posted on 2024-01-05, 23:03authored byAaron Döring, Yuqing Qiu, Andrey L. Rogach
Optical sensing methods offer a convenient
noncontact
approach
to monitor different environmental parameters with a high spatial
resolution and fast response times. Temperature monitoring can benefit
from optical sensing using luminescent nanoprobes, but many of those
substances are toxic or expensive. Carbon dots are a class of luminescent
colloidal nanoparticles that have recently gained recognition as optical
probes, which are easy to produce by environmentally friendly synthesis,
nontoxic, and stable. While carbon dots show temperature-dependent
optical properties, their broad emission profiles may constitute a
challenge for optical sensing. In this study, three types of carbon
dots with different emission profiles were tested as optical probes
for intensity-, spectral-shift-, intensity-ratio-, bandwidth-, and
lifetime-based temperature sensing. Depending on the optical characteristics
of the specific probe, either intensity- or lifetime-based sensing
was shown to be the most accurate, with accuracies of up to 1.65 and
0.70 K, respectively. Employing Gaussian fits improved accuracies
of the intensity-ratio-based sensing to 1.24 K, with the additional
benefit of greater stability against excitation fluctuations. Finally,
a multiple linear regression model combining steady-state and time-resolved
luminescence data of carbon dots has been applied to further increase
the sensing accuracies with carbon dots to 0.54 K. Our study demonstrates
how multidimensional machine learning methods can greatly improve
temperature sensing with optical probes.