Global food demand has increased in tandem with the world’s growing population, prompting calls for a new sustainable agricultural method. The scarcity of fertile soil and the world’s agricultural land have also become major concerns. Soilless and microgreen farming combined with nanotechnology may provide a revolutionary solution as well as a more sustainable and productive alternative to conventional farming. In this review, we look at the potential of nanotechnology in soilless and microgreen farming. The available but limited nanotechnology approaches in soilless farming include: (1) Nutrients nanoparticles to minimize nutrient losses and improve nutrient uptake and bioavailability in crops; (2) nano-sensing to provide real-time detection of p H, temperature, as well as quantifying the amount of the nutrient, allowing desired conditions control; and (3) incorporation of nanoparticles to improve the quality of substrate culture as crop cultivation growing medium. Meanwhile, potential nanotechnology applications in soilless and microgreen farming include: (1) Plant trait improvement against environmental disease and stress through nanomaterial application; (2) plant nanobionics to alter or improve the function of the plant tissue or organelle; and (3) extending the shelf life of microgreens by impregnating nanoparticles on the packaging or other preservation method.

Malaysians generate 15,000 tons of food waste per day and dispose of it in the landfill, contributing to greenhouse gas emissions. As a solution for the stated problem, this research aims to produce an excellent quality bokashi compost from household organic waste using a smart composting bin. The bokashi composting method is conducted, whereby banana peels are composted with three types of bokashi brans prepared using 12, 22, and 32 mL of EM-1 mother cultured. During the 14 days composting process, the smart composting bin collected the temperature, air humidity, and moisture content produced by the bokashi-composting process. With the ATmega328 microcontroller, these data were uploaded and synchronized to Google Sheet via WIFI. After the bokashi-composting process was completed, three of each bokashi compost and a control sample were buried in separate black soil for three weeks to determine each compost’s effectiveness. NPK values and the C/N ratio were analyzed on the soil compost. From the research, 12 mL of EM-1 shows the most effective ratio to the bokashi composting, as it resulted in a faster decomposition rate and has an optimum C/N ratio. Bokashi composting can help to reduce household food wastes. An optimum amount of the EM-1 used during the bokashi-composting process will produce good quality soil without contributing to environmental issues.

The generation of municipal solid waste (MSW) is increasing globally every year, including in Malaysia. Approaching the year 2020, Malaysia still has MSW disposal issues since most waste goes to landfills rather than being utilized as energy. Process network synthesis (PNS) is a tool to optimize the conversion technologies of MSW. This study optimizes MSW conversion technologies using a PNS tool, the “process graph” (P-graph). The four highest compositions (i.e., food waste, agriculture waste, paper, and plastics) of MSW generated in Malaysia were optimized using a P-graph. Two types of conversion technologies were considered, biological conversion (anaerobic digestion) and thermal conversion (pyrolysis and incinerator), since limited data were available for use as optimization input. All these conversion technologies were compared with the standard method used: landfilling. One hundred feasible structure were generated using a P-graph. Two feasible structures were selected from nine, based on the maximum economic performance and minimal environmental impact. Feasible structure 9 was appointed as the design with the maximum economic performance (MYR 6.65 billion per annum) and feasible structure 7 as the design with the minimal environmental impact (89,600 m³/year of greenhouse gas emission).

This study aims to produce amidoxime-modified poly(acrylonitrile-co-acrylic acid) using an optimized method and to investigate the performance of amidoxime-modified poly(acrylonitrile-co-acrylic acid) on the adsorption of boron ions in batch operations. Batch adsorption was conducted at the physiochemical parameters of pH, adsorbent dosage, and initial boron concentration. The isotherms and kinetics of adsorption data were studied at various initial boron concentrations. The renewed synthesis process gave a production yield of 77%, and the conversion of nitrile group to amidoxime was 78%. The adsorption reached its optimum point at pH = 8, adsorbent dosage = 4 g·L−1, and initial adsorbent concentration at 40 ppm. The best model fits for isotherm adsorption was the Sips model with heterogeneity factor (n) = 0.7611. In the kinetic study, the adsorption data fitted best with pseudo-second-order model. The synthesized polymeric adsorbent could be recycled with little decline in its boron entrapment capacities. Hence, amidoxime-modified poly(acrylonitrile-co-acrylic acid) exhibited high adsorption capacity and could be potentially explored as an alternative to commercial resin in the removal of boron from wastewater.

This research reports application of artificial neural network (ANN) in investigation and optimisation of boron adsorption capacity in aqueous solution using amidoxime-modified poly(acrylonitrile-co-acrylic acid) (AO-modified poly(AN-co-AA)). Both feed-forward and recurrent ANN have been utilized to predict the adsorption potential of synthesised polymer. Three operational parameters, which are adsorbent dosage, initial pH and initial boron concentration during adsorption process were designed to study their effects on the removal capacity. The ANN was trained from experimental data and serviced to optimize, develop and create various prediction models in the process of boron adsorption by AO-modified poly(AN-co-AA). Among several models, radial basis function (RBF) with orthogonal least square (OLS) algorithm displayed good prediction on boron adsorption capacity with mean square error (MSE) and coefficient of determination (R2) at 0.000209 and 0.9985, respectively. With desirable the MSE and R2 values, ANN worked as a promising prediction tool that was able to generate good estimate. The simulated maximum adsorption capacity of the synthesized polymer is 15.23 ± 1.05 mg boron/g adsorbent. Besides, from the results of ANN, the AO-modified poly(AN-co-AA) was proven to be a potential adsorbent for the removal of boron in wastewater treatment.