Liquid droplets paving the way for
highly sensitive sensors
Jiangtao Cheng
Associate
Professor
Research
Focus:
Optofluidic so-
lar concentra-
tors based on
electrowetting
on dielectric-
driven liquid
prisms
Sensitive, miniaturized sens-
ing systems are important
for medical and environmen-
tal diagnostic and monitor-
ing applications. Chip scale
integrated photonic sens-
ing systems that combine
optical, electrical, and
fluidic functions are espe-
cially attractive for sensing
applications due to the high
sensitivity of optical sensors,
the small form-factor of chip
scale systems, and the low processing cost.
While optical sensing with a detection limit down to single nanoparticles has been achieved
by various methods, microcavity sensing attracts much attention because their high quality
factors (Q factor) and small mode volumes enable significant enhancement of light-matter
interactions. Microcavity sensing has seen tremendous progress and the sensing performance
has been demonstrated by detecting single biological molecules. However, detection in liquids
with whispering gallery mode (WGM, i.e., closed circular beams supported by total internal
reflections at the external cavity interface) cavities was achieved only in rare cases. No real and
stable high Q-factor sensing experiment with non-solid optical resonators has been reported
to date.
Creating
intelligent
tires for safer
automobiles
The concept of intelligent tires was introduced more than a
decade ago. Various tire companies invested in developing
this technology internally and a few have come to market
with a preliminary product.
The idea is to measure, in real-time, the tire vibration
characteristics and make indication of various important
parameters such as road surface type and condition, fric-
tion potential, wear, hydroplaning, health monitoring, and
load. Knowledge about any of the above parameters could
potentially help with improving safety of the transportation
system, specially with autonomous vehicles. At CenTiRe,
research on this topic started 8 years ago and continues
towards realization of the
concept into a viable prod-
uct.
With funding from NSF and
several tire companies, we
have been able to put the
intelligence in the tire. One
such example is a real-time
machine learning algorithm
which can predict the road
surface type and its state
(wet, dry, snow, or ice). Cur-
rent efforts are focused on
estimating friction between
the tire and the road and
feeding that information into
the advanced chassis control
systems on-board the vehicle
(ABS, VSC, ADAS, etc.)
12 Revised and Corrected, Nov. 2019
Saied Taheri
Professor
Research
Focus:
Tire and ve-
hicle dynamic
modeling and
simulation;
Intelligent tire
development
and application;
Vehicle chassis
systems control