Motivation

Coal tar, which is typically considered a low-value byproduct of coal distillation, is often disposed of through burning or used in paving tar-and-chip driveways¹. These mainstream coal tar utilization methods are inefficient and environmentally unfriendly. The objective of this study is to explore a strategy for upgrading coal tar and large-scale application scenarios.

Underutilized and undervalued coal tar contains rich chemical compounds mainly comprising aromatic hydrocarbons, making it possible to customize the tar functionality through laser annealing^2–4. It is reasonable to upgrade certain road components, namely the coal tar, into functional devices without introducing sensors with higher economic and environmental costs, thereby making the large-scale application of coal tar-based sensors possible.

Results

The entire waste-to-device strategy can be summarized into six steps, including substrate formation, tar injection, oxidation, laser annealing, electrode integration, and encapsulation (Fig. 1a). In this process, the conductivity of laser-annealed coal tar (LACT) reached 328.99 S/m, and LACT was packaged as a resistance strain sensor. The LACT-based sensors exhibit a strain capacity of more than 5% under both tension in the length direction and compression in the thickness direction, while the sensitivity varies greatly (Fig. 1b).

Furthermore, we designed a supporting system including data acquisition, processing, and wireless communication functions to enable the application of the sensors in traffic and bridge vibration monitoring (Fig. 2). By decoding the pulse signals of the sensing array, the loading, wheelbase, and speed of the vehicle are accurately estimated. Moreover, power spectrum estimation of sensor signals can quickly determine the frequency of bridge vibrations with a signal-to-noise ratio greater than 18 dB.

Due to the much larger load on the road compared to the experimental conditions, larger sensors or stiffness matching are required. This article uses acrylic gaskets to share the load applied when vehicles pass through to adjust the strain range borne by the sensor and avoid damage. After installing the stiffness adjuster, the sensor has high stability and sensitivity when the vehicle passes.

Next research plan

We plan to conduct research on asphalt-based piezoelectric power supply devices for intelligent roads integrated with self-powered and sensing functions. Based on the energy harvesting system, the tar-based strain sensor packaged in the Marshall specimen can be embed directly into the road. Moreover, extracting the features contained in the road information collected by the tar-based sensors in the actual environment will also be the focus of our next work.