ReviewInternational journal of pharmaceutics: X2025
Advanced microfluidic techniques for the preparation of solid lipid nanoparticles: Innovations and biomedical applications.
Review in International journal of pharmaceutics: X, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Solid Lipid Nanoparticles Based Dry Powder Inhalers for Enhanced Drug Delivery Against Lung Diseases.AAPS PharmSciTech · 2026Review
- Integration of Artificial Intelligence and Microfluidics for Drug Delivery Applications.Micromachines · 2026Review
- Lipid-Based Delivery Systems for Therapeutic Glycoproteins: Current Advances, Challenges, and Future Perspectives.Pharmaceutics · 2026Review
- Smart nanoparticle vaccines integrate nanotechnology artificial intelligence and immunoengineering for precision immunization.Discover nano · 2026Review
- Engineering smart polymeric lipid nanoparticles for breast cancer: AI-guided formulation design, biological barriers, and translational constraints.Journal of nanobiotechnology · 2026Review
- Microfluidic-Driven Assembly of RNA Nanocomplexes: Design, Process Control and Translational Perspectives in Oncology.Pharmaceutics · 2026Review
- Polymer Nanoparticles in Medical Applications-Future Directions.Nanomaterials (Basel, Switzerland) · 2026Review
- Synergy between Antimicrobial Peptides and Lipid Nanoparticles for Skin Infection Control.ACS applied materials & interfaces · 2026Review
- PLGA-Based Co-Delivery Nanoformulations: Overview, Strategies, and Recent Advances.Pharmaceutics · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Solid lipid nanoparticles (SLNs) represent a promising category of nanocarriers used in medicine and cosmetics, offering enhanced drug protection, controlled release, and targeted delivery for both hydrophilic and lipophilic compounds. Conventional preparation methods, such as high-pressure homogenization and solvent emulsification-evaporation, face several challenges, including increased polydispersity, scaling limitations, and the presence of hazardous residual solvents. Microfluidic technology has emerged as a novel approach for preparing SLNs, addressing issues such as variable particle sizes and residual solvents by facilitating enhanced control over particle dimensions, morphology, and encapsulation efficiency. Microfluidics enables rapid and uniform mixing through micro-scale fluid dynamics, resulting in the production of homogeneous nanoparticles with adjustable characteristics. The review examines key parameters in microfluidic SLN preparation and categorizes various microfluidic chip designs and mixing techniques in detail, illustrating their unique advantages in controlling nanoparticle properties. Furthermore, this article provides a comprehensive overview of microfluidic SLN preparation, emphasizing its advantages over conventional methods, and explores the transformative potential of SLNs for advancing drug delivery systems, cosmetic formulations, and diagnostics. The integration of artificial intelligence (AI) and machine learning to optimize synthesis conditions and enhance reproducibility and scalability for industrial translation are also discussed.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.