ReviewSensors (Basel, Switzerland)2024
Advances in Nanomaterials and Colorimetric Detection of Arsenic in Water: Review and Future Perspectives.
Review in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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.
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.
Who cites it
5 citing papers in PubMed.
- Electroanalytical overview: recent advances in the sensing of arsenic using screen-printed electrochemical platforms.Environmental science. Nano · 2026Review
- Fluorescent Chemosensors in the Detection of Ultra-trace Quantity of Toxic HgJournal of fluorescence · 2025Review
- Review
- Heavy Metal-Gut Microbiota Interactions: Probiotics Modulation and Biosensors Detection.Biosensors · 2025Review
- Exploring the frontiers of emerging sensing of silver nanoprisms: recent progress and challenges.RSC advances · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Arsenic, existing in various chemical forms such as arsenate (As(V)) and arsenite (As(III)), demands serious attention in water and environmental contexts due to its significant health risks. It is classified as "carcinogenic to humans" by the International Agency for Research on Cancer (IARC) and is listed by the World Health Organization (WHO) as one of the top 10 chemicals posing major public health concerns. This widespread contamination results in millions of people globally being exposed to dangerous levels of arsenic, making it a top priority for the WHO. Chronic arsenic toxicity, known as arsenicosis, presents with specific skin lesions like pigmentation and keratosis, along with systemic manifestations including chronic lung diseases, liver issues, vascular problems, hypertension, diabetes mellitus, and cancer, often leading to fatal outcomes. Therefore, it is crucial to explore novel, cost-effective, and reliable methods with rapid response and improved sensitivities (detection limits). Most of the traditional detection techniques often face limitations in terms of complexity, cost, and the need for sophisticated equipment requiring skilled analysts and procedures, which thereby impedes their practical use, particularly in resource-constrained settings. Colorimetric methods leverage colour changes which are observable and quantifiable using simple instrumentation or even visual inspection. This review explores the colorimetric techniques designed to detect arsenite and arsenate in water. It covers recent developments in colorimetric techniques, and advancements in the role of nanomaterials in colorimetric arsenic detection, followed by discussion on current challenges and future prospects. The review emphasizes efforts to improve sensitivity, selectivity, cost, and portability, as well as the role of advanced materials/nanomaterials to boost the performance of colorimetric assays/sensors towards combatting this pervasive global health concern.
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What OpenQuestion holds
Registered trials
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.