Search Results (659)

Encapsulation and coating approaches have become important tools in modern food systems for improving the stability, functionality, sensory quality, processability, and controlled delivery of bioactive and sensitive food ingredients. Their widespread adoption has enabled the incorporation of functional compounds into diverse food products while enhancing product quality, shelf life, and manufacturing performance. However, successful implementation depends not only on the encapsulation or coating strategy itself but also on the interactions among ingredient properties, carrier materials, food matrices, processing conditions, storage environments, and intended release behavior. Whereas recent reviews have mainly focused on specific encapsulation methods, carrier systems, industrial implementation, sensory functions, or regulatory aspects separately, this review integrates scientific publications and patent literature to examine method and system selection from food-engineering, formulation, processing, and industrial perspectives. Conventional processing and formulation approaches, including spray drying, freeze drying, coacervation, ionic gelation, emulsion-based encapsulation, and fluidized-bed coating, remain widely used, while established carrier systems such as liposomes and cyclodextrin inclusion complexes continue to support ingredient protection and delivery. Emerging carrier systems, including nanoemulsions, nanoliposomes, lipid nanoparticles, and hybrid multilayer structures, together with fabrication methods such as electrospraying and microfluidics, provide greater control over carrier architecture and release behavior but continue to face challenges related to manufacturing scalability, production throughput, storage stability, production cost, regulatory acceptance, and validation under industrial processing conditions. Although patent activity demonstrates continuing development of processing methods and carrier designs, patent publications alone do not establish commercial manufacture, market adoption, or industrial implementation. Across food applications, encapsulation improves ingredient protection, oxidation stability, sensory quality, dispersibility, controlled release, and process compatibility. By integrating research evidence with patent literature, this review further shows that recent progress is characterized primarily by application-driven refinement of carrier systems and fabrication methods rather than replacement of established approaches. Pet food is discussed as a representative specialized food application illustrating how encapsulation and coating strategies require adaptation to product format, processing severity, storage stability, palatability, and species-specific digestive requirements. Overall, this review highlights application-oriented food-engineering principles for selecting encapsulation methods and carrier systems suitable for industrial food applications. Full article

Bioactive polysaccharides (e.g., fucoidan, β-glucans, and medicinal plant polysaccharides) contain functional groups that interact with drug molecules, and some also retain their own biological activities. Through reversible noncovalent interactions, they can associate with small-molecule drugs and form supramolecular nanocomplexes, defined here as nanoscale assemblies in which the polysaccharide is a main structural component and its association with the drug contributes to assembly or drug retention. Multicomponent composites and bulk local matrices are discussed separately as related or extended systems. The review covers hydrogen bonding, hydrophobic association, electrostatic complexation, π–π stacking, and the cooperation among these interactions, together with the effects of pH, ionic strength, concentration, and solvent composition. Nanoprecipitation/solvent exchange, polyelectrolyte complexation, direct aqueous self-assembly, and microfluidic-assisted assembly are compared with respect to nanostructure formation, process control, and reproducibility. Molecular, colloidal, solid-state, and computational evidence is examined together when interpreting structure–assembly–performance relationships. Reported advantages include improved drug dispersibility, colloidal stability, release control, bioavailability, cellular uptake, biodistribution, and safety. In some systems, the polysaccharide itself may also contribute to therapeutic effects in tumors, inflammatory diseases, and wound healing. Related local-matrix systems are considered separately. Further development of these nanocomplexes will require better quantitative analysis of assembly mechanisms, more consistent polysaccharide characterization, careful biocompatibility assessment, scalable preparation, and longer-term safety evaluation. Full article

For nearly a century, flotation kinetics has relied on deterministic first-order rate equations treating the cell as a homogeneous reactor, a paradigm that faces significant limitations for heterogeneous ores, especially coal, whose organic macerals, porosity, and oxidation susceptibility defy a single rate constant. While more advanced distributed-k, mixed-order, and population-balance models can account for certain types of particle heterogeneity (e.g., size or liberation), they still assume that the floatability distribution remains invariant during flotation, an assumption that fails when surface chemistry evolves concurrently with the separation process. Breaking from chronological cataloguing, this review proposes a three-dimensional taxonomy based on physical scale, inherent material heterogeneity, and epistemic certainty. We demonstrate that critical industrial prediction failures arise from structural mismatches between model physics and particle surface chemistry, notably time-dependent oxidation deactivation and selective maceral recovery. Six fundamental failure modes are identified, from neglected time-dependence of rate constants to the absence of a thermodynamic deactivation term, corroborated by experimental evidence from coal and base-metal flotation. Advanced microfluidic, automated mineralogical, surface-sensitive spectromicroscopic, CFD-DEM, and physics-informed machine learning tools are dismantling the black box of the flotation rate constant “k”. We introduce the Distributed Reactive Surface Kinetics (DRSK) framework, which embeds particle-scale heterogeneity into a population balance via an adaptive surface-sensitive selection function and treats kinetic uncertainty through stochastic differential equations. A comprehensive comparison table facilitates the transition from conventional models to the DRSK paradigm. We conclude with a roadmap for flotation kinetics 4.0, where digital twins, real-time froth analytics, and self-calibrating hybrid models transform this empirical discipline into a truly predictive engineering science. Quantitative validation against published coal and copper flotation data demonstrates that DRSK reduces prediction error by 60%–75% compared to conventional first-order and distributed-k models, while providing probabilistic uncertainty bounds essential for risk-based decision-making. The framework is elaborated for coal and conventional minerals, underscoring why coal demands its own dedicated kinetic theory and how these lessons can revolutionize the processing of increasingly complex, low-grade ores and secondary resources. Full article

Cardiac-related pain, including angina pectoris and pain accompanying myocardial infarction, remains clinically important, and existing treatments are inadequate for some patients. Animal and primary-tissue studies provide important mechanistic evidence, but species differences and limited access to patient-matched human neuro-cardiac tissue constrain translation. This review synthesizes the following separate advances relevant to future human iPSC-based cardiac-pain modeling: molecular mechanisms that may be reconstructed in vitro; platforms ranging from two-dimensional cultures to proposed sensory-innervated three-dimensional and microfluidic systems; and potential applications in target validation, patient-specific modeling, and analgesic screening. We distinguish direct human iPSC evidence from findings obtained in animal, primary-cell, cardiac-only, autonomic-neuron, or non-cardiac pain models. We also discuss maturation, standardization, and regulatory challenges. iPSC technologies may complement existing models and support mechanism-focused, patient-stratified research, but direct validation in human sensory–neuron–cardiac systems remains limited. Full article

High-efficiency mixing and precise droplet generation are essential for the broad application of microfluidic chips in biology, chemistry, and medicine, with integrated micromixer and microdroplet generator systems playing an increasingly important role in diagnostics and drug detection. This review offers a comprehensive and systematic overview of micromixers and microdroplet generators, covering their classification, working principles, performance characterization, and integration strategies. Passive and active micromixers, the latter employing pressure, electric, acoustic, magnetic, and thermal fields to enhance mixing, are summarized with a critical discussion of their advantages, limitations, and structural optimization. Various droplet generation methods, including crossflow, flow focusing, coflow, step emulsification, and active techniques, are examined alongside key parameters affecting droplet size, monodispersity, and generation frequency. Performance characterization metrics for both components, including mixing efficiency, mixing time, pressure drop, droplet size distribution, generation frequency, and stability, are also discussed. The review then focuses on integration, emphasizing two primary architectural strategies: mixing reagents before encapsulation and inducing mixing within droplets after formation. Synergistic applications in single cell analysis, materials synthesis, and drug screening are presented. Notably, unlike previous reviews that treat micromixers or droplet generators separately, this review uniquely emphasizes architectural integration and critically evaluates the associated trade-offs. Current challenges and future directions, including material selection, fabrication techniques, and practical application-oriented considerations, are also addressed. Full article

Lipid nanoparticles (LNPs) are now the leading delivery platform for nucleic acid therapeutics, but progress in the field is measured almost entirely by physicochemical and computational proxies rather than by functional properties that determine therapeutic outcomes. This review examines six interconnected areas of LNP development: microfluidic manufacturing, lyophilization, transdermal microneedle delivery, machine learning-guided formulation design, endosomal escape biology, and protein corona-mediated organ targeting. Although these areas are often discussed separately, they are linked by a common gap between routinely measured physicochemical or computational endpoints and the biological outcomes that determine therapeutic performance. A recently developed antifouling coating substantially reduced microfluidic channel fouling under the tested conditions, although its scalability remains to be validated. Lyophilization, by contrast, still requires formulation specific re-optimization for each new lipid composition, which remains an important barrier to clinical translation. In microneedle-based delivery, physicochemical integrity after fabrication is routinely treated as a proxy for therapeutic function, although, to our knowledge, no published study has directly compared endosomal escape capacity before and after microneedle fabrication. In machine learning, model accuracy is limited primarily by fragmented, outcome-biased training data rather than by algorithm design. Independent measurements of endosomal escape efficiency converge on a low ceiling whose biological origin, whether lipid-specific or inherent to the mechanism, remains unknown. For organ-selective targeting, one mechanistic account rests on a hypothesis tested in advance; another, equally prominent, has not been shown to have been anticipated rather than reconstructed after the fact. Closing this gap is now the field’s central methodologically priority. Full article

The escalating accumulation of microplastics (MPs) in marine ecosystems presents a critical environmental crisis. However, current monitoring efforts rely heavily on labor-intensive, contamination-prone, and time-consuming laboratory analyses. While these conventional off-chip methods provide high accuracy, they inherently lack the throughput and autonomy required for continuous, real-time oceanic surveillance. To bridge this technological gap, microfluidic technologies (Lab-on-a-Chip) provide a viable route towards miniaturized, reagent-free in situ detection with reduced sample volumes and continuous operation capability. This review examines the transition from benchtop to field-deployable platforms and organizes the available microfluidic approaches for MP analysis into a structured overview. We examine on-chip sample manipulation and complementary separation techniques, such as acoustophoresis, dielectrophoresis, and optical tweezers, which are essential for isolating target particles from complex environmental matrices and overcoming intrinsic microfluidic challenges. Following sample preparation, we provide a comprehensive evaluation of state-of-the-art optical and spectroscopic identification methods optimized for continuous flow detection. Finally, we address current analytical limitations and discuss how the integration of machine learning with dynamic spectral libraries could enable autonomous, field-deployed monitoring networks for long-term MP surveillance. Full article

Aqueous two-phase systems (ATPSs) provide a versatile, fully aqueous platform for probing solute–water interactions and protein structure. This review first surveys the diversity and phase behavior of biphasic aqueous systems formed by polymers and salts. We describe how phase diagrams characterize ATPS formation and composition and how both polymer chemistry and salt identity, rather than molecular size alone, govern phase separation by modulating the solvent properties of water. Building on a modified binodal model, we show that phase separation and solute partitioning can be understood in terms of changes in aqueous solvent dipolarity/polarizability, hydrogen-bond donor/acceptor properties, hydrophobicity, and electrostatics, quantified via solvatochromic probes and homologous solute series. These measurements underpin solvent interaction analysis (SIA), in which the partition coefficients of small molecules and proteins across panels of ATPSs are used to generate “structural signatures” that sensitively report on amino acid substitutions, conformational changes, aggregation, ligand binding, osmolyte effects, and post-translational modifications, independent of protein size. We discuss how SIA can be implemented in vial-, plate-, and microfluidic formats and combined with diverse analytical readouts (HPLC, MS, colorimetric assays, and immunoassays), and we contrast this structure-focused approach with conventional concentration-only proteomic and biomarker strategies. Particular emphasis is placed on structure-based biomarker discovery, where disease-relevant shifts in proteoform distributions—especially glycosylation changes—are often more informative than bulk protein levels and where SIA can complement or simplify complex glycomics and top-down proteomics workflows. As a case study, we describe the recently FDA-approved IsoPSA assay, which applies SIA principles to prostate-specific antigen by measuring cancer-associated structural alterations in circulating PSA via its partition behavior in a proprietary ATPS. IsoPSA generates a single index that discriminates between high-grade prostate cancer and benign and low-grade conditions. Prospective, longitudinal, and MRI-integrated clinical studies demonstrate that IsoPSA improves pre-biopsy risk stratification, reduces unnecessary biopsies, and provides robust negative and positive predictive values within the PSA “gray zone.” Collectively, the data support aqueous solvent interaction analysis as a broadly applicable, mechanistically grounded technology for protein characterization, drug–protein interaction studies, and structure-centric biomarker development, exemplified by the clinical translation of IsoPSA. Full article

Open-surface coalescence of microdroplets is essential for droplet-based microreactors, emulsion processing, and multiphase microfluidics, yet existing methods often require closed channels or patterned electrodes. Here, we report a corona-driven open-oil-film platform that achieves voltage-regulated coalescence of aqueous microdroplets in a simple needle–plate electrode configuration. Positive corona discharge induces coupled electrohydrodynamic effects—including ion transport, interfacial charge redistribution, and Maxwell stresses—that drive oil-film contraction and charge-regulated droplet bouncing, thereby reducing inter-droplet spacing and promoting successive merging. The coalescence rate and final droplet size are tunable via the applied voltage and oil volume: complete coalescence into a single droplet is achieved at 12 kV, and an optimal oil volume of 60 μL maximizes confinement efficiency. To enable quantitative, frame-by-frame analysis, we develop an improved YOLOv5–OC-SORT framework that yields an overall mAP@0.5 of 0.905 for automatic droplet detection and tracking. As a proof-of-concept, the platform achieves electro-demulsification of a surfactant-stabilized water-in-oil emulsion, increasing the average droplet diameter from ~0.005 mm to ~0.2 mm and enabling effective oil–water separation. This work provides a simple, electrode-pattern-free strategy for controllable droplet coalescence and open-surface emulsion breaking. Full article

Conventional in vitro assays and many microphysiological systems struggle to generate time-dependent drug exposure profiles because medium replacement simultaneously removes or re-adds drugs in the culture compartment. Here, we developed an Open-access Dialysis Membrane-integrated Microfluidic Device (O-DMiMD) that uses molecular weight-dependent transport across a dialysis membrane to decouple nutrient supply from drug exposure control. The device comprises a cell culture compartment (CCC) and a donor compartment (DC) separated by a dialysis membrane. Transport functions were evaluated using Lucifer Yellow, FITC-dextran, and glucose, followed by drug-response studies using SN-38 and T-DM1 under different medium change conditions. Lucifer Yellow and glucose permeated through the dialysis membrane, whereas FITC-dextran was retained. DC medium change supplied glucose to the CCC and maintained A549/HepG2 co-culture proliferation comparably to direct CCC medium replacement. For SN-38, partial transport to the DC and retention in the CCC generated time-dependent exposure profiles; in A549/HepaRG co-culture, medium change conditions altered A549 viability. For T-DM1, conditions with or without re-addition to the CCC produced different SK-BR-3 responses, suggesting exposure-dependent effects for high-molecular-weight drugs. The O-DMiMD provides an open-access in vitro platform for evaluating drug responses under exposure profiles governed by molecular weights, protein binding, medium changes, and metabolic cell contexts. Full article

The coronavirus disease 2019 (COVID-19) pandemic highlighted the need for rapid, accurate, and point-of-care diagnostic technologies, accelerating interest in biosensors as next-generation analytical platforms. However, biosensor performance is governed by a connected sequence of processes, including bioprobe–target recognition, sensor fabrication, structural optimization, and signal interpretation. Because these processes involve multiple interacting variables, conventional empirical approaches often have limitations in efficiently optimizing biosensor performance and interpreting complex analytical signals. Artificial intelligence (AI) and machine learning (ML) provide tools to model these relationships and support prediction-guided biosensor development. This review discusses recent progress in AI-assisted biosensor development in three sequential stages. First, AI-assisted bioprobe design is reviewed, including in silico aptamer discovery, smart-SELEX-based aptamer screening, and peptide receptor design for improving molecular recognition. Second, AI-driven sensor fabrication and structural optimization are discussed, focusing on electrochemical feature extraction, paper-based microfluidic device optimization, and optical biosensor parameter prediction. Third, ML-based signal analysis is examined as a strategy for converting complex electrochemical, colorimetric, and optical responses into quantitative analytical outputs. By organizing these examples as a connected workflow rather than as separate applications, this review highlights how AI can link molecular design, device engineering, and signal interpretation to accelerate the development of next-generation biosensors. Full article

Circulating tumor cells (CTCs) are tumor cells that break away from the origin tumors and disseminate in the bloodstream and lymphatic circulation systems. CTCs originate from the original tumor with a similar bimolecular source. This makes CTCs play a vital status in cancer prognosis and diagnosis. However, CTC separation is highly challenging due to rarity and heterogeneity. In the present work, we designed a double-array petal flower-shaped microfluidic chip, a multifunctional capturing and isolation chip combining affinity and physical properties. The chip is composed of three arrays of microfluidic barriers organized one after the other. For the first array, six convex structures are set in each narrow channel. The first structure has a total of 12 such channels, which can increase collision frequency between cancer cells and convex structures in the channel. The second capture structure is one composed of an S-shaped array of concave triangle microcolumns and parabolic circular microcolumns. The advantage of this setting is that it can capture CTCs in the blood flowing into the first structure in 12 directions from multiple angles and multiple times, so as to improve capture efficiency. The third capture structure is composed of elliptical microposts and cylinders. The treated blood is captured for the last time. Because of the round or elliptical shape, it can retain the cell viability to a great extent, which is convenient for later pathological analysis of tumor cells. Simulation of velocity influence, pressure effects, streamline tendency, and shear rates is carried out for each structure. Therefore, theoretical validation has been illustrated to achieve high capture rate and purity. These delicate designs and numerical analysis clarify feasibility for further experiments of CTC enumeration, clinical analysis, and evaluation of cancer therapy. Full article

Tesla valves have emerged as promising passive flow-regulation devices for sustainable water systems because they provide directional flow control without moving parts, external energy input, or complex maintenance requirements. This review systematically examines the fundamental mechanisms, structural evolution, and engineering applications of Tesla valves in water-related systems. The underlying rectification behavior is analyzed from the perspectives of flow separation, recirculation, jet interaction, vortex evolution, and mechanism switching under varying hydraulic conditions. Recent advances in geometric optimization, multistage configurations, three-dimensional architectures, topology optimization, and data-driven design approaches are summarized to illustrate the transition from classical Tesla geometries to next-generation passive flow-control structures. Current applications in microfluidic systems, water-quality monitoring, thermo-hydraulic devices, pressure-regulation networks, and hydraulic safety enhancement are critically reviewed. The analysis indicates that Tesla-valve performance is governed by coupled interactions among geometry, flow regime, fluid properties, and operating conditions, while multifunctional designs increasingly integrate flow regulation, mixing enhancement, heat transfer, and pressure management. Finally, key challenges related to performance standardization, realistic operating conditions, manufacturability, and system-level integration are discussed. Tesla valves are expected to play an increasingly important role in intelligent and energy-efficient water infrastructure, supporting the development of next-generation sustainable water and fluid-management systems. Full article

Nanoemulsion (NEM) is an effective adjuvant and delivery system for vaccines and nucleic acids, capable of inducing immune responses against diverse pathogens. Background/Objectives: Conventional NEM manufacture uses multi-step operations, typically high-shear homogenization and then microfluidization (HSHM), thereby increasing process complexity and contamination risk. As water-rich colloidal dispersions, NEM is prone to microbial proliferation and droplet coalescence; freezing further disrupts microstructure, causing phase fusion and separation, so NEM adjuvants are often stored separately from antigens in multi-vial formats. Lyophilization could reduce cold-chain dependence and enable single-vial products, but there is no systematic study on lyoprotectants comparation and process optimization of lyophilized NEM. Methods: An impingement jet mixing (IJM) process was evaluated as a simplified, scalable route for NEM production. Key IJM parameters, including flow ratio, total flow rate, preparation temperature, microchannel type, and shear mode—were examined to match attributes of conventional HSHM. Lyophilized and reconstituted NEM were characterized by dynamic light scattering, scanning electron microscopy, transmission electron microscopy, differential scanning calorimetry and/or in vitro potency to inform lyoprotectant selection, and Taguchi Design of Experiment (DOE) methodology guided lyophilization processes. Results: IJM yielded NEM with droplet size, polydispersity index (PDI) and morphology comparable to HSHM, with higher throughput and fewer unit operations. Optimized lyophilization technique with designed lyoprotectant and process formed closed structures to prevent the easy-to-flow monolayer of the emulsion from fusing, producing robust and stable NEM. Conclusions: Coupling IJM with targeted lyophilization establishes a scalable, lower-risk manufacturing paradigm for NEM that preserves critical quality attributes, reduces cold-chain reliance and enables single-vial adjuvanted vaccine formats with tangible industrial and clinical benefits. Full article

Sensors and biosensors designed for biomarker detection in biological samples often suffer from performance loss caused by surface fouling, interface blocking, and matrix interference. Although these effects are frequently discussed separately, in real sensing systems they are strongly interconnected and they determine analytical reliability, especially in body fluids like serum, plasma, whole blood, sweat, and other complex media. This review provides a practical and mechanism-oriented overview of how these processes originate, how they differ, and how they ultimately lead to signal drift, reduced sensitivity, false-positive responses, and shortened sensor lifetime. We first discuss the molecular origins of interface failure, including protein adsorption, conditioning film formation, nonspecific binding, ionic strength effects, pH fluctuations, viscosity-related diffusion changes, and electroactive interferents. The impact of these phenomena is then compared across major sensing platforms, including electrochemical, potentiometric, optical, capacitive sensors, field-effect transistors and wearable biosensors. A central part of this review focuses on practical prevention strategies already employed in real biomarker sensing platforms. These include hydration-driven antifouling coatings, zwitterionic and hydrogel interfaces, post-immobilization blocking with bovine serum albumin, mercaptohexanol and ethanolamine, ionophore and membrane engineering in ion-selective electrodes, hydrophobic solid-contact layers for water-layer suppression, regeneration workflows, membrane and microfluidic pre-treatment, and AI-assisted drift correction. By combining advances in materials engineering, surface chemistry, sample handling, and algorithmic correction, this review highlights strategies to improve sensor stability in complex biological fluids. Overall, it offers a practical guide for developing next-generation low-fouling, drift-resistant, and self-correcting sensing systems for reliable biomarker analysis at the point of care. Full article