, the covalent surface functionalization of nanoscale diamond remains largely limited to carboxylate-based chemistries. Amine termination is particularly attractive because theoretical studies predict suppression of midgap states and extended electron-spin coherence times. Recently, chemical activation of alcohol-terminated NDs to alkyl bromides (ND-Br) using SOBr2 has enabled nucleophilic substitution through a carbocation intermediate, allowing formation of simple amine terminations. Here, we evaluate whether sterically demanding amines can form covalent diamond-nitrogen bonds on ND-Br surfaces. ND-Br was reacted with branched, linear, and cyclic amines, including polyethylenimine, diethylenetriamine, and melamine. X-ray spectroscopies were used to confirm successful and to probe the resulting electronic structure at the diamond-amine interface. These results expand the chemical toolbox for tuning diamond surface dipoles and electron affinity, providing new pathways for engineering nanodiamond surfaces for quantum sensing and photocatalysis applications.

Fly ash (FA) represents a promising secondary resource for the recovery of critical metals, particularly aluminum and lithium. This study proposes a combined process of NaOH roasting followed by hydrochloric acid leaching for efficiently extract aluminum and lithium from FA. The effects of key operational parameters on the leaching efficiencies of both metals were systematically investigated Under the optimized conditions (roasting at 700 °C with a FA/NaOH mass ratio of 1:1.1, followed by leaching at 90 °C with a liquid-to-solid ratio of 10:1), the leaching efficiencies reached 90.79% for aluminum and 80.72% for lithium. The underlying extraction mechanism was elucidated through thermodynamic calculations and comprehensive characterization of the roasting products and leached residues. The analysis revealed that the alkali roasting process effectively destroys the stable aluminosilicate matrix, facilitating the subsequent selective dissolution of aluminum and lithium. This work provides an effective strategy for recovering aluminum and lithium and a potential pathway for the sustainable and high-value utilization of FA.

In this research, a novel sensor system was prepared for the rapid and selective determination of Amyloid β1-42 (Aβ1-42), a key biomarker in the early recognition of Alzheimer's disease. A screen-printed gold electrode (AuE) was used as the base for a self-assembled monolayer (SAM)-based molecularly imprinted polymer (MIP). The electrode was modified by sequential incubation in 100 mM cysteamine, 5% glutaraldehyde, and PAMAM G4 dendrimer. Electropolymerization was performed using pyrrole-3-carboxylic acid and Aβ1-42. Desorption was achieved using 500 mM HCl to remove the template molecule, generating specific recognition sites. The sensor's performance was determined by electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV), with additional surface characterization via SEM, XPS, and FT-IR. The nonimprinted polymer (NIP) showed no response to Aβ1-42, confirming the selectivity of the MIP. Selectivity tests using Tau protein showed minimal interference. The quantification limit (LOQ) and the limit of detection (LOD) were found to be 0.42 and 0.14 ng/mL, respectively. In diagnostic applications, the proposed sensor shows promise as a quick, sensitive, and selective way to detect Aβ1-42.

This study investigates the influence of reaction temperature on the coprecipitation synthesis of ZnO NPs and correlates their structural and morphological evolution with their photocatalytic performance. ZnO NPs synthesized at five temperatures, namely, 25, 35, 45, 55, and 65 °C, produced wurtzite-phase materials with varying crystallinities in the range of 77-89%. XRD, SEM, EDX, UV-vis spectroscopy, BET surface area measurement, and PL spectroscopy were used to obtain structural and compositional information about the synthesized NPs. The increase in temperature transforms the nanosheet-like structures into quasi-spherical particles with average crystallite sizes of 14-20 nm, as a function of variations in crystal defects and a high concentration of lattice distortions. Optical characterization revealed a red shift in the absorption edges, with a reduction in the bandgap (E g) to 2.75 eV. PL spectra exhibited a pronounced Stokes shift, confirming defect-mediated emission. The BET analysis revealed a maximum BET surface area of 41.14 m2/g at 45 °C, and the pore size distribution range is 1.6-48.82 nm, along with a significant change in zeta (ζ) potential. The photocatalytic activities were then determined in the degradation of Rhodamine B (Rh-B) under visible-light irradiation in a specially designed photocatalytic reactor. ZnO prepared at 45 °C exhibited the best performance, degrading up to 99% of Rh-B within 200 min, corresponding to a reaction rate constant of 0.022 min-1. Finally, an optimized ZnO sample was produced at 45 °C and directly introduced into the reactor to immobilize the catalyst, thereby avoiding the release of NPs into water streams and eliminating subsequent recovery steps. In this work, the reaction temperature has been identified as a critical determinant of ZnO defect concentration, morphology, and photocatalytic efficiency, providing a scalable route to high-performance photocatalysts for effective, environmentally friendly water purification systems.

α-Amylases are among the most important enzymes used in various industries; however, their large-scale application is restricted by limited stability, low reusability, and low activity loss during operation. In this study, immobilization of α-amylase from Priestia megaterium (PmAmy) onto glutaraldehyde-activated chitosan beads (PmAmy@GA-CS) was optimized using response surface methodology (RSM) and machine learning (ML) approaches, including support vector regression (SVR), Gaussian process regression (GPR), and random forest regression (RFR). The effects of the CS concentration, GA concentration, and enzyme binding time were investigated. RSM analysis identified optimal immobilization conditions at 2.6% CS, 0.5% GA, and 6 h of binding time, resulting in approximately 90% immobilization efficiency and high enzymatic activity. ML models captured similar immobilization trends, with GPR exhibiting the highest predictive accuracy (R 2 ≈ 0.98) and superior generalization performance compared to SVR, RFR, and RSM. Immobilization produced nearly uniform porous beads and induced a shift in the pH optimum toward slightly alkaline conditions (from pH 7.0 to 8.0), while the temperature optimum remained unchanged at 40 °C. Furthermore, the immobilized enzyme exhibited significantly improved stability, with a 2.1-fold increase in thermal stability and enhanced storage stability. Moreover, PmAmy@GA-CS retained approximately 50% of its initial activity after ten consecutive reuses, demonstrating strong operational stability. Overall, this study highlights the complementary strengths of the RSM and ML in designing robust immobilized enzymes and provides a solid foundation for the development of reusable biocatalysts.