Evidence map›Paper›PMID 42095554›Full record

ArticleLangmuir : the ACS journal of surfaces and colloids2026

Upper Bounds on the Colloid Separation Efficiency of Diffusiophoresis.

Fernando Temprano-Coleto, Jeongmin Kim, Marcel M Louis, Howard A Stone

Abstract read
In one paragraph

Article in Langmuir : the ACS journal of surfaces and colloids, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Fernando Temprano-ColetoDepartment of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey 08544, United States.ORCID 0000-0002-2179-3148
Jeongmin KimDepartment of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey 08544, United States.
Marcel M LouisDepartment of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey 08544, United States.
Howard A StoneDepartment of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey 08544, United States.ORCID 0000-0002-9670-0639

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The separation of colloidal particles from fluids is essential to ensure a safe global supply of drinking water, yet in the case of microscopic particles, it remains a highly energy-intensive process when using traditional filtration methods. Water cleaning through diffusiophoresis, spontaneous colloid migration in chemical gradients, effectively circumvents the need for physical filters, representing a promising alternative. This separation process is typically realized in internal flows, where a cross-channel electrolyte gradient drives particle accumulation at walls, with colloid separation slowly increasing in the streamwise direction. However, the maximum separation efficiency, achieved sufficiently downstream as diffusiophoretic migration (driving particle accumulation) is balanced by Brownian motion (inducing diffusive spreading), has not yet been characterized. In this work, we develop an asymptotic theory to predict colloid separation in this limit, deriving expressions for the water recovery, defined as the fraction of clean water that can be obtained from the suspension. We find that the mechanism by which the chemical permeates in the channel and the reaction kinetics governing its dissociation into ions play key roles in the process. Moreover, we identify four distinct regimes in which separation is controlled by different scaling laws involving Damköhler and Péclet numbers, which measure the ratios of reaction kinetics to ion diffusion and diffusiophoresis to Brownian motion, respectively. We also confirm the scaling of one of these regimes using microfluidic experiments where separation is driven by CO

Identifiers

PMID42095554
PMCPMC13192318

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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.