In a commendable display of community spirit and dedication to saving lives, SRM University-AP hosted a Blood Donation Camp to honour World Blood Donor’s Day. Organised in collaboration with Guntur Medical College, the event marked a significant success and underscored the university’s steadfast commitment to societal welfare.

The camp’s inauguration was graced by Chief Guest Mr Venugopal Reddy, Collector of the Guntur Region. He was accompanied by Dr V Kiran Kumar, Chief Surgeon, and his team from the Government Blood Centre at Government General Hospital-Guntur, Dr R Premkumar, Registrar of SRM University-AP, and Ms Revathi Balakrishnan, Associate Director of Student Affairs, and other key dignitaries witnessed the inaugural ceremony.

In his opening address, Mr Venugopal Reddy stressed the crucial significance of blood donation, emphasising that “Blood is a vital component that cannot be artificially manufactured in laboratories.”

Registrar Dr Premkumar emphasised the importance of donating blood and highlighted that “donation continues to be the sole way to guarantee an ongoing supply of this life-saving elixir.” He stressed the significance of these camps in cultivating a culture of altruism and accountability within the university community.

The university enthusiastically participates in coordinating frequent blood donation drives to support the community and meet the demand for blood donations. The event also served as a platform to dispel myths surrounding blood donation, thereby encouraging broader participation. The students, faculty, and staff of SRM University-AP showed immense enthusiasm in donating blood, significantly contributing to the camp’s outstanding success.

Adding to the day’s significance, Dr Karthik Rajendran, Associate Dean-QAR, was honoured with the Lifesaver Award by the Indian Red Cross Society. This prestigious accolade was bestowed in recognition of his substantial contributions to blood donation and his unwavering commitment to saving lives.

The varsity’s Blood Donation Camp exemplifies the profound impact of collective effort in addressing critical healthcare needs. It highlights the university’s unwavering commitment to making a positive societal impact and reinforces the importance of regular blood donation and community service.

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The Department of Chemistry and RARE Lab are excited to announce a groundbreaking advancement in the field of analytical detection. Researchers Dr Rajapandiyan Panneerselvan, Asst. Professor and Ph.D scholars, Ms Arunima Jinachandran and Ms Jayasree Kumar have developed a novel method for detecting nitrofurazone (NFZ) using three-dimensional silver nanopopcorns (Ag NPCs) on a flexible polycarbonate membrane (PCM) in their paper “Silver nanopopcorns decorated on flexible membrane for SERS detection of nitrofurazone” published in Microchimica Acta. This innovative technique leverages the power of surface-enhanced Raman spectroscopy (SERS) to provide a highly sensitive and practical solution for detecting NFZ on various surfaces, including fish.

Nitrofurazone (NFZ) is an antibiotic commonly used in veterinary medicine that poses significant health risks if residues enter the food chain. Despite regulatory bans, its illegal use continues, necessitating highly sensitive detection methods. While effective, traditional methods such as high-performance liquid chromatography and mass spectrometry are often costly and labor-intensive. The new SERS-based method offers a more efficient and straightforward alternative.

Abstract

The synthesis of three-dimensional silver nanopopcorns (Ag NPCs) onto a flexible polycarbonate membrane (PCM) for the detection of nitrofurazone (NFZ) on fish surfaces by surface-enhanced Raman spectroscopy (SERS) is presented. The proposed flexible Ag-NPCs/PCM SERS substrate exhibits significant Raman signal intensity enhancement with a measured enhancement factor of 2.36 × 10^6. This enhancement is primarily attributed to the hotspots created on Ag NPCs, which include numerous nanoscale protrusions and internal crevices distributed across the surface. The detection of NFZ using this flexible SERS substrate demonstrates a low limit of detection (LOD) of 3.7 × 10^−9 M and uniform, reproducible Raman signal intensities with a relative standard deviation below 8.34%. The substrate also exhibits excellent stability, retaining 70% of its efficacy even after 10 days of storage. Notably, the practical detection of NFZ in tap water, honey water, and fish surfaces achieves LOD values of 1.35 × 10^−8 M, 5.76 × 10^−7 M, and 3.61 × 10^−8 M, respectively, highlighting its effectiveness across different sample types. The developed Ag-NPCs/PCM SERS substrate presents promising potential for the sensitive SERS detection of toxic substances in real-world samples.

Methodology

The synthesis involves creating silver nanopopcorns on a flexible polycarbonate membrane using a simple chemical method. The resulting Ag NPCs exhibit high surface roughness with numerous nanoscale features that enhance the Raman signal. This flexible substrate can easily collect samples from irregular surfaces without requiring extensive preparation.

This SERS substrate can detect NFZ in various real-world samples, including:

• Tap water
• Honey water
• Fish surfaces

The method’s sensitivity and ease of use make it a promising tool for ensuring food safety and monitoring environmental contaminants.

The Department believes this development will significantly impact public health by providing a reliable and accessible method for detecting harmful substances in the food chain.

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Dr Ramesh Vaddi, Associate Professor & Head of the Department of Electronics and Communication Engineering, along with his PhD Scholar Mr A Vinod Kumar has developed a new system for real-time and accurate detection and classification of manufacturing defects in Printed Circuit Boards (PCBs). This groundbreaking invention has been filed and published with Application Number: 202441021739 in the Patent Office Journal.

Abstract

This study presents a new system for real-time detection and classification of defects in Printed Circuit Boards (PCBs), which are critical in electronic products and systems. It employs an efficient model with pre-trained weights to detect defects for enhanced quality control. The model is initially trained and fine-tuned on a computer and then deployed on a compact computing board. For real-time imaging, a high-definition USB camera is connected to the system, allowing direct defect identification without the need for external devices. The output is shown on a monitor, with the PCB image featuring clearly labelled boxes to indicate the type and location of defects. This method offers a streamlined approach to defect classification, helping to improve the quality control process in electronics manufacturing.

Explanation of the Research in Layperson’s Terms

This research focuses on finding defects in PCBs, which are essential for most electronic devices like computers and phones. The system uses a powerful computer model to quickly identify any defects in real time. The model is trained on a regular computer to recognise normal PCBs and various defects. Once ready, it is transferred to a small, efficient computer board. A camera captures images of the PCBs, and the system analyses these images to identify defects. The results are displayed on a screen, clearly marking where the defects are and what types they are. This helps companies quickly and accurately detect defects in their electronics manufacturing process, saving time, reducing waste, and improving product quality

Practical Implementation/Social Implications of the Research

The practical implementation of this research involves deploying a system for real-time detection and classification of defects in PCBs, essential components in nearly all electronic devices. Using advanced deep learning techniques, the system can quickly identify manufacturing defects early in the production process. This leads to significant improvements in quality control, reduced waste, and lower production costs. By improving quality control in electronics manufacturing, the system helps reduce electronic waste, a significant environmental concern. Early detection of defects also decreases the chances of faulty electronic products reaching consumers, enhancing safety and reducing the need for product recalls. The system’s efficiency and accuracy could lead to more reliable electronics, fostering greater consumer trust in electronic products. This, in turn, encourages companies to invest in higher-quality manufacturing processes, ultimately leading to a more sustainable and responsible electronics industry.

Collaborations

To develop this system, the research team first trained a computer model to recognise defects in PCBs. The training involved feeding the model a large dataset of PCB images, some with defects and some without. The model learned to identify common defects by analysing these examples. Once trained, the model was implemented in a real-time setting and integrated with equipment to inspect PCBs during production. The system used a camera to capture images of each PCB and applied the trained model to analyse these images for defects. Running in real-time, the system could immediately detect issues and alert the manufacturing team, allowing them to correct problems on the spot. This approach improved product quality, reduced the chances of defective electronics reaching consumers, sped up the quality control process, and reduced waste, making the manufacturing process more efficient.

Future Research Plans

The research team has outlined several future plans to enhance and expand their defect detection system for PCBs:

• Model Optimization: Refining the machine learning model to improve accuracy and speed, experimenting with different architectures and training techniques to boost performance.
• Expanded Defect Library: Gathering a more extensive dataset of PCB defects to enable the model to identify a wider range of issues, making the system more versatile for various manufacturing environments.
• Real-World Testing: Testing the system in a broader range of manufacturing settings to ensure robustness and adaptability, understanding performance in diverse scenarios, and fine-tuning for optimal results.
• Integration with Manufacturing Systems: Aiming to integrate the system with other manufacturing processes and technologies for seamless communication between defect detection and other quality control systems, enhancing overall workflow.
• Automation and Robotics: Exploring the use of automation and robotics to streamline the defect detection process, potentially leading to a more automated manufacturing line with reduced human intervention and errors.
• Collaboration and Partnerships: Planning to collaborate with more industry partners and academic institutions to accelerate research and development, gaining valuable insights and resources to advance the system.