ReviewTurkish journal of biology = Turk biyoloji dergisi2025
Green carbon dots in the era of AI: sustainable synthesis, intelligent drug delivery, advanced diagnostics, and bioimaging.
Review in Turkish journal of biology = Turk biyoloji dergisi, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Advances in green-synthesized quantum dot-based nanoplatforms for cancer treatment, photodynamic therapy, photothermal therapy and cancer theranostics.RSC advances · 2026Review
- Plant-mediated green nanoparticles: combining nanometal and biometabolite potential for Alzheimer's treatment.Biomedical engineering online · 2026Review
- Carbon Dots in Nanomedicine: Advanced Fabrication, Biomedical Applications, and Future Clinical Perspectives.Pharmaceutics · 2026Review
- Stimuli-Responsive Carbon Nanotubes for On-Demand Cancer Therapy: A Review.AAPS PharmSciTech · 2026Review
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
No grant is acknowledged in the PubMed record.
Abstract
Background/aim: Green carbon dots (GCDs) are a rapidly developing class of nanomaterials that are revolutionizing various scientific disciplines due to their unique optical properties, low toxicity, and sustainable synthesis. This review offers a comprehensive roadmap for the field, emphasizing the synergy between GCDs and artificial intelligence (AI). Materials and methods: We begin by detailing the sustainable synthesis of GCDs, highlighting green chemistry principles and the transformative role of AI in optimizing their production. Subsequently, we explore the critical characterization of GCDs, including their structural, optical, and biocompatibility assessment. The core of this study explores the diverse biomedical applications of GCDs, including their integration into intelligent drug delivery systems enhanced by AI, utility in advanced diagnostics and biosensing, and contribution to state-of-the-art bioimaging techniques by deep learning (DL). Results: Analysis of the literature confirms that AI-driven optimization is crucial for enhancing the scalability and reproducibility of GCD production. Furthermore, the integration of DL models significantly boosts the analytical precision and real-time capabilities of these platforms, validating the profound convergence of the fields. Conclusion: This review provides a holistic roadmap, concluding that the AI- GCD synergy is indispensable for developing the next generation of smart nanomedicines. Future efforts must prioritize addressing scalability, standardization, and regulatory pathways to accelerate successful clinical translation.
Indexed as
Identifiers
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.