Sulfur Quantum Dots Emitting Blue–Violet Chemiluminescence, Photoluminescence, and Near-Infrared Electrochemiluminescence
Bibliographic record
Abstract
Currently, there remains a lack of single nanomaterials capable of emitting across the spectrum from the ultraviolet to near-infrared. The development of such broadband-emissive nanomaterials would greatly advance a variety of fields, including biosensing, display technologies, and anticounterfeiting applications. Sulfur quantum dots (SQDs), emerging as luminescent materials, harbor significant potential for their diverse applications at low costs. In this work, light emissions from our synthesized SQDs, photoluminescence (PL), chemiluminescence (CL), and electrochemiluminescence (ECL) in the aqueous phase, were tuned across a wavelength range from 350 to 1050 nm by altering the excitation sources and reaction enthalpy. Both PL and CL display a similar emission peak around 420 nm, with 35% of the photons falling into the ultraviolet region. These may be attributed to emissions from the SQD core states. In the ECL process, a significant red shift in its emission peak at 690 nm was observed, with the emission range extending up to 1050 nm. This shift implies that the radiative relaxation center has switched to the surface states, underscoring the ECL process’ pronounced preference for surface states or low-energy band gaps in semiconductor nanoparticles. Such phenomena were further confirmed through the absolute PL quantum yield, CL and ECL quantum efficiency determinations, and reaction enthalpy calculations. The photoluminescence quantum yield of SQDs was determined to be 70.3% ± 4.4%, while absolute quantum efficiencies of CL and ECL were measured to be 1.1% ± 0.14% and 0.00072% ± 0.00005%, respectively. Notably, the CL quantum efficiency of SQDs is 110 times higher than that of nitrogen-doped carbon quantum dots of the equivalent size, while the ECL efficiency is one of the strongest among many semiconductor QDs, which shows great potential for applications in medical diagnosis, biological sensing, and other promising fields. This research offers valuable insights into devising the design of future quantum dots.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".