ArticleJournal of chemical information and modeling2022
Machine Learning-Assisted Carbon Dot Synthesis: Prediction of Emission Color and Wavelength.
Article in Journal of chemical information and modeling, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- Carbon dots derived from natural medicines: Therapeutic advances and challenges.Journal of pharmaceutical analysis · 2026Review
- Green Synthesis of Fluorescent Carbon Dots and AI-Driven New Paradigms: A Comprehensive Review.Biosensors · 2026Review
- A critical review on antibacterial mechanisms of carbon dots: a comprehensive approach to combating multidrug resistance.Archives of microbiology · 2026Review
- Machine-Learning-Assisted Carbon Dots: From Algorithms to Applications and Beyond.Molecules (Basel, Switzerland) · 2026Review
- A review of applications of machine learning in quantum dots research.Discover nano · 2026Review
- Predicting Carbon Dot Photoluminescence: A Comparative Machine Learning Study on Systematic Synthesis Data.ACS omega · 2026Article
- Carbon dots as photosensitizers: unraveling their ultrafast charge transfer, challenges, and future prospects.Nanoscale advances · 2026Review
- Reproducibility roadblocks and standardization in carbon dot synthesis: a critical review of current practices, challenges, and future directions.Mikrochimica acta · 2025Review
- Carbon Dot Nanoparticles Synthesized from Horticultural Extracts for Postharvest Shelf-Life Extension of Fruits and Vegetables.Plants (Basel, Switzerland) · 2025Review
- Characterizing DNA Origami Nanostructures in TEM Images Using Convolutional Neural Networks.Journal of chemical information and modeling · 2025Article
- Predicting Fluorescence Emission Wavelengths and Quantum Yields via Machine Learning.Journal of chemical information and modeling · 2025Article
- Structure and Zeta Potential of Gold Nanoparticles with Coronas of Varying Size and Composition.The journal of physical chemistry. C, Nanomaterials and interfaces · 2025Article
- Computational Modeling of Properties of Quantum Dots and Nanostructures: From First Principles to Artificial Intelligence (A Review).Nanomaterials (Basel, Switzerland) · 2025Review
- Green carbon dots in the era of AI: sustainable synthesis, intelligent drug delivery, advanced diagnostics, and bioimaging.Turkish journal of biology = Turk biyoloji dergisi · 2025Review
- Machine learning-guided realization of full-color high-quantum-yield carbon quantum dots.Nature communications · 2024Article
- Machine Learning Prediction of Quantum Yields and Wavelengths of Aggregation-Induced Emission Molecules.Materials (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Carbon dots (CDs) have attracted great attention in a range of applications due to their bright photoluminescence, high photostability, and good biocompatibility. However, it is challenging to design CDs with specific emission properties because the syntheses involve many parameters, and it is not clear how each parameter influences the CD properties. To help bridge this gap, machine learning, specifically an artificial neural network, is employed in this work to characterize the impact of synthesis parameters on and make predictions for the emission color and wavelength for CDs. The machine reveals that the choice of reaction method, purification method, and solvent relate more closely to CD emission characteristics than the reaction temperature or time, which are frequently tuned in experiments. After considering multiple models, the best performing machine learning classification model achieved an accuracy of 94% in predicting relative to actual color. In addition, hybrid (two-stage) models incorporating both color classification and an artificial neural network
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.