Evidence map›Paper›PMID 39222210›Full record

ArticleJournal of fluorescence2025

Enhancing Photoelectric Response of Self-powered UV and Visible Detectors Using CuO/ZnO NRs Heterojunctions.

Ghaith A Hasach, Husam S Al-Salman

Abstract read
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In one paragraph

Article in Journal of fluorescence, 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. Article
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

2 authors.

Ghaith A HasachDepartment of Physics, College of Science, Basrah University, Basrah, Iraq. Ghaith.hasach@uobasrah.edu.iq.
Husam S Al-SalmanDepartment of Physics, College of Science, Basrah University, Basrah, Iraq.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding the development and performance of UV photodetectors is crucial, given their extensive applications in both military and civilian sectors. The evolution of self-powered photodetectors, especially those based on heterojunction nanostructures, has demonstrated significant potential for enhancing both device efficiency and functionality. By exploring the effects of material composition and structural design, can optimize these devices for improved photoelectric response and energy efficiency. In this study, we prepared the CuO/ZnO NRs heterojunction photodetector on an ITO substrate to enhance photoelectric response of UV detectors. The fabrication process utilized the hydrothermal method and the spin coating technique. The effect of CuO concentration on the optical response of the photodetector under UV radiation at wavelengths of 405 nm and 385 nm was investigated. The samples were characterized using FESSEM, XRD, EDX, and UV-Vis spectra. The device is further distinguished by its standard I-V curves and photocurrent-time curves, which demonstrate the device's behavior under various light conditions. The prepared thin films are polycrystalline, with CuO layers displaying monoclinic phases and ZnO layers exhibiting a hexagonal wurtzite phase. All samples have the potential to exhibit photovoltaic properties and self-powered capabilities. Furthermore, the I-V curve confirms that the photocurrent mechanism of these junctions adheres to the recombination standard, in addition to demonstrating correction behavior. A sample with a CuO concentration of 0.1 M shows the highest photosensitivity, reaching 340,700%, and a photocurrent gain (Iph/Idark) of 3,408 when exposed to light irradiation at 405 nm. Additionally, it exhibits a rapid response time of 0.8 s.

Indexed as

CuOHeterojunctionHydrothermal methodSelf-powered deviceSpin coating techniqueZnO NRs

Identifiers

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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.