The Department of Chemistry is glad to announce that Dr Nimai Mishra, Assistant Professor, along with his research group comprising PhD scholars, Syed Akhil, Manoj Palabathuni, Subarna Biswas, Rahul Singh, have published an article titled Highly-Stable Amine-Free CsPbBr3 Perovskite Nanocrystals for Perovskite-Based Display Applications in the  journal ACS Applied Nano Materials published by the American Chemical Society, having an impact factor of 6.14.

Colloidally synthesised cesium lead halide (CsPbX3; X=Cl, Br, and I) perovskite nanocrystals (PNCs) often suffer from poor ambient and environmental stability conditions, limiting their practical applications. The commonly used surfactant oleylamine is converted to oleylammonium cation, which pulls out the halide anion from the PNCs surface, thus disrupting the nanocrystal’s structural integrity and stability.

The research group has developed a simple, completely amine-free colloidal synthesis with a hot injection method in open-atmospheric conditions and introduced bromooctane as a bromine precursor to overcome the above issues. These, as synthesized amine-free PNCs, showed a photoluminescence quantum yield (PLQY) of around 60 %, and the size of PNCs is ~25 nm. Moreover, these amine-free PNCs were highly stable in the colloidal solution and thin films for more than five months in ambient conditions, with 66% of its initial PLQY.

In addition, these PNCs have shown exceptional stability under different environmental conditions, with 44 % of initial PL even after 6 hours of water treatment and 28 % of initial PL under ethanol treatment for 120 minutes. Furthermore, it has exhibited excellent photostability for 96 hours and retained 36 % of its initial PL under ceaseless UV light irradiation at 365 nm (8 W/cm2). Additionally, these PNCs have good stability upon heat treatment and maintained 34 % of initial PL upon heating up to 90 ºC.

The research team has also successfully fabricated the green-emitting down-conversion LED using these amine-free PNCs. Thus, they visualize that these amine-free CsPbBr3 PNCs are perhaps the ideal candidates for perovskite-based display applications.

Read the full paper

Dr Rajapandiyan Panneerselvam, Assistant Professor, Department of Chemistry, and his team have developed a method using a portable Raman spectrometer to quickly identify six common pathogenic Vibrio species that can contaminate seafood. His latest research paper Intelligent convolution neural network-assisted SERS to realise highly accurate identification of six pathogenic Vibrio, has been published in the Q1 Nature Index journal Chemical Communications, having an Impact Factor of 6.0.

By using gold-silver nanoparticles, the study was able to accurately detect these harmful microorganisms. The new deep learning model called a convolutional neural network (CNN), outperformed traditional machine learning methods with a classification accuracy of 99.7%. The entire identification process only took 15 minutes. The researchers also discovered that the Raman signals emitted by Vibrio species are similar to signals from certain substances found in purine degradation, such as uric acid and adenine. This knowledge helps them explain why different Vibrio species produce slightly different Raman signals. Overall, the CNN-assisted Raman spectroscopy method offers a fast and accurate way to diagnose and identify harmful microorganisms responsible for food contamination.

Abstract

The utilisation of label-free Surface-Enhanced Raman Spectroscopy (SERS) technology enabled a comprehensive analysis of the connection between Raman signals emitted by pathogenic Vibrio microorganisms and purine metabolites. Through extensive research, a sophisticated Convolutional Neural Network (CNN) model was developed, demonstrating exceptional performance with an accuracy rate of 99.7% in the rapid identification of six common pathogenic Vibrio species within a mere 15-minute timeframe. This breakthrough offers a groundbreaking approach to pathogen identification, introducing a novel and efficient method to the field.

Practical Implementation of the Research

The practical implementation of label-free SERS technology combined with a deep learning CNN model enables rapid and accurate identification of pathogenic Vibrio microorganisms. This has important social implications, including improving public health and safety by quickly identifying and controlling outbreaks, enhancing food safety measures, and enabling real-time pathogen detection in resource-limited areas. The method’s speed and accuracy contribute to more informed decision-making, mitigating the spread of infectious diseases and ultimately creating a safer society.

Future Research Plans

Moving forward, future work in the field of label-free SERS technology for pathogen identification could focus on expanding the coverage to include a wider range of Vibrio species, increasing the diversity of the dataset used for training, conducting rigorous cross-validation and external validation studies, exploring integration with portable SERS devices for on-site detection, optimising the deep learning model for speed and efficiency, and investigating clinical and environmental applications. By pursuing these avenues, the research can further enhance the versatility, reliability, and real-world applicability of the method, leading to improved methods for rapid and accurate pathogen identification in various domains.

Collaborations

• Dr Jianfeng Li (College of Materials, State Key Laboratory for Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, College of Energy, School of Aerospace Engineering, Xiamen University, Xiamen 361005, China)
• Dr Lin Zhang (State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China)
• Dr Zehui Chen (Xiamen City Center for Disease Control and Prevention, Xiamen 361005, China)