In a significant breakthrough for environmental monitoring and public health, scientists from the Centre for Nano and Soft Matter Sciences (CeNS), Bengaluru, India, have developed an affordable and highly sensitive sensor capable of detecting sulfur dioxide (SO₂) gas at extremely low concentrations.

Sulfur dioxide, a toxic air pollutant primarily released from vehicle exhaust and industrial processes, is notorious for triggering respiratory irritation, asthma attacks, and long-term lung damage. Monitoring its presence in real time is essential, but existing technologies are often expensive, power-hungry, or ineffective at detecting the gas at trace levels.

To address this gap, the CeNS team, under the leadership of Dr. S. Angappane, has engineered a novel sensor by combining two metal oxides — nickel oxide (NiO) and neodymium nickelate (NdNiO₃). NiO serves as the receptor that captures SO₂ molecules, while NdNiO₃ acts as a transducer that converts the chemical interaction into an electrical signal. This innovative design enables the sensor to detect SO₂ at concentrations as low as 320 parts per billion (ppb), outperforming many commercial alternatives.

Speaking about the development, Dr. Angappane said in a media statement, “This sensor system not only advances the sensitivity benchmark but also brings real-time gas monitoring within reach for a wider range of users. It demonstrates how smart materials can provide practical solutions for real-world environmental challenges.”

The CeNS team has also built a portable prototype incorporating the sensor. It features a user-friendly threshold-triggered alert system with color-coded indicators: green for safe levels, yellow for warning, and red for danger. This visual approach ensures that even non-specialist users can understand and respond to pollution risks instantly. Its compact size and lightweight design make it ideal for deployment in industrial zones, urban neighborhoods, and enclosed environments requiring continuous air quality surveillance.

The sensor system was conceptualized and designed by Mr. Vishnu G Nath, with key contributions from Dr. Shalini Tomar, Mr. Nikhil N. Rao, Dr. Muhammed Safeer Naduvil Kovilakath, Dr. Neena S. John, Dr. Satadeep Bhattacharjee, and Prof. Seung-Cheol Lee. The research findings were recently published in the journal Small.

With this innovation, CeNS reinforces the role of advanced materials science in developing cost-effective technologies that protect both public health and the environment.

In the aftermath of the COVID-19 pandemic, industries worldwide grappled with a shared vulnerability: sudden breakdowns and disrupted repair services. Now, a new research study by Indian mathematicians C.K. Anjali and Sreekanth Kolledath, from Amrita Vishwa Vidyapeetham, Kochi, Kerala, offers a scientifically robust answer.

Published in one of Elsevier‘s peer-reviewed journals, the study introduces an innovative multi-server machining queuing model that simulates emergency vacations — sudden, unplanned leaves of absence taken by maintenance staff due to crises such as pandemics or natural disasters.

This innovative approach also accounts for “reneging”, when malfunctioning units exit the system before being serviced, and integrates retention strategies to keep these units within the repair cycle — a nod to the real-world pressures and adaptations faced by modern industrial systems.

“The disruptions caused by the COVID-19 pandemic made it clear how critical unexpected breakdowns and service interruptions can be in industrial systems,” co-author Sreekanth Kolledath said to EdPublica. “This inspired us to model such emergency scenarios more realistically and explore efficient optimization strategies.”

The Power of teaching–learning-based optimization

What truly sets this study apart is its use of a relatively novel algorithm: Teaching–Learning-Based Optimization (TLBO) — a human-inspired metaheuristic. TLBO mimics the interactions in a classroom, where students improve by learning from both teachers and peers. This “educational” algorithm is benchmarked against more established methods like Particle Swarm Optimization (PSO) and Genetic Algorithms (GA).

The result? TLBO consistently outperformed its peers in optimizing the cost and efficiency of repair operations under complex conditions, showing robustness in handling dynamic workloads and service interruptions.

“This research helps bridge a gap in queuing theory by not only modelling realistic industrial disruptions but also applying an underused yet highly effective optimization technique,” explained lead researcher C.K. Anjali.

Modelling real-life Complexities

The model simulates environments like CNC machining systems where multiple machines (K), standbys (S), and repairmen (R) operate under fluctuating conditions. Emergency vacations are modelled using probability distributions, while the likelihood of units leaving (reneging) and being retained is factored into performance and cost metrics.

Using matrix-analytic methods, the researchers assessed system behaviour across parameters like waiting times, failure rates, and repair loads. Their simulations revealed:

• Increased emergency vacations lead to higher wait times and unit failures.
• Faster server startup (post-vacation) mitigates congestion.
• Higher reneging probability severely affects system throughput — but retention mechanisms help stabilize it.
• TLBO yielded the lowest total operational cost among the three algorithms across all test cases.

A blueprint for resilient manufacturing

Beyond academic impact, the implications of this research are practical and global. Industries like aerospace, healthcare, and smart manufacturing—where machine uptime is crucial—can integrate this model to simulate and prepare for emergency disruptions.

Moreover, by applying TLBO, organizations can fine-tune costs related to machine downtime, labour availability, and service logistics, helping build resilience in supply chains and production floors.

What’s next?

The researchers suggest future work could extend the model to cloud-based repair simulations, energy-aware systems, and AI-integrated predictive maintenance, further aligning with the Industry 5.0 vision.

“This research was made possible only due to the constant encouragement and support of Dr. U. Krishnakumar, our visionary Director at the Kochi Campus in Kerala, India,” adds Kolledath. “He is widely known for fostering a culture of quality research within the institution.”

As the world continues to adapt to increasingly unpredictable events, the fusion of human-inspired algorithms with real-world engineering models might just be the lesson industries need most.

In a significant step toward sustainable photonic technologies, scientists from the Raman Research Institute (RRI), an autonomous institute under the Department of Science and Technology (DST), Government of India, have discovered that teak leaf extract can serve as an effective, natural shield against harmful laser radiation. This breakthrough offers new potential for protecting both sensitive optical sensors and human eyes from high-intensity lasers used in medical, industrial, and defense applications.

The team has found that the otherwise discarded leaves of the teak tree (Tectona grandis L.f) are rich in anthocyanins, natural pigments responsible for their reddish-brown colour. When exposed to light, these pigments exhibit nonlinear optical (NLO) properties, allowing them to absorb intense laser beams—a key feature required for laser safety gear.

The discovery, recently published in the Journal of Photochemistry and Photobiology A: Chemistry, proposes a non-toxic, biodegradable, and cost-effective alternative to conventional synthetic materials like graphene and metal nanoparticles, which are often expensive and environmentally hazardous.

“Teak leaves are a rich source of natural pigments such as anthocyanin… We explored the potential of teak leaf extract as an eco-friendly alternative to synthetic dyes in the field of nonlinear optics,” said Beryl C, DST Women Scientist at RRI, in a media statement issued by the government.

To extract this natural dye, researchers dried and powdered teak leaves, soaked them in solvents, and processed the mixture using ultrasonication and centrifugation. The resulting reddish-brown liquid was then tested with green laser beams under continuous and pulsed conditions.

Using advanced techniques like Z-scan and Spatial Self-Phase Modulation (SSPM), the dye demonstrated reverse saturable absorption (RSA)—a rare and desirable trait where the material absorbs more light as the intensity increases, effectively acting as a self-regulating shield against laser exposure.

This development is particularly crucial as laser technologies become increasingly prevalent in everyday environments—from surgical devices and industrial cutters to military-grade systems. By offering a natural and renewable solution to a global safety challenge, the RRI team has opened the door to a future of eco-conscious optical safety equipment, such as laser-resistant eyewear, coatings, and sensor shields.

Researchers also indicated that further studies will focus on enhancing the stability and commercial usability of the dye for long-term deployment.

This innovation aligns with the principles of Industry 5.0, emphasizing human-centered and environmentally responsible technology, and showcases how indigenous, sustainable resources can play a pivotal role in global tech solutions.