ResearchSpace

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Welcome to ResearchSpace, the institutional repository of the CSIR. ResearchSpace is an open access electronic archive collecting, preserving and distributing scholarly digital materials created by the CSIR.

Recent Submissions

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2D metal oxides and their heterostructures for gas chemical sensing

(CRC Press, Taylor & Francis Group, 2024) Shingange, K; Dima, Ratshilumela S; Letsoalo, MR; Maluta, EN; Makgwane, PR; Kumar, N; Motaung, DE

This chapter argues the characteristics of two-dimensional (2D) metal oxide (MOX) and their heterostructures for application as chemical gas sensors. It is established that applying 2D MOX and their heterostructures is a favorable advance in enhancing the sensing parameters of gas sensors, including sensitivity, selectivity, and response kinetics. The experimental perspectives coupled with computational calculations perspectives are used to shed an understanding of the performance of these materials when applied as chemical gas sensors. Their weaknesses that may surface when applying them in the real application are also taken into consideration, and these weaknesses also limit their commercialization.

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Discovering HPC Resources in Africa: Empowering Collaborative Research Opportunities

(2025-07) Johnston, Bryan J; Johnston, K; Maslamoney, S

Africa faces significant challenges in accessing advanced cyberinfrastructure due to resource constraints. Consequently, the African scientific community must explore innovative approaches, such as fostering collaboration and leveraging shared resources, to overcome financial barriers to cyberinfrastructure adoption. We outline the methodology and results of a preliminary discovery survey aimed at mapping High-Performance Computing (HPC) resources across Africa to support scientific computing research. The initiative stemmed from the recognition of the critical importance of an African HPC resource catalogue in fostering research and scientific collaboration. The survey is a result of the collective efforts of diverse stakeholders to promote scientific advancement throughout the continent by generating a preliminary overview of available computational resources in Africa. The survey gathered a total of 51 completed submissions from 23 African countries, establishing a solid foundation for further exploration of existing HPC resources across Africa.

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Ethylene detection performance of Co3O4 sheet-like hierarchical structures: Experimental and DFT calculations

(2025-04) Letsoalo, MR; Dima, Ratshilumela S; Maluta, NE; Shingange, K

This study investigates the gas detection of ethylene (C2H4) using cobalt oxide (Co3O4) structures synthesized via hydrothermal method for 6, 12, and 24 hrs. X-ray diffraction (XRD) confirmed the strong crystallinity. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) revealed a sheet-like morphology forming hierarchical structures, with the surface area obtained through the Brunauer-Emmett-Teller (BET) method decreasing as the reaction duration increased. Selectivity studies conducted at 100 ◦C using 100 ppm of several gases (CH4, CO, C3H6O, C2H5OH, and C6H6) revealed distinct responses among the different Co3O4-based sensors. The Co3O4_6hrs-based sensor exhibited high selectivity for C2H4, whereas the Co3O4_12hrs-based sensor showed a strong response to C2H5OH. Additionally, the Co3O4_24hrs sensor demonstrated a high response to C6H6. Notably, the Co3O4_6hrs sensor recorded the highest overall response of 49.6 and exhibited rapid response and recovery times of 27 seconds and 42 seconds, respectively. BET and Photoluminescence (PL) analyses indicated that the superior performance of the Co3O4_6 hrs sensor was due to its high surface area and defects. Density functional theory (DFT) calculations were used to provide insights into the gas-sensing mechanisms. The calculations were performed using the Perdew–Burke–Ernzerhof (PBE) exchange–correlation functional within the generalized gradient approximation (GGA) for optimization. DFT calculations showed that the gas performance of Co3O4 towards ethylene is influenced by the physisorption gas adsorption mechanism and electron transfer process. In the future, optimizing defect engineering could further enhance the sensor performance.

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Design and applications of multi-frequency programmable metamaterials for adaptive stealth

(2025-08) Orasugh, JT; Mohanty, A; Malakar, A; Bose, S; Ray, Suprakas S

Metamaterials (MMs) are precisely designed composites with electromagnetic properties not found in natural materials, emerging as a groundbreaking technology for advanced stealth applications. This review offers a thorough synthesis of recent advancements in MM design, highlighting their exceptional capability to manipulate electromagnetic waves across microwave, infrared, and visible spectral ranges. The core principles behind MM-enabled stealth, such as negative refractive index, cloaking, and wavefront shaping are explored, showcasing their effectiveness in significantly lowering radar cross-section and thermal signatures, thus improving concealment. A detailed evaluation of nanoscale synthesis techniques, using both inorganic and organic materials, underscores the crucial importance of precise structural control to achieve these sophisticated functionalities. This work provides a comprehensive analysis of MM applications within military and aerospace stealth contexts, while also addressing contemporary challenges related to scalability, cost-effectiveness, and environmental stability. Additionally, it presents a balanced evaluation of the technology's current maturity and its prospects for near-future deployment. Beyond strategic defense uses, the transformative potential of MMs in civilian fields like transportation and communication is examined, highlighting their extensive influence on the progress of next-generation technologies. This review outlines a clear path for future research, highlighting the crucial role of MMs in advancing electromagnetic control and stealth.

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Facial expression recognition using visible and IR by early fusion of deep learning with attention mechanism

(2025-03) Naseem, MT; Lee, CS; Shahzad, T; Khan, MA; Abu-Mahfouz, Adnan MI; Ouahada, K

Facial expression recognition (FER) has garnered significant attention due to advances in artificial intelligence, particularly in applications like driver monitoring, healthcare, and human-computer interaction, which benefit from deep learning techniques. The motivation of this research is to address the challenges of accurately recognizing emotions despite variations in expressions across emotions and similarities between different expressions. In this work, we propose an early fusion approach that combines features from visible and infrared modalities using publicly accessible VIRI and NVIE databases. Initially, we developed single-modality models for visible and infrared datasets by incorporating an attention mechanism into the ResNet-18 architecture. We then extended this to a multi-modal early fusion approach using the same modified ResNet-18 with attention, achieving superior accuracy through the combination of convolutional neural network (CNN) and transfer learning (TL). Our multi-modal approach attained 84.44% accuracy on the VIRI database and 85.20% on the natural visible and infrared facial expression (NVIE) database, outperforming previous methods. These results demonstrate that our single-modal and multi-modal approaches achieve state-of-the-art performance in FER.