Methylene Blue and Congo Red are industrial dyes primarily used in industries such as textile manufacturing and pharmaceuticals. They are primarily used in histology, the study of the microscopic structure of tissues and cells, for staining tissues and cells, and sometimes to dye textiles. Wastewater discharge from these industries is also heavy contaminated with these dyes and has been a significant source of water pollution. These don’t just add colour to our waterways but pose serious threats to aquatic life and human health, leading to issues like skin problems and respiratory diseases.
Methods traditionally used to clean this water, such as electrochemical or ozone treatments, are often energy-intensive, expensive, and rely on harsh chemicals that can be hazardous to the environment themselves. One of the most promising and environmentally friendly approaches involves developing catalytic technologies that can harness renewable and clean energy sources. Photocatalysis, which uses the sun's abundant, limitless power, is a particularly appealing and energy-efficient process. It works by converting sunlight into chemical energy using semiconductors, all at a minimal cost. However, this method faces challenges, primarily because sunlight is only available during the daytime. Additional inefficiencies arise from light-absorbing substances in the water.
Researchers at the Institute of Nanoscience and Technology (INST) in Mohali, India, an autonomous institute of the Department of Science and Technology (DST), have been working on a solution to address this economic challenge. Led by Dr. Aviru Basu, the team has designed a new type of water filter that traps and neutralises these chemicals. It does this using an ingenious mix of sunlight, gentle vibrations, and a little help from Machine Learning.
The new filter is made of a substrate, a 3D-printed structure made from a biodegradable polymer called polylactic acid (PLA). The substrate is then coated with Bismuth Ferrite (BiFeO3), a material with a unique piezo-photocatalytic property. Piezo-photocatalysis combines the principles of piezocatalysis and photocatalysis to enhance the degradation of pollutants and other chemical transformations.
The method leverages the piezoelectric effect, the generation of an electrical potential from mechanical stress, and the photocatalytic effect, which utilises light and a catalyst to drive reactions. In piezo-photocatalysis, the internal electric field generated by the piezoelectric effect helps to separate the photogenerated electrons and holes, reducing their recombination rate and enhancing the photocatalytic activity. This leads to improved efficiency in pollutant degradation and other chemical transformations.
Did You Know? Piezo-photocatalysis has been explored for various applications, including – Wastewater treatment; Hydrogen production: Splitting water to produce hydrogen gas; CO2 reduction: Converting carbon dioxide into valuable chemicals; and N2 fixation: Converting nitrogen gas into ammonia. |
BiFeO3 is known for its ability to break down pollutants when it's exposed to light and mechanical energy. The PLA scaffold acts as a catalyst for BiFeO3, enabling the process of piezo-photocatalysis. The 3D-printed PLA substrate was uniformly coated with BFO nanoparticles using a simple dip-coating method to ensure uniform coating
When light hits the structure, they get activated and react with the MB and CR in the water, degrading them. In the absence of light, the piezo part of the material takes over. Gentle vibrations, like those from water flowing or ambient environmental movements, are enough to activate the catalyst.
This means the water cleaning process doesn't stop, even when the sun isn't shining brightly. It's a brilliant workaround for the limitations of traditional solar-powered purification systems.
Furthermore, machine learning regression models were employed to predict the dye degradation behaviour by training them with experimental data. The predicted data from these regression models were then compared with untrained experimental data to assess the models' accuracy. The main machine learning models used were Catboost, XGBoost, Random Forest, Light GBM, and Artificial Neural Networks (ANNs), which are reliable algorithms for determining relationships between dependent and independent variables, especially when complex linear or non-linear relationships exist and the outcome variables are continuous.
The hybrid system was tested on wastewater samples containing Congo Red and Methylene Blue, proving extremely successful in degrading these dyes. Specifically, 98.9% degradation was achieved for Congo Red and 74.3% for Methylene Blue within just 90 minutes.
Unlike traditional methods that use harsh chemicals, this innovation is biodegradable and eco-friendly, harnessing renewable energy sources – sunlight and vibrations – instead of relying on fossil fuels. The technology is designed to be low-cost and reusable, making it accessible for wider adoption. The innovation offers a cost-effective, reusable, and environmentally friendly solution, representing a significant step towards safeguarding our water sources from industrial waste, offering a practical and scalable solution for communities and industries worldwide, moving beyond the limitations of traditional powder catalysts and energy-intensive methods.
This article is based on a Press Release from PIB.
This article was written with the help of generative AI and edited by an editor at Research Matters.