Fractionated Lignosulfonates for Laccase-Catalyzed Oxygen-Scavenging Films and Coatings

Lignin derivatives have potential as antioxidants in advanced packaging materials through their ability to scavenge oxygen in reactions catalyzed by phenol-oxidizing enzymes, such as laccase. The effects of size fractionation of lignosulfonates on laccase-catalyzed reactions were investigated in experiments with aqueous solutions, films, and coated paperboard. Four industrial lignosulfonate preparations were compared: Feed (unfractionated), Prod (5–60 kDa enriched), Conc (≥60 kDa enriched), and Perm (≤60 kDa enriched). Extraction of lignosulfonates from films showed that the enzymic reaction increased the average molecular weight from <10,000 to up to 66,000. The enzymatic reaction resulted in an increase in the water contact angle of the films from the range 25–49° to 56–81°. The four preparations showed relatively small differences with regard to their ability to scavenge oxygen in aqueous solution and in experiments with coated paperboards in sealed chambers. Coatings with lignosulfonates where the contents of low-molecular weight material had been reduced (i.e., Prod and Conc) showed improved water resistance after the enzymic reaction. Thus, in both aqueous and solid media, fractionation of lignosulfonates had little effect on oxygen scavenging, but fractionation was beneficial for other reasons, such as improved cross-linking resulting in higher molecular weight and superior water resistance.

An Overview of AI-Assisted Design-on-Simulation Technology for Reliability Life Prediction of Advanced Packaging

Several design parameters affect the reliability of wafer-level type advanced packaging, such as upper and lower pad sizes, solder volume, buffer layer thickness, and chip thickness, etc. Conventionally, the accelerated thermal cycling test (ATCT) is used to evaluate the reliability life of electronic packaging; however, optimizing the design parameters through ATCT is time-consuming and expensive, reducing the number of experiments becomes a critical issue. In recent years, many researchers have adopted the finite-element-based design-on-simulation (DoS) technology for the reliability assessment of electronic packaging. DoS technology can effectively shorten the design cycle, reduce costs, and effectively optimize the packaging structure. However, the simulation analysis results are highly dependent on the individual researcher and are usually inconsistent between them. Artificial intelligence (AI) can help researchers avoid the shortcomings of the human factor. This study demonstrates AI-assisted DoS technology by combining artificial intelligence and simulation technologies to predict wafer level package (WLP) reliability. In order to ensure reliability prediction accuracy, the simulation procedure was validated by several experiments prior to creating a large AI training database. This research studies several machine learning models, including artificial neural network (ANN), recurrent neural network (RNN), support vector regression (SVR), kernel ridge regression (KRR), K-nearest neighbor (KNN), and random forest (RF). These models are evaluated in this study based on prediction accuracy and CPU time consumption.