In a first-of-its-type invention in the country, scientists at the Institute of Nanoscience and Technology (INST), Mohali, have developed a smart and biodegradable water filter that not only removes toxic dyes from industrial wastewater but also breaks them down using a combination of sunlight and vibrations.
The innovation, seen as a major breakthrough in eco-friendly water purification, has featured recently as the cover art of the journal “Nano Energy” (Elsevier I.F.=17.1), the INST scientist, Dr Aviru Kumar Basu, who led the research conducted by his student, Manshu Dhillon, and team, told The Tribune here.
He said the innovation depicts a futuristic setup where dye-laden water is purified using a 3D-printed biodegradable polylactic acid scaffold activated by solar energy and subtle mechanical vibrations.
“This smart filter, coated with a catalytic layer of Bismuth Ferrite (BiFeO3), degrades dyes like Congo Red and Methylene Blue using a green process called piezo-photocatalysis,” disclosed Dr Basu, while sharing that this dual-triggered process harnesses both light and mechanical energy to initiate powerful chemical reactions that dismantle toxic dye molecules.
He said even under cloudy conditions where solar output is limited, the system continues to function using ambient vibrations.
“This makes it ideal for year-round deployment, especially in resource-limited areas,” said the scientist.
The researchers tested the setup in real-time degradation of dyes, achieving a remarkable efficiency, removing 98.9% of Congo Red and 74.3% of Methylene Blue from the contaminated water samples.
To evaluate the practical applicability of this filter, tests were conducted using real textile wastewater samples obtained from the Common Effluent Treatment Plant (CETP) at Sanganer in Jaipur. “This facility, which currently treats effluents from 192 out of 860 connected textile industries, provided a representative sample for real-world conditions,” he said.
The filter demonstrated effective dye degradation within 60 minutes under visible light and mechanical stimulation.
The results revealed that degradation efficiency varied depending on the nature and composition of the wastewater, which can fluctuate daily due to differences in industrial discharge.
Adding a layer of machine learning (ML) to the system, the researchers used artificial neural networks (ANNs) to train predictive models that simulate how the filter performs under varying light, dye concentration and vibration levels. The machine learning is a subfield of artificial intelligence (AI), which focuses on developing algorithms and allows computers to learn from and make predictions or decisions based on data without explicit programming.
“These models achieved up to 99% accuracy in predicting catalytic performance, helping optimisation of the filter for diverse field conditions,” the researchers said.
The device is reusable, low-cost and avoids the need for harmful chemicals, positioning it as a sustainable alternative to conventional techniques like ozonation and electrochemical oxidation, which are often expensive and energy-intensive.
The innovation is being hailed as a scalable solution for industries like textiles, leather and pharmaceuticals, all major contributors to dye pollution, and may also benefit rural and off-grid communities where clean water access is still a challenge.