Can the application of artificial intelligence in industry cut China's industrial carbon intensity?

As an emerging technology, industrial intelligence focus on the integration of artificial intelligence and production, which creates a new access to achieve the goal of carbon emissions reduction. Using data on provincial panel data from 2006 to 2019 in China, we empirically analyze the impact and spatial effects of industrial intelligence on industrial carbon intensity from multiple dimensions. Results show an inverse proportionality between industrial intelligence and industrial carbon intensity, and the mechanism is to promote green technology innovation. Our results remain robust after accounting for endogenous issues. Viewed from spatial effect, industrial intelligence can inhibit not only the industrial carbon intensity of the region but also the surrounding areas. More strikingly, the impact of industrial intelligence in the eastern region is more obvious than that in the central and western regions. This paper effectively complements the research on the influencing factors of industrial carbon intensity and provides a reliable empirical basis for industrial intelligence to reduce industrial carbon intensity, as well as a policy reference for the green development of the industrial sector.

Effects of transglutaminase cross-linking process on printability of gelatin microgel-gelatin solution composite bioink.

Three-dimensional (3D) bioprinting has emerged as a powerful engineering approach for various tissue engineering applications, particularly for the development of 3D cellular structures with unique mechanical and/or biological properties. For the jammed gelatin microgel-gelatin solution composite bioink, comprising a discrete phase of microgels (enzymatically gelled gelatin microgels) and a cross-linkable continuous gelatin precursor solution-based phase containing transglutaminase (TG), its rheological properties and printability change gradually due to the TG enzyme-induced cross-linking process. The objective of this study is to establish a direct mapping between the printability of the gelatin microgel-gelatin solution based cross-linkable composite bioink and the TG concentration and cross-linking time, respectively. Due to the inclusion of TG in the composite bioink, the bioink starts cross-linking once prepared and is usually prepared right before a printing process. Herein, the bioink printability is evaluated based on the three metrics: injectability, feature formability, and process-induced cell injury. In this study, the rheological properties such as the storage modulus and viscosity have been first systematically investigated and predicted at different TG concentrations and times during the cross-linking process using the first-order cross-linking kinetics model. The storage modulus and viscosity have been satisfactorily modeled as exponential functions of the TG concentration and time with an experimentally calibrated cross-linking kinetic rate constant. Furthermore, the injectability, feature formability, and process-induced cell injury have been successfully correlated to the TG concentration and cross-linking time via the storage modulus, viscosity, and/or process-induced shear stress. By combing the good injectability, good feature formability, and satisfactory cell viability zones, a good printability zone (1.65, 0.61, and 0.31 h for the composite bioinks with 1.00, 2.00, and 4.00% w/v TG, respectively) has been established during the printing of mouse fibroblast-based 2% gelatin B microgel-3% gelatin B solution composite bioink. This printability zone approach can be extended to the use of other cross-linkable bioinks for bioprinting applications.