Lithium resource serves as critical material for lithium-ion battery industry, and it becomes indispensable for global technological progress and economic development. Facing the challenge of high magnesium-to-lithium ratio in salt lake brines of China, electrodialysis (ED) exhibits great potential as an extraction technology. However, lithium-magnesium separation using ED is a complicated multi-physical process, and insufficient understanding of lithium extraction mechanism limits its efficiency improvement. Hence, the current work constructs a multi-physical model for lithium extraction from high magnesium salt lake brine using ED to reveal ion migration behavior and separation mechanism under synergistic fields. On the basis of a parametric analysis with optimization, the magnesium-to-lithium ratio in brine is dramatically decreased from initial state at 105.05 to 1.87 at the outlet of ED device under the temperature of 323.15 K, potential difference of 0.5 V, and membrane thickness of 0.35 mm. Furthermore, five neural-network models are developed as surrogates of the multi-physical model using 2784 simulation-generated samples without experimental data. Multidimensional dilated convolutional neural network (MDCNN) achieves the lowest in-domain mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of 0.0293%, 0.0395%, and 0.7735%, respectively, whereas R[2] (>0.998) provides limited discrimination. In 24 additional out-of-domain simulation cases, MDCNN achieves the lowest MAPE (3.8846%), whereas convolutional attention neural network (CNN-Attention) achieves the lowest RMSE (0.2525) and the highest R[2] (0.9494), demonstrating complementary generalization strengths. The current work offers a theoretical basis for optimizing lithium-magnesium separation performance using ED with neural-network surrogate modeling, laying foundation for big data analysis and development of experimental correlations.

Capillary electrophoresis (CE) is an efficient, rapid, and sensitive separation and analysis technique that has been widely applied in biochemistry, pharmaceutical research, and environmental monitoring. However, charge-coupled device (CCD)-based CE fluorescence signals are susceptible to baseline drift, random noise, and channel crosstalk, which reduce the accuracy of peak recognition. To meet the demand for high-precision signal analysis, this study proposes a convolutional neural network (CNN)-bidirectional gated recurrent unit (BiGRU) peak recognition algorithm on the basis of wavelet transform and one-dimensional asymmetric least squares (ALS). In the proposed system, an FPGA-based platform is used for real-time signal acquisition and data transmission, whereas the host computer performs subsequent processing of the received spectral data. Experimental results show that the proposed method achieves high-accuracy peak recognition on the spectral dataset, with Precision, Recall, and F1-score reaching 98.44%, 99.93%, and 99.18%, respectively. These results demonstrate its good robustness and practical applicability, providing technical support for the miniaturization and intelligent development of portable gene sequencers.

Type 2 diabetes mellitus (T2DM) is the most common form of diabetes worldwide. In Brazil, diabetes affects 7.7% of adults and represents a substantial public health and economic burden. Variants in the MTR gene may disrupt one-carbon metabolism, potentially contributing to hyperhomocysteinemia and increased susceptibility to T2DM. A case-control study involving 219 age- and sex-matched individuals was conducted to evaluate the association between the MTR rs1805087 variant and susceptibility to T2DM. Associations under different genetic inheritance models were examined using χ[2] tests and odds ratios (95% confidence interval [CI]). In parallel, machine-learning (ML) algorithms were applied, with predictive performance assessed using accuracy, precision, sensitivity, and receiver operating characteristic-area under the curve (ROC-AUC). The variant was not independently associated with T2DM. However, among individuals with T2DM, genotype distributions differed according to smoking status, with significant associations under the codominant and recessive genetic models. Exploratory ML analyses showed limited discriminative ability in the independent test set, with ROC-AUC values of 0.555 for K-nearest neighbors (KNN), 0.524 for support vector machine (SVM), and 0.503 for random forest (RF). All models exhibited substantial declines in performance compared with cross-validation results obtained in the training set. MTR rs1805087 was not independently associated with T2DM in this studied population. However, differences in genotype distributions according to smoking status among individuals with T2DM may reflect context-dependent genetic effects. Exploratory ML analyses showed limited predictive performance but identified smoking as one of the most influential predictors across all evaluated algorithms.

Fluorescent capillary gel electrophoresis (CGE) with sodium dodecyl sulfate (CE-SDS) provides a powerful, high-sensitivity alternative to ultraviolet (UV)-based detection for characterizing therapeutic monoclonal antibodies (mAbs). Regulatory and standards organizations, such as the United States Pharmacopeia (USP), include only UV-based CE-SDS methods, hindering the adoption of alternative detection methods. There is growing opportunity to expand beyond exclusively UV-based CE-SDS methods. In this study, we present an analytical validation of a light-emitting diode (LED) fluorescence-based parallel CE-SDS method for both non-reduced and reduced analysis of therapeutic antibodies. Using the NISTmAb reference material as a model system, size heterogeneity critical quality attributes (CQAs), including monomeric purity (non-reduced conditions), percent glycosylation (reduced conditions), and percent thioether (reduced conditions), were assessed. The fluorescence method demonstrated high specificity and precision with relative standard deviation (RSD) values <1% for monomeric purity and glycosylation, and <3% for thioether, as well as robust performance across variations in injection voltage, electrophoresis voltage, labeling temperature, and Labeling Buffer concentration. Ruggedness testing across users and reagent lots confirmed reproducibility, and accuracy assessments showed strong agreement with reported values from the National Institute of Standards and Technology (NIST) and traditional UV detection measurements. Linearity studies yielded the coefficient of determination (R[2]) values >0.99 for both non-reduced and reduced analyses. These results highlight the high sensitivity, stable baseline performance, and suitability of LED fluorescence-based parallel CE-SDS as a validated, higher-throughput alternative to traditional UV-based methods for mAb quality control (QC).

Chiral analysis plays a relevant role in numerous scientific fields, such as the pharmaceutical, biomedical, agrochemical, environmental, or food science, due to the different properties that stereoisomers may present. Capillary electrophoresis (CE) techniques, mainly electrokinetic chromatography (EKC) and micellar electrokinetic chromatography (MEKC), are well-established techniques to achieve enantiomeric separations owing to its high selectivity, methodological versatility, and low consumption of reagents and samples. In this context, method sensitivity is a critical characteristic, as it determines the capability to detect and quantify enantiomers at trace levels and to assess the enantiomeric purity of raw materials, pharmaceutical or agrochemical formulations, and food ingredients, among others. This article reviews the analytical strategies reported from 2022 to June 2026 aimed at enhancing the sensitivity in chiral CE techniques and to apply them to the analysis of pharmaceutical formulations, and to biological, environmental, and food samples. Off-line and in-capillary preconcentration strategies, as well as their combinations, together with the use of alternative detections systems to UV-Vis detection, are described as the most effective. The most relevant results are presented and critically discussed through the manuscript.

Biologics such as therapeutic monoclonal antibodies (mAbs) require comprehensive chemistry, manufacturing, and control (CMC) strategies to ensure product safety, efficacy, and consistency. Among these strategies is the comprehensive assessment of product-related variants arising throughout the product development lifecycle. Routine capillary isoelectric focusing (cIEF) analysis of a therapeutic IgG molecule (mAb1) revealed a novel, poorly resolved basic peak at varying abundance across several lots, prompting an investigation. A preparative cIEF-based fractionation strategy with downstream identity characterization by both intact and site-specific mass spectrometry was performed, revealing several basic posttranslational modifications (PTMs), including incomplete N-terminal pyroglutamate conversion and Fc methionine oxidation. These PTMs were ultimately deemed low risk due to their overall low abundance in the unfractionated control and lack of impact on target binding, informing data-driven decision-making for cIEF integration at product release.

Adavosertib (ADB) is an investigational oral Wee1 kinase inhibitor evaluated in acute myeloid leukemia and solid tumors. Reported ADB assays all use hydrophilic interaction or aqueous normal-phase chromatography because the analyte is poorly retained under reversed-phase conditions, and all target plasma, blood, or tissue rather than microsomal incubations. This work establishes reversed-phase retention of ADB and applies the assay to substrate-depletion metabolic stability in human liver microsomes (HLMs). ADB was retained on an Eclipse Plus C8 phase (2.1 × 50 mm, 1.8 µm) using 10 mM ammonium formate at pH 3.2 in water/acetonitrile (75:25, v/v) at 0.35 mL/min, eluting at 0.75 min against fedratinib (internal standard [IS]) at 0.52 min. Validation gave linearity from 1 to 4000 ng/mL under 1/x weighting (y = 3.1389x - 6.7996; r[2] = 0.9973). Intraday precision and accuracy were 0.59%-3.12% RSD and -1.11% to -9.00% error; inter-day, 0.70%-3.22% RSD and -2.20% to -12.00% error. The method cuts cycle time 5-fold and mobile-phase use roughly 11-fold. Because the IS is not stable-isotope-labeled, compensation for HLM matrix effects may be less complete than with a labeled analog. In vitro t1/2 was 26.56 min, and intrinsic clearance was 30.53 mL/min/kg, indicating high clearance. Greenness scored 0.63 (analytical GREEnness metric [AGREE]).

Molecularly imprinted polymers (MIPs) have become significantly more desirable as synthetic polymers with recognition sites designed for target molecules for use in liquid-phase separation due to their high selectivity, stability, and robustness. However, the development of MIPs remains challenging, as the selection of functional monomers, cross-linkers, pore-forming agents, and polymerization conditions often relies on labor-intensive experimental trial-and-error methods. Recent advances in artificial intelligence (AI), machine learning (ML), and silica computational tools have presented new opportunities to accelerate the rational design and optimization of MIPs by enabling data-driven prediction and decision-making. The integration of AI and ML with MIP technology and liquid chromatography (LC) enables the development of highly selective separation materials. These combined approaches support real-time analysis and adaptive process control, driving progress towards fully autonomous analytical systems. ML algorithms, including artificial neural network (ANN), support vector machines, random forests (RF), and gradient boosting models, have been successfully applied to predict adsorption capacity, imprinting factor, binding affinity, and synthesis performance. In parallel, computational techniques such as molecular docking, molecular dynamics (MD) simulations, and density functional theory (DFT) calculations have enabled the understanding of molecular recognition mechanisms. Furthermore, AI tools have further facilitated more rational and systematic selection of imprinting components. Beyond materials design, AI is increasingly integrated into liquid-phase separation technologies through quantitative structure-retention relationship (QSRR) models, Bayesian optimization, and reinforcement learning. These approaches enable automated retention time predictions, chromatographic method optimization, and intelligent data analysis. This review comprehensively addresses the latest developments published between 2018 and 2025, with particular focus on recent developments that reflect the rapidly evolving intersection of AI/ML in MIP technology and liquid-phase separation.

Reversed-phase liquid chromatography is routinely used for oligonucleotide analysis, often requiring the use of ion-pairing reagents for resolving closely related oligonucleotides. However, ion-pairing reagents have their fair share of obstacles such as instrument contamination or prolonged column equilibration times. In this work, we demonstrate that their use in reversed-phase liquid chromatography is redundant when separating short 3-mer phosphorothioate oligonucleotides and their diastereomers. These short 3-mer sequences used as model compounds mimic possible degradation products or synthetic impurities. A separation of these sequences, which differ in the number and placement of phosphorothioates, was achieved by further method optimization. The BEH C18 and phenyl-based columns were utilized individually and in series, which improved the resolution of closely related sequences. A mobile phase consisting of 25 mM ammonium acetate (pH 6.8) yielded sufficient retention with acetonitrile as the organic modifier. A shallow gradient starting with a high aqueous phase (>98%) was essential to separate a fully modified 3-mer from its partially modified counterparts.

A theoretical analysis is presented for the sedimentation potential of a dilute suspension of charged spherical colloidal particles in an uncharged polymer gel medium saturated with an electrolyte solution. The polymer gel is described by the Brinkman-Debye-Bueche model, in which the polymer network provides hydrodynamic resistance to the flow of the electrolyte solution. An important advantage of sedimentation-potential measurements for determining the zeta potential is that, unlike electrophoretic mobility measurements, they do not require an externally applied electric field, since the potential arises from the gravitational sedimentation of charged particles. In the present study, rather than deriving the sedimentation potential directly from the coupled electrokinetic equations, we employ the Onsager reciprocal relation between sedimentation and electrophoresis. The sedimentation potential is therefore obtained directly from the electrophoretic mobility of a spherical particle in the same gel medium, for which an analytical expression has been derived previously. This approach leads to a simple analytical expression for the sedimentation potential and establishes a direct connection between sedimentation and electrophoresis in an uncharged polymer gel medium. The result reduces to the corresponding expression for a particle in a free electrolyte solution in the limit of vanishing hydrodynamic resistance of the polymer network. The present analysis demonstrates that the Onsager reciprocal relation provides a simple and useful route for obtaining sedimentation potentials from established gel electrophoresis theories.

Understanding interfacial interactions on porous water filtration membranes and developing innovative methods to reduce membrane fouling remain key challenges in membrane-based water treatment systems. This work presents an innovative 3D microfluidic system that integrates electrochemical impedance spectroscopy (EIS) to examine membrane-electrolyte interactions on nonconductive polymeric membranes. Glass slides patterned with micro-gold electrodes act as the working, reference, and counter electrodes. In contrast, a porous nylon membrane is sandwiched between them using pressure-sensitive double-sided tape with microchannels. The device was tested through colorimetric and fluorescence-based leakage analysis, confirming complete fluid confinement within the microchannel and demonstrating that the tape-membrane interfaces are robust and leak-free. To assess the electrochemical performance of the platform, EIS was performed at varying KCl concentrations as the electrolyte. The results showed a strong link between charge transfer resistance and electrolyte ionic strength, in line with classical electrochemical theory, which states that higher ionic strength compresses the electric double layer and promotes faster charge transfer. As KCl concentration increased from 1 to 10 and 100 mM, the charge transfer resistance decreased significantly, indicating improved ionic conductivity and lower interfacial impedance, and producing stable, reproducible Nyquist spectra at higher concentrations. Comparison with the control configuration (without the membrane) revealed consistently higher charge-transfer resistance in the membrane-integrated device, confirming the porous membrane's contribution to ion-transport resistance. Overall, these findings demonstrate that the microfluidic membrane device offers a leak-free, stable, and precisely controlled microscale environment, ideal for studying interfacial electrochemical processes and fouling behavior. It also provides a versatile foundation for future integration of spectroscopic, photoelectrochemical, and advanced membrane-science techniques for real-time mechanistic investigations.

In this article, we investigate a derived moving reaction boundary (MRB) generated upon current application in a discontinuous electrolyte system consisting of formic buffer (anolyte) and sodium hydroxide (catholyte). The model showcases the formations of two derived zones, including an acidic zone (Zone β1) and an alkaline zone (Zone β2), and the subsequent development of a derived MRB between Zones β1 and β2. The proposed model was validated through numerical simulations and experiments using capillary electrophoresis and gel-based visualization, with insulin and phenylalanine selected as model analytes. Quantitative comparisons demonstrate good agreement between the derived MRB theory and experimental observations, whereas the conventional model fails to accurately predict the observed boundary behavior. This work provides new insights into reaction boundary dynamics in weak acid-strong base electrolyte systems and offers practical guidance for sample stacking strategies in peptide and protein electrophoresis.

Monodispersed molecularly imprinted polymers (MIPs) for abiraterone (Abi), 5'-chloroabiraterone (5'-ClAbi), 5'-methylabiraterone (5'-MeAbi), and 6'-methylabiraterone (6'-MeAbi) (MIPAbi, MIP5'-ClAbi, MIP5'-MeAbi, and MIP6'-MeAbi, respectively) were prepared using methacrylic acid (MAA) and ethylene glycol dimethacrylate (EDMA) as a functional monomer and a crosslinker, respectively, by multistep swelling and polymerization. The molar ratio of the template molecule to MAA and EDMA was 2:7:25. The retention and molecular recognition properties of the non-imprinted polymer and MIPs (MIPAbi, MIP5'-ClAbi, MIP5'-MeAbi, and MIP6'-MeAbi) were evaluated using a mobile phase consisting of sodium phosphate buffer and acetonitrile in reversed-phase liquid chromatography (LC). Abi, 5'-MeAbi, and 6'-MeAbi showed the highest retention on these MIPs in a mobile phase consisting of sodium phosphate buffer and acetonitrile (50:50) at pH 5.7, whereas 5'-ClAbi exhibited the highest retention in a mobile phase consisting of sodium phosphate buffer and acetonitrile (50:50) at pH 3.7. On the other hand, the imprinting factors of the template molecules followed the order MIP5'-ClAbi < MIP5'-MeAbi < MIP6'-MeAbi < MIPAbi. The DFT calculations suggested two hydrogen‑bonding interactions between the (substituted) pyridines and the hydroxy groups of Abi and its derivatives with MAAs in the gas phase. The interaction energies followed the order MIP5'-ClAbi < MIPAbi < MIP5'-MeAbi < MIP6'-MeAbi. These results suggested that ionic or hydrogen-bonding interactions and hydrophobic interactions could work for the retention and molecular recognition of Abi and its derivatives on these MIPs in reversed-phase mode.

The capacitively coupled contactless conductivity detector features a reference channel for the suppression of the signal caused by the background conductivity and high voltage excitation for a good signal-to-noise ratio. It has a built-in high resolution analog-to-digital convertor, and the numerical signal is passed to an attached personal computer via a universal serial bus. There the readings are plotted and can be saved in a file. The operating frequency (200-1000 kHz), as well as the excitation amplitudes (100-350 V) of the signal and reference channels can be set independently by the user. This allowed a detailed evaluation of the influence of the individual sensitivities of the two channels on the electropherograms, and it is demonstrated that corrupt signals may be obtained for a referenced detector unless the working conditions are controlled appropriately. Detection limits of about 5 µM were obtained for small inorganic cations.

Deterministic lateral displacement (DLD), a hydrodynamic particle manipulation technology, has been extensively studied for the development of fast and efficient microfluidic chips for particle manipulation and detection. However, particles are prone to clogging in DLD chips with closely spaced pillars, which can reduce sorting efficiency. Previous studies have shown that elliptical pillars provide better separation performance than equilateral triangular pillars. In elliptical-pillar DLD, a negative angle of inclination (Aoi) reduces the particle period and facilitates efficient isolation of large particles, whereas a positive Aoi decreases the critical diameter, thereby improving the sorting of smaller particles. On the basis of these findings, we designed a two-stage DLD microfluidic chip with inclined elliptical-pillar arrays, using an Aoi of -45° in the first stage and 45° in the second stage. The combination of negative and positive Aoi values enhanced the isolation of large particles, improved the purification of medium- and small-sized particles, and reduced the risk of clogging. This design provides a practical approach for improving the separation and purification performance of DLD devices and offers potential for efficient multi-size particle separation.