Evidence map›Paper›PMID 40893329›Full record

ArticleACS omega2025

Organosilanized Hydrophobic Sand for Drought Resilience: Reducing Water Percolation and Enhancing Crop Growth Conditions.

Yashwanth Arcot, Ramya Srinivas, Minchen Mu, Mahshad Maghoumi, Luis Cisneros-Zevallos, Mustafa E S Akbulut

Abstract read
In one paragraph

Article in ACS omega, 2025. 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
–field-weighted citation impact
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

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.

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

6 authors.

Yashwanth ArcotArtie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843, United States.ORCID https://orcid.org/0000-0002-3955-0427
Ramya SrinivasArtie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843, United States.
Minchen MuArtie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843, United States.ORCID https://orcid.org/0000-0001-7831-362X
Mahshad MaghoumiDepartment of Horticultural Sciences, Texas A&M University, College Station, Texas 77843, United States.
Luis Cisneros-ZevallosDepartment of Horticultural Sciences, Texas A&M University, College Station, Texas 77843, United States.ORCID https://orcid.org/0000-0003-0580-5943
Mustafa E S AkbulutArtie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843, United States.ORCID https://orcid.org/0000-0001-7343-2187

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recently, increasing frequency and severity of drought events have resulted in significant crop yield reductions worldwide, indicating the critical need for innovative agricultural water management strategies to enhance water use efficiency. Addressing this challenge, we present a novel approach involving the strategic placement of highly hydrophobic sand layers below the subrhizosphere. This method utilizes silica sand modified via a facile, single-step surface treatment, yielding a material with strong hydrophobicity, characterized by a static water contact angle of 133.0 ± 1.0°. Importantly, the modified sand demonstrated stability and retained its hydrophobic properties under simulated adverse agricultural conditions. Systematic investigations of the hydraulic properties revealed that the incorporation of these hydrophobic sand layers substantially controlled the vertical infiltration flux of irrigation water. Specifically, a hydrophobic sand layer with an areal density of 796.5 mg/cm

Identifiers

PMID40893329
PMCPMC12392176

What OpenQuestion holds

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Registered trials

None linked

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.