ArticleMicromachines2026
Microfluidic Chip for High-Throughput Microstructure Detection of Precursor Particles.
Article in Micromachines, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
The microstructure of ternary precursors significantly influences the electrochemical performance of ternary cathode materials and, consequently, the overall performance of lithium-ion batteries. In industrial production utilizing traditional co-precipitation methods, Scanning Electron Microscopy (SEM) is typically employed for particle detection. However, this approach is limited by offline sampling lag, poor representativeness, cumbersome sample preparation, and low efficiency, failing to achieve real-time quality feedback on production lines. To enable high-throughput particle detection, this study proposes a multi-layer PDMS chip designed for three-dimensional (3D) hydrodynamic focusing. The sheath fluid compressed the sample flow in horizontal and vertical directions, respectively, to form a flat ribbon flow passing through the detection area. High-fidelity raw images are captured for automated particle microstructure analysis. Firstly, a chemical pretreatment protocol was optimized to ensure stable precursor solution transport. Secondly, a three-layer composite microchannel featuring a sequential horizontal and vertical sheath-flow compression mechanism was designed, with its geometry optimized via Computational Fluid Dynamics (CFD) simulations. Subsequently, experimental optimizations of flow rate ratios were performed using sodium fluorescein, followed by validation with ternary precursor solutions. The results indicate that the microchannel achieves flattened monolayer focusing of randomly distributed precursor particles, compressing the sample stream height to approximately 15.44 μm, thereby maintaining the particle stream within the microscope's depth of field and analyzing particle microstructure efficiently based on a microscopic image. Moreover, it is confirmed that the focused stream dimensions are primarily governed by the flow rate ratio, allowing for a flexible increase in detection throughput by adjusting the total flow rate. Different from static offline particle analyzers, this platform captures dynamic particle morphology under continuous flow, providing real-time data to guide co-precipitation reaction adjustment.
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.