Evidence map›Paper›PMID 40893251›Full record

ArticleACS omega2025

Regression Modeling of Oxygen-Functionalized Single-Walled Carbon Nanotubes in Aqueous Dispersions.

Hoa Le, Amos Abioye, Hai V Nguyen, Adeboye Adejare

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. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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.

Hoa LePhiladelphia College of Pharmacy, Department of Pharmaceutical Sciences, Saint Joseph's University, 600 S. 43rd St, Philadelphia, Pennsylvania 19104, United States.ORCID https://orcid.org/0009-0005-6219-3228
Amos AbioyeCollege of Pharmacy and Health Sciences, School of Pharmacy, Belmont University, 1900 Belmont Blvd., Nashville, Tennessee 37212, United States.
Hai V NguyenFaculty of Pharmaceutical Chemistry and Technology, Hanoi University of Pharmacy, 13-15 Le Thanh Tong St., Cua Nam Ward, Hanoi 11021, Vietnam.
Adeboye AdejarePhiladelphia College of Pharmacy, Department of Pharmaceutical Sciences, Saint Joseph's University, 600 S. 43rd St, Philadelphia, Pennsylvania 19104, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Due to high drug loading and effective cell permeability, single-walled carbon nanotubes (SWCNTs) are promising nanocarriers for drug delivery systems. However, SWCNTs tend to aggregate in dispersion media because of their high aspect ratio and intrinsic hydrophobicity. Thus, the dispersion and stability of SWCNTs in water strongly depend on the dispersion method and CNT surface characteristics. Finding the optimal processing parameters and techniques for SWCNT dispersion remains challenging. This study proposes a surface modification technique (oxidation) and ultrasonication methodology to achieve well-dispersed and stable SWCNT dispersions in water. A D-optimal design was utilized to construct the experiment and investigate the effects of oxidation time and sonication conditions on hydrodynamic particle sizes (HDSs), polydispersity indices (PDIs), zeta potentials (ZPs), and Raman peak area ratios (RATs). The dynamic light scattering method was utilized to determine HDS, PDI, and ZP. Raman spectroscopy was employed to assess impurities and structural defects within SWCNTs by analyzing the Raman peak area ratio. FTIR spectroscopy and thermal gravimetric analysis were used to examine oxygenated groups on CNT surfaces. The morphology and elemental compositions of SWCNTs were determined using scanning electron microscopy combined with energy-dispersive X-ray analysis spectroscopy. The contour profiler was used to determine the optimal design space that produced dispersions with HDS, PDI, and ZP values below 250 nm, 0.350, -30 mV, respectively, and minimal defects in SWCNTs. The optimal dispersion conditions were identified using the desirability function. Overall, the models that described the relationship between the input and output factors were validated, enabling accurate prediction of responses and the optimization of input variables.

Identifiers

PMID40893251
PMCPMC12391941

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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.