With the advancement of technologies and growth of the economy, it is inevitable that more complex processes are deployed, producing more heterogeneous wastewater that comes from biomedical, biochemical and various biotechnological industries. While the conventional way of wastewater treatment could effectively reduce the chemical oxygen demand, pH and turbidity of wastewater, trace pollutants, specifically the endocrine disruptor compounds (EDCs) that exist in µg L-1 or ng L-1 have further hardened the detection and removal of these biochemical pollutants. Even in small amounts, EDC could interfere human's hormone, causing severe implications on human body. Hence, this review elucidates the recent insights regarding the effectiveness of an advanced 2D material based on titanium carbide (Ti3C2Tx), also known as MXene, in detecting and removing EDCs. MXene's highly tunable feature also allows its surface chemistry to be adjusted by adding chemicals with different functional groups to adsorb different kinds of EDCs for biochemical pollution mitigation. At the same time, the incorporation of MXene into sample matrices also further eases the analysis of trace pollutants down to ng L-1 levels, thereby making way for a more cleaner and comprehensive wastewater treatment. In that sense, this review also highlights the progress in synthesizing MXene from the conventional method to the more modern approaches, together with their respective key parameters. To further understand and attest to the efficacy of MXene, the limitations and current gaps of this potential agent are also accentuated, targeting to seek resolutions for a more sustainable application.
Municipal wastewater is ubiquitously laden with myriad pollutants discharged primarily from a combination of domestic and industrial activities. These heterogeneous pollutants are threating the natural environments when the traditional activated sludge system fails sporadically to reduce the pollutants' toxicities. Besides, the activated sludge system is very energy intensive, bringing conundrums for decarbonization. This research endeavoured to employ Chlorella vulgaris sp. In converting pollutants from municipal wastewater into hydrogen via alternate light and dark fermentative process. The microalgae in attached form onto 1 cm3 of polyurethane foam cubes were adopted in optimizing light intensity and photoperiod during the light exposure duration. The highest hydrogen production was recorded at 52 mL amidst the synergistic light intensity and photoperiod of 200 μmolm-2s-1 and 12:12 h (light:dark h), respectively. At this lighting condition, the removals of chemical oxygen demand (COD) and ammoniacal nitrogen were both achieved at about 80%. The sustainability of microalgal fermentative performances was verified in recyclability study using similar immobilization support material. There were negligible diminishments of hydrogen production as well as both COD and ammoniacal nitrogen removals after five cycles, heralding inconsequential microalgal cells' washout from the polyurethane support when replacing the municipal wastewater medium at each cycle. The collected dataset was finally modelled into enhanced Monod equation aided by Python software tool of machine learning. The derived model was capable to predict the performances of microalgae to execute the fermentative process in producing hydrogen while subsisting municipal wastewater at arbitrary photoperiod. The enhanced model had a best fitting of R2 of 0.9857 as validated using an independent dataset. Concisely, the outcomes had contributed towards the advancement of municipal wastewater treatment via microalgal fermentative process in producing green hydrogen as a clean energy source to decarbonize the wastewater treatment facilities.