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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Riverine microplastics in South Africa: Unravelling pollution sources from source to sediment

(2026-02) Malambule, NL; Kumar, A; Amoah, ID; Tyrone Moodley, T; Malla, MA; Nnadozie, CF; Thangwane, Christabel S; Kumar, S

Microplastics (MPs) are persistent environmental pollutants of growing concern, threatening aquatic ecosystems worldwide. This study examined the influence of different pollution sources on the abundance, types, and polymer composition of MPs in two South African river systems, the uMsunduzi and Swartskop Rivers. Surface water and sediment samples were collected from sites impacted by industrial, wastewater, agricultural, and urban activities. Both rivers showed high MP contamination, with the highest concentrations detected in industrial and agricultural zones. Fibers dominated the particle shapes, while polyethylene (PE) and polypropylene (PP) were the most common polymers, alongside site-specific contaminants such as polytetrafluoroethylene (PTFE). Sediments generally contained higher MP concentrations and smaller particles than surface waters. These findings highlight the role of land use in shaping MP pollution profiles and the need for targeted mitigation strategies to protect freshwater systems.

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Classical and quantum artificial intelligence and blockchain technologies for next-generation biosensing systems

(Intechopen, 2026-06) Mpofu, Kelvin T; Tsebesebe, Nkgaphe T; Manoto, Sello T; Karakuş, S

Biosensing technologies are experiencing a major transformation driven by advances in artificial intelligence, quantum computing, and distributed digital infrastructures. Classical artificial intelligence (AI) has already demonstrated significant value in biosensing through improved signal processing, pattern recognition, and data-driven diagnostics. However, the increasing complexity of biosensor data, together with challenges such as limited datasets, noise, and real-time decision-making requirements, highlights the limitations of purely classical approaches. Quantum artificial intelligence (QAI), which combines quantum principles such as superposition, entanglement, and hybrid quantum–classical learning, offers a promising alternative by enabling improved feature extraction and enhanced performance in data-constrained environments. In parallel, blockchain technology introduces a decentralized and secure framework for managing biosensing data, supporting integrity, traceability, and trust in distributed diagnostic and monitoring systems. This chapter presents a comprehensive overview of classical AI, quantum AI, and blockchain technologies as applied to next-generation biosensing systems. It discusses their individual roles, synergies, and integration into unified architectures for secure, intelligent, and resilient biosensing. Applications in healthcare diagnostics, environmental monitoring, and point-of-care testing are highlighted, alongside key challenges, ethical considerations, and future research directions toward quantum-ready and trustworthy biosensing ecosystems. In addition, the chapter considers how these technologies may support the development of more adaptive biosensing platforms capable of learning from diverse data sources, operating across decentralized healthcare environments, and maintaining data security throughout the diagnostic workflow. Emphasis is placed on the importance of interdisciplinary design, where biosensor engineering, quantum machine learning, quantum information science, blockchain and biomedical development converge to address practical diagnostic challenges.

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The Evolution of Southern Ocean Net Primary Production in a Changing Climate: Challenges and Opportunities

(2025-12) Tagliabue, A; Thomas Ryan-Keogh, Thomas; Baker, A; Bibby, TS; Follett, C; Lohan, MC; Naveira-Garabato, A; Mayor, DJ; Milne, A; Moore, CM; Ussher, S

Net primary production in the Southern Ocean plays a critical role in governing ecosystem production, the biological carbon pump, and global biogeochemical cycles. Recent work has advanced our understanding of novel factors regulating Southern Ocean net primary production and the regional physiological adaptations employed by Southern Ocean phytoplankton in terms of their photosynthetic strategies and resource acquisition. Here we assess trends in Southern Ocean net primary production from different remote sensing algorithms and bgc-Argo floats to compare them to the latest Earth System Models used to forecast future changes under three different future climate scenarios. Overall, remote sensing and bgc-Argo floats indicate net primary productivity in the Southern Ocean is declining at basin scale. This contrasts with the Earth System Models that display muted contemporary trends and consistent increases in net primary production that are relatively robust across SSP2-45, SSP3-70, and SS5-85. This mismatch in trends suggests low confidence in these projected net primary production changes, with implications for assessments of changes in ecosystem services. Despite their coherence in terms of net primary production trends, Earth System Models show large disagreement in the relative role of different drivers, suggesting we lack sufficient mechanistic understanding. Improved knowledge of the role of manganese alongside iron and the coupled responses of phytoplankton and zooplankton through the integration of observations and experiments into a new generation of models is necessary to deliver confident forecasts of Southern Ocean ecosystem change. Advancing knowledge in these areas is an important priority for future research in the region and provides context for policy discussions around the marine protection of Antarctic ecosystems that depend on sufficiently confident projections of climate change impacts.

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Evaluating the reliability of LLMs in OSINT investigations: A friend or foe?

(2026-06) Baloyi, Errol; Siphambili, Nokuthaba; Ntshangase, Ntomfuthi L; Letshwenyo, Mpho; Makharamedzha, Fhatuwani; Mmbodi, Rendani; Hlongwane, Ndabezinhle E

This study investigates the use of Large Language Models (LLMs), including GPT, Claude 3, Gemini, Meta, DeepSeek, Qwen 2.5, Mistral Large, and Grok, in Open-Source Intelligence (OSINT) investigations, focusing on their capabilities, limitations, and practical implications. Using a controlled fictional organization, the CtrlZ Society, to simulate a plausible online footprint, the LLMs were provided with a synthetic dataset purportedly linked to the organization. The dataset included social media posts, a Reddit thread, a Pastebin document, a GitHub repository, a blog post, a Telegram broadcast, a WHOIS record, and a news article. Based on this dataset, the models were evaluated on accuracy, timeline reconstruction, account attribution, evidence traceability, susceptibility to hallucination, and handling of ambiguity and incomplete information. Results revealed substantial variation among models: Claude 3, GPT, and Qwen 2.5 demonstrated strong analytical performance and reliable synthesis of investigative outputs, while Gemini and DeepSeek exhibited weaker capabilities. Some models, including Meta were also prone to forced narrative construction when prompted adversarially, highlighting risks of misinterpretation or overreach. Despite these limitations, all LLMs provided valuable support for structuring and summarising complex data, demonstrating their potential as efficiency multipliers in OSINT workflows. Based on these findings, the study provides recommendations for practitioners, including rigorous human oversight, multi-model validation, adherence to verification protocols, and careful evaluation of outputs to mitigate risks and maximise the reliability of LLM-assisted investigations.

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A global comparison of net-zero carbon building practices

(2026-07) Senaratne, S; Nadeeshani, M; Perera, S; Domingo, N; Lombardi, P; Moghadam, ST; Van Reenen, Coralie A; Lai, JHK; Chan, DWM; Mallawaarachchi, H

Despite extensive research on individual net-zero carbon building (NZCB) practices, prior studies fail to systematically map global adoption patterns across diverse regulatory and market contexts. This study addresses this gap by investigating current NZCB practices worldwide. A comprehensive list of global NZCB practices, with associated enablers and barriers, was first identified through a literature review. A global survey was then employed to explore the practical adoption of the practices. Six countries/ regions participated in the survey: Australia, New Zealand, Italy, South Africa, Sri Lanka and Hong Kong. Survey data were analysed using descriptive and frequency analysis. The study identifies three empirically derived adoption patterns: common, varied, and low across countries/regions. Use of solar photovoltaics, energy-efficient HVAC and modular systems were found to be commonly adopted, while advanced technologies like carbon-infused materials were least adopted. Lack of direct financial incentives emerged as the key global barrier, while robust government policies were the main enabler for NZCB adoption worldwide. These findings provide empirical benchmarks for cross-country NZCB adoption and actionable strategies to accelerate low-adoption practices globally.