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Quality lignin-based carbon fiber with high mechanical performance has been made from enzyme–mediator and dialysis fractionated lignin. In particular, the elastic modulus of lignin-based carbon fiber showed good correlations with PDI.

Road surface quality is essential for improving driving experience and reducing traffic accidents. Traditional road condition monitoring systems are limited in their temporal (speed) and spatial (coverage) responses needed for maintaining overall road quality. Several alternative systems have been proposed that utilize sensors mounted on vehicles. In particular, with the ubiquitous use of smartphones for navigation, smartphone-based road condition assessment has emerged as a promising new approach. In this paper, we propose to analyze different multiclass supervised machine learning techniques to effectively classify road surface conditions using accelerometer, gyroscope and GPS data collected from smartphones. Our work focuses on classification of three main class labels-smooth road, potholes, and deep transverse cracks. We hypothesize that using features from all three axes of the sensors provides more accurate results as compared to using features from only one axis. We also investigate the performance of deep neural networks to classify road conditions with and without explicit manual feature extraction. Our results indicate that models trained with features from all axes of the smartphone sensors outperform models that use only one axis. We also observe that the use of neural networks provides a significantly improved data classification. The machine learning approach discussed here can be implemented on a larger scale to monitor roads for defects that present a safety risk to commuters as well as to provide maintenance information to relevant authorities.

The deployment of automated vehicles (AVs) has many potential benefits, such as reductions in congestion and emissions, and safety improvements. However, two notable aspects of AVs are their impact on roadway hydroplaning and pavement life. Since most AVs are programmed to follow a set path and maintain a lateral position in the center of the lane, over time, significant rutting will occur in asphalt surfaced pavements. This study measured AV lateral wandering patterns and compared them with human driven vehicles. Both wandering patterns could be modeled with a normal distribution but have significantly different standard deviations. AVs have a standard deviation for the lateral traffic wander pattern at least three times smaller than human driven vehicles. The influences of AVs with smaller lateral wandering on pavement rutting and fatigue life were analyzed with the Texas Mechanistic-Empirical Flexible Pavement Design system. The research discovered that AVs would shorten pavement fatigue life by 20%. Additionally, pavement rut depths (RD) increased by 13% and reached critical values of the RD 30% earlier. Deeper ruts formed more quickly leading to thicker water films on wet roads, and consequently, a much higher risk of hydroplaning. The research also calculated maximum tolerable RDs at different hydroplaning speeds. AVs have a much smaller tolerable RD human driven vehicles because of a greater water film in the rutted wheel path. This research thus proposed an optimal AV lateral wandering pattern: a uniform distribution. A uniformly distributed lateral wandering pattern for AVs prolongs pavement fatigue life, reduces pavement RD, and decreases hydroplaning potential.

The utilization of lignin for fungible products remains a major challenge for biofuel, pulp and paper industries. We hereby demonstrated the potential of lignin to be used as the asphalt binder modifier, and addressed the challenges in producing high-performance asphalt binder modifiers from lignin. We first demonstrated that Kraft lignin could improve the high temperature performance of asphalt binder, yet compromise the low temperature performance. To address the challenge, we developed both enzyme-mediator-based biological processing and formic acid-based chemical processing to derive lignin fractions to improve the high temperature performance of asphalt binder without compromising its low temperature performance. Moreover, the soluble fraction of biologically processed lignin could improve both high temperature and low temperature performance of asphalt binder, which enabled lignin to serve as a modifier with unique features. We also carried out a thorough characterization of different lignin fractions, and revealed the potential mechanisms for lignin to improve the asphalt binder performance. Overall, the study opened the new avenues for lignin to serve as an exceptional modifier and renewable substitute to improve both high and low temperature performance of asphalt binder. The novel application also transformed lignin waste into a valuable byproduct with market size compatible to biorefinery, pulp and paper industries.

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