Evidence map›Paper›PMID 41483226›Full record

ArticleMikrochimica acta2026

Synthesis of biomass nitrogen self-doped carbon quantum dots and their application in multicolor imaging and temperature sensing in cells.

Yue-Fang Hu, Yu-Xin Chen, Ting Chen, Jin-Fang Li, Sheng-Yu Chen

Abstract read
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In one paragraph

Article in Mikrochimica acta, 2026. 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

5 authors.

Yue-Fang Hu *Guangxi Key Laboratory of Calcium Carbonate Resources Comprehensive Utilization, College of Materials and Chemical Engineering, Hezhou University, Hezhou, 542899, China.
Yu-Xin Chen *Hezhou Hospital of Traditional Chinese Medicine, Hezhou, 542899, China.
Ting ChenHezhou Hospital of Traditional Chinese Medicine, Hezhou, 542899, China.
Jin-Fang LiGuangxi Key Laboratory of Calcium Carbonate Resources Comprehensive Utilization, College of Materials and Chemical Engineering, Hezhou University, Hezhou, 542899, China.
Sheng-Yu ChenGuangxi Key Laboratory of Urban Water Environment, Baise University, Baise, 533000, China. 376163159@qq.com.

Funding

the National Natural Science Foundations of China Grant No. 22464001
6 · The paper itself

Abstract

Biomass-derived nitrogen self-doped carbon quantum dots (N-CQDs) were synthesized by the hydrothermal method using soybean residue as both nitrogen and carbon sources. The N-CQDs were applied to the study of cell multicolor imaging and temperature sensing in living cells. The experimental results show that N-CQDs have strong fluorescence intensity, good biocompatibility, good cell membrane penetration, good cell imaging performance and strong temperature sensitivity. In the temperature range 25 ~ 60℃, the intensity of the N-CQDs decreased with increase of temperature, and showed a good linear relationship. At 60℃, the relative fluorescence intensity was quenched by 65%. The results show that the soybean residue N-CQDs have high application potential and value in the field of fluorescence temperature sensing in living cells, and are of great significance for the development of nondestructive cell measurement nanothermometers. It is expected that the N-CQDs may establish as a temperature-sensitive fluorescence nanoprobe widely used in biomedical fields such as cell research.

Indexed as

CarbonFluorescent DyesNitrogenOptical ImagingQuantum DotsTemperatureBiomassGlycine maxHeLa CellsHumansCarbonFluorescent DyesNitrogenBiomass-derived nitrogen self-doped carbon quantum dotsCell temperature sensingCellular imagingFluorescence quenchingHydrothermal methodPhotoelectric property

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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.