ReviewACS omega2025
Static Mixers for High-Viscosity Systems: From Classical Helices to Machine-Learning-Optimized Geometries.
Review in ACS omega, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Bio-Inspired Flexible-Wall Squeezing Mixer with ALE-CFD-Based Actuation Optimization and Fluorescence-Imaging Assessment of Outlet Mixing Uniformity.Biomimetics (Basel, Switzerland) · 2026Article
- Design and Fabrication of Biomimetic Gradient Bone Tissue Engineering Scaffolds: Evolution from Single-Gradient to Multi-Gradient.Gels (Basel, Switzerland) · 2026Review
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
Static mixers are widely used in chemical, polymer, and process industries to handle high-viscosity media, such as polymer melts, yield-stress resins, and particle-laden suspensions, where conventional mixing is inefficient. By inducing chaotic advection through fixed internal geometries, they offer compact, low-maintenance solutions compatible with continuous production. This review charts the progression from classical helical ribbons to advanced, application-specific elements optimized by using computational fluid dynamics (CFD) and machine learning (ML). Design strategies for viscoelastic and yield-stress fluids are critically assessed, with attention to balancing mixing intensity against hydraulic losses. Remaining challenges include the scale-up of complex ML-derived designs, mitigation of viscoelastic instabilities, integration of multifunctional features, and further gains in energy efficiency. Advances in these areas will enhance the role of static mixers in sustainable, high-performance industrial processes.
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.