Evidence map›Paper›PMID 40199953›Full record

ArticleScientific reports2025

Elevated temperature and pressure performance of water based drilling mud with green synthesized zinc oxide nanoparticles and biodegradable polymer.

Milad Khashay, Mohammad Zirak, James J Sheng, Tarek Ganat, Ehsan Esmaeilnezhad

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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

5 authors.

Milad KhashayDepartment of Petroleum Engineering, Hakim Sabzevari University, Sabzevar, Iran.
Mohammad ZirakDepartment of Physics, Hakim Sabzevari University, Sabzevar, Iran.
James J ShengBob L. Herd Department of Petroleum Engineering, Texas Tech University, Lubbock, TX, 79409, USA.
Tarek GanatPetroleum and Chemical Engineering Department, Sultan Qaboos University, 123, Al Khodh, Sultanate of Oman. t.ganat@squ.edu.om.
Ehsan EsmaeilnezhadDepartment of Petroleum Engineering, Hakim Sabzevari University, Sabzevar, Iran. e.esmailnezhad@hsu.ac.ir.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Water-based mud (WBM) faces challenges in high-temperature, high-pressure (HTHP) conditions due to fluid loss and property degradation. Enhancing eco-friendly drilling fluids with optimal rheology is crucial for sustainable, cost-effective, and environmentally safe drilling operations. This study formulated a WBM using green-synthesized zinc oxide (ZnO) nanoparticles (NPs, ~ 45 nm) and tragacanth gum (TG), a biodegradable natural polymer. The synthesized ZnO NPs were comprehensively characterized using energy-dispersive X-ray spectroscopy (EDS), field-emission scanning electron microscopy (FE-SEM), Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), and thermogravimetric analysis (TGA/DTG) to determine their structural, morphological, and chemical properties. Rheological properties, including flow behavior index (n), consistency index (K), plastic viscosity (PV), and yield point (YP), were analyzed at 25, 50, and 75 °C using the Bingham-plastic and Power-law models. The accuracy of the model was validated using Analysis of Variance (ANOVA), which assessed the significance of the results. Additionally, Design Expert software was utilized to optimize the concentrations of TG and ZnO for elevated temperature applications. Moreover, the response surface methodology (RSM) results were evaluated by reporting the R

Indexed as

Environmentally-friendly WBMsGreen-synthesized ZnO NPsHTHP filtrationNatural biodegradable TG

Identifiers

PMID40199953
PMCPMC11978892

What OpenQuestion holds

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Registered trials

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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.