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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.


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.


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.


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.


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.

The poor in India has continuous future benefits from globalization through improved healthcare – A new research from SRM University-AP economists
A new research from SRM University-AP economistsIn a groundbreaking study, Dr. Manzoor H. Malik, an Assistant Professor in the Department of Economics, and Sodiq O. Bisiriyu, a Research Scholar, have developed a comprehensive framework known as the globalization-growth-poverty (GPP) triangular nexus.

This innovative model examines the interplay between globalisation waves, economic growth trends, and their collective impact on poverty reduction in India, aligning with the nation’s commitment to achieving Goal 1 of the Sustainable Development Goals.

The researchers used three different indicators of poverty, encompassing the monetary and non-monetary constructs. The research controls for the impact of government development spending and domestic investment in the GPP nexus. The result of the research validates the significant globalization-led growth hypothesis in the short run and establishes the existence of the GPP triangular nexus in India. Additionally, the researchers found positive association of globalization and growth with monetary and non-monetary poverty measures in the short-run while the impact on monetary measure in the long-run is ambiguous.

Finally, the results offer significant insight into the impact of government development spending and domestic investment on poverty. The results prove that government development expenditure in India is detrimental to non-monetary poverty while domestic investment have insignificant impact on all poverty constructs.

From policy perspective, the researcher said, “the short-run validation of globalization-led growth hypothesis for India is challenging and calls for government broad-based framework to tackle impediments of long-term globalization-led growth and monetary poverty benefits”. They emphasized that only sustained long-term growth can accelerate the vision of India as stated in “Viksit Bharat 2047” agenda. Published in the esteemed ‘Quality & Quantity’ journal by Springer, with an impact factor of 2.87, this study provides valuable insights for academics, practitioners, and policymakers. It sheds light on the complex dynamics shaping contemporary socio-economic environments and offers evidence-based analysis supported by an extensive review of relevant literature.

Link to the Article

In a significant development, Prof. Kamesh AVS, Professor at Paari School of Business, along with his PhD Scholar, Ms. Ashrafunnisa Mohammed, has successfully published and filed a groundbreaking patent titled “SYSTEM AND METHOD TO DETECT BOTTLENECKS AND ENHANCE EMPLOYEE PRODUCTIVITY” with the Application Number: 202441014093 A in the Patent Office Journal.

This pioneering system aims to revolutionise the way organisations detect and alleviate bottlenecks within their operations, consequently boosting overall employee productivity. The patent’s unique approach and methodology signify a remarkable stride towards fostering efficiency and performance in the workplace. Prof. Kamesh AVS and Ms. Ashrafunnisa Mohammed’s collaborative effort underscores their commitment to innovation and research excellence. Their accomplishment not only reflects their expertise in the field but also highlights the spirit of ingenuity and problem-solving that drives academia at Paari School of Business.

The SRM AP community extends its heartfelt congratulations to Prof. Kamesh AVS and Ms Ashrafunnisa Mohammed on this remarkable achievement and eagerly anticipates the positive impact their invention will have on the realm of employee productivity and operational efficiency.

Abstract of the Research
The present disclosure relates to a system for detecting bottlenecks and enhancing employee productivity. The system broadly includes an input module (106), a pre-processing module (108), a segmentation module (110), a feature selection module (112), a feature extraction module (114), a data training module (116), and a classification module (118). The system also consists of a data repository (102), a processor (104), and an evaluation module (120). The present disclosure aims to examine counterproductive work behaviors of employees through the effort-reward imbalance model by considering dark triad traits as moderators. The dark triad traits can indulge the employees more in unethical practices in organizations and one among those traits is Narcissism which negatively affects counterproductive work behaviors. Organizations can predict the Dark Triad traits in individuals, and they can avoid unethical practices in organizations and counterproductive work behaviors.

Explanation of the Research in Layperson’s Terms:

The present process aims to examine counterproductive work behaviors through the effort-reward imbalance model by taking into consideration Dark triad traits as moderators. The Dark Triad traits can indulge the employees more in unethical practices in organizations and one among those traits is Narcissism which negatively affects counterproductive work behaviors. Organizations can predict the Dark Triad traits in individuals, and they can avoid unethical practices in organizations and can avoid counterproductive work behaviors. There is a notable knowledge gap concerning the moderating role of Dark Triad traits in the relationship between efforts and rewards imbalance and counterproductive work behaviors, even though there is existing research on both the association between Dark Triad traits and counterproductive work behaviors and the relationship between efforts and rewards imbalance and counterproductive work behaviors.

Practical Implementation or Social Implications Associated with the Research:

• The Invention aims at identifying and eliminating the consequences of the Dark Triad Traits while performing Human Resources Practices like Recruitment, Training and Development, Performance Analysis, and Compensation and Reward Management system.
• The Invention either weakens or eliminates the Counter Productive work behaviors as a potential consequence of springing up of Dark Triad traits during the execution of various Human Resource practices in the organizations and this leads to achieving the estimated Organizational Productivity as per the strategy.
• Organizations can identify the impact of the Dark triad traits on employees and take steps to reduce the stress that leads to unproductive work behaviors.
• Organizations can eliminate the job stressors which are seen as the main sources of
• Counterproductive work behaviors can indicate the Dark triad traits’ influence on individuals.
• Management should investigate the causes of rudeness in the workplace to improve morale and productivity.
• During the hiring process, management should use a battery of psychological tests to gain insight into candidates’ personalities.
• Management can formulate standard policies to eliminate malicious activities and avoid effort-reward imbalances and counterproductive work behaviors.
• Understanding the causes of counterproductive workplace behavior is vital because it has become a widespread and expensive problem for businesses worldwide.

Future Research Plans
To Incorporate AI and ML tools to identify the Dark Triad traits, thereby enhancing the employee evaluation process and mitigating counterproductive work behaviors within organisations.

Link to the article