The manufacture of detergent products such as laundry detergents, household cleaners and fabric softeners are of increasing interest to the consumer oriented chemical industry. Surfactants are the most important ingredient in detergent formulations, as they are responsible for the bulk of the cleaning power. In this research, a methodology has been developed to design a detergent product using computational tools. Different surfactant systems, such as single anionic, single nonionic, and binary mixtures of anionic-nonionic surfactants are covered in this work. Important surfactant properties such as critical micelle concentration (CMC), cloud point (CP), hydrophilic-lipophilic balance (HLB) and molecular weight (MW) have been identified. A group contribution (GC) method with the aid of computer modelling was used to determine the CMC, CP, and MW of surfactant molecules. The design of a surfactant molecule can be formulated as a multi-objective optimization problem that tradeoffs between CMC, CP, HLB and MW. Consequently, a list of plausible nonionic surfactant structures has been developed with the selected surfactant being incorporated into a binary surfactant mixture. Additives such as antimicrobial agents, anti-redeposition agents, builders, enzymes, and fillers were also considered and incorporated into a hypothetical detergent formulation together with the binary surfactant mixture. The typical ingredients and their compositions in detergent formulations are presented in the final stage of the detergent product design.
Cancer is a leading cause of death globally and early diagnosis is of paramount importance for identifying appropriate treatment pathways to improve cancer patient survival. However, conventional methods for cancer detection such as biopsy, CT scan, magnetic resonance imaging, endoscopy, X-ray and ultrasound are limited and not efficient for early cancer detection. Advancements in molecular technology have enabled the identification of various cancer biomarkers for diagnosis and prognosis of the deadly disease. The detection of these biomarkers can be done by biosensors. Biosensors are less time consuming compared to conventional methods and has the potential to detect cancer at an earlier stage. Compared to conventional biosensors, photoelectrochemical (PEC) biosensors have improved selectivity and sensitivity and is a suitable tool for detecting cancer agents. Recently, 2D carbon materials have gained interest as a PEC sensing platform due to their high surface area and ease of surface modifications for improved electrical transfer and attachment of biorecognition elements. This review will focus on the development of 2D carbon nanomaterials as electrode platform in PEC biosensors for the detection of cancer biomarkers. The working principles, biorecognition strategies and key parameters that influence the performance of the biosensors will be critically discussed. In addition, the potential application of PEC biosensor in clinical settings will also be explored, providing insights into the future perspective and challenges of exploiting PEC biosensors for cancer diagnosis.