Large quantities of organic solvents and reagents are usually required in the fabrication of these materials. Moreover, the use of excess reagents in solvent extraction, purification, filtration/ centrifugation, and cleaning processes is essential for adequate product purity. Because of the numerous practical applications of advanced materials, some are synthesized on a large scale and commercialized. [3] The excessive consumption of nonrenewable, hazardous chemicals is an unsustainable practice and a prominent criticism of these materials. Nonetheless, the use of advanced materials in sensing applications, medicine, electronics, and catalysis is inevitable. [4] During various research activities, such as fabricating advanced materials, as well as in the subsequent stage, i.e., industrial operations, new challenges have arisen that impact not only almost every aspect of human life but also other living creatures and ecosystems. It is estimated that the impact of these practices will endure into the next century. [5] Since the advancement of science, industry, and technology is inevitable, scientists have been persuaded to search for methods to counteract or minimize the negative impacts of human activities. [6] Thus, green chemistry has advanced and green principles have been defined for most sciences, including synthesis, [7] analytical chemistry, [8] engineering, [9] and pharmaceutical science. [10] Fortunately, contemporary society has great awareness of deleterious environmental side effects, resulting in a sense Advances in revolutionary technologies pose new challenges for human life; in response to them, global responsibility is pushing modern technologies toward greener pathways. Molecular imprinting technology (MIT) is a multidisciplinary mimic technology simulating the specific binding principle of enzymes to substrates or antigens to antibodies; along with its rapid progress and wide applications, MIT faces the challenge of complying with green sustainable development requirements. With the identification of environmental risks associated with unsustainable MIT, a new aspect of MIT, termed green MIT, has emerged and developed. However, so far, no clear definition has been provided to appraise green MIT. Herein, the implementation process of green chemistry in MIT is demonstrated and a mnemonic device in the form of an acronym, GREENIFICATION, is proposed to present the green MIT principles. The entire greenificated imprinting process is surveyed, including element choice, polymerization implementation, energy input, imprinting strategies, waste treatment, and recovery, as well as the impacts of these processes on operator health and the environment. Moreover, assistance of upgraded instrumentation in deploying greener goals is considered. Finally, future perspectives are presented to provide a more complete picture of the greenificated MIT road map and to pave the way for further development.

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In a fierce competitive industry, firms conducting a corporate social responsibility (CSR) differentiation strategy can build a relative advantage. However, there is lack of literature to discuss the approach to identifying companies’ CSR differentiation conditions. Based on the theoretical foundations of consumers’ responses to CSR differentiation strategies, this paper proposes a consumer-oriented approach to identify CSR differentiation by using the best–worst scaling approach. In the context of the mobile phone industry, CSR activities were prioritized according to the extent to which they were valued by consumers. Consumers’ perceptions of the CSR activities of Huawei and Apple were also assessed in this study. Finally, the CSR differentiation conditions between the two companies was evaluated. The findings include the following: (1) the consumer priorities for different CSR activities vary greatly, and it is essential for firms to adopt a CSR differentiation strategy; (2) it is feasible to adopt a proper CSR premium in product pricing to build a socially responsible company; and (3) the lack of CSR communication between companies and consumers leads to consumers’ perceived distortion. The results provide implications for firms’ CSR practice.

Incomplete data with missing feature values are prevalent in clustering problems. Traditional clustering methods first estimate the missing values by imputation and then apply the classical clustering algorithms for complete data, such as K-median and K-means. However, in practice, it is often hard to obtain accurate estimation of the missing values, which deteriorates the performance of clustering. To enhance the robustness of clustering algorithms, this paper represents the missing values by interval data and introduces the concept of robust cluster objective function. A minimax robust optimization (RO) formulation is presented to provide clustering results, which are insensitive to estimation errors. To solve the proposed RO problem, we propose robust K-median and K-means clustering algorithms with low time and space complexity. Comparisons and analysis of experimental results on both artificially generated and real-world incomplete data sets validate the robustness and effectiveness of the proposed algorithms.