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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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Work Package 2: Strategic Environmental Assessment for the proposed Boegoebaai Port, Special Economic Zone and Namakwa Region.

(CSIR, 2026-06) Schreiner, Gregory O; Mqokeli, Babalwa R; Snyman-Van der Walt, Luanita; Lochner, Paul A; Tsedu, Rinae

Green hydrogen (GH2), and its derivative Power-to-X (PtX) products, such as green ammonia and green methanol, could assist South Africa’s transition to a low-carbon economy by decarbonising hard-to-abate sectors and catalysing regional development. The Northern Cape Green Hydrogen Masterplan (NCGHM, 2023) proposes that the Northern Cape is well positioned to lead this transition, given its’ globally competitive solar and wind resources, large tracts of land, and a 300 km coastline where a deepwater port at Boegoebaai is being investigated. The province’s ambition includes 5 gigawatts (GW) of electrolysers supported by 10 GW of renewables by 2033, scaling to 40 GW of electrolysers by 2050. The realisation of this ambition will require extensive infrastructure development across multiple municipalities and ecosystems, including energy generation sites, transmission corridors, desalination plant/s, and transport networks. These expansive developments could potentially intersect with sensitive ecological systems, heritage landscapes, and socio-economic dynamics across the Namakwa District. For this reason, a Strategic Environmental Assessment (SEA) was initiated through a collaboration between the South African National Energy Development Institute (SANEDI), the Northern Cape Economic Development, Trade and Investment Promotion Agency (NCEDA) and Transnet National Ports Authority (TNPA). The Council for Scientific and Industrial Research (CSIR) was appointed to lead and coordinate an independent SEA process, drawing on established expertise in SEA, renewable energy, port planning and GH.

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Cannabis sativa L. biomass valorization as a strategic bioresource for South Africa’s circular bioeconomy: Integrating biorefinery pathways and green extraction

(2026-07) Motsa, Vuiswa L; Seedat, N; Mekuto, L; Sekoa, P

South Africa’s legalized Cannabis Sativa L. industry generates substantial biomass waste, including post-harvest materials such as stalks, leaves, roots, and seeds, as well as post-extraction residues rich in lignocellulosic components (cellulose, lignin, hemicellulose) and functional compounds (fibers, phenolic compounds, and proteins) which remain largely unvalorized. This review critically and systematically examines valorization pathways for Cannabis sativa L. biomass within the context of a circular bioeconomy, focusing on its potential as a bioresource amid South Africa’s evolving regulatory, agricultural, and industrial landscape. A systematic scoping review of 65 peer-reviewed studies (2018–2025) was conducted using international databases to evaluate biochemical and thermochemical conversion, integrated biorefinery strategies, and green extraction methods such as supercritical CO2 extraction, microwave-assisted extraction (MAE), and ultrasound-assisted extraction (UAE). Compositional analysis indicates that lignocellulosic fractions from Cannabis sativa L., particularly bast fibers and hurds, are well-suited to cascaded biorefinery applications, enabling the recovery of cannabinoids, carbon-based materials, and bioenergy. However, several challenges persist, including biomass recalcitrance to enzymatic hydrolysis, inconsistent feedstock availability, and the lack of a standardized protocol within South Africa’s regulated environment. Techno-economic assessments (TEA) highlight the need for financial incentives, decentralized infrastructure, and cohesive policies among government entities, including DALRRD, SAHPRA, and DTIC. The review proposes a South African collaboration framework linking policy with research-based process design. Key gaps include the need for life cycle assessment (LCA) and TEA validation at the pilot scale, characterization of landrace cultivars, and development of accessible pretreatment technologies for smallholders.

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A unified framework for secure and interoperable digital identity in South Africa

(2026-05) Mthethwa, Sthembile N; Ntshangase, Cynthia S; Myaka, Zanele S; Ndhlovu, Nomalisa; Lefophane, Samuel

Digital identity represents a technological advancement facilitating the secure access and management of identities for both individuals and organisations within digital environments. Despite considerable progress in digital identity systems over the past decade, their implementation has been hindered by challenges, including fragmentation, lack of interoperability, and inadequate governance framework. This study analyses global digital identity frameworks to identify best practices and opportunities for accelerating adoption, focusing on key principles such as privacy protection, trust, inclusivity, and cross-border interoperability. Additionally, it outlines the essential requirements for a well-governed digital identity ecosystem and proposes a national digital identity framework consistent with international standards, aimed at enhancing the security, resilience, and global recognition digital identities issued in South African.

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Deep learning for carotid Doppler spectra classification

(2026-04) Bhikha, Charita B; Dhuness, Kahesh; Mennen, M; Jamieson-Luff, N; Ntusi, NAB; Wheatley, Richard E

Cardiovascular disease (CVD) remains a global health challenge, with limited specialist access in low- and middle-income countries hindering early detection. Carotid Doppler ultrasound offers promise for screening in non-specialist settings. However, spectral Doppler lacks the anatomical context provided by duplex ultrasound, making it challenging to determine which carotid vessel is being assessed. This study focuses on accurately identifying signals from the common, internal, and external carotid arteries (CCA, ICA, and ECA) based solely on Doppler spectra. This forms a critical step for subsequent disease classification. A clinical study enrolled 398 participants who underwent bilateral carotid Doppler examination (198 healthy controls, 200 with CVD). Several classifiers were evaluated including i) five deep convolutional neural networks (CNN) utilizing transfer learning on spectral images, and ii) conventional machine learning classifiers applied to the maximum frequency envelope and extracted features. The best performing classifier was a CNN (GoogLeNet) which achieved a mean area under the curve (AUC) of 0.929, effectively distinguishing between carotid artery segments. It exhibited f1-scores of 0.830, 0.803 and 0.764 for the ICA, ECA and CCA, respectively. Explainable AI tools (GradCAM and LIME) provide intuitive visual insights into these predictions. This study addresses a previously unsolved problem of automated carotid artery segment identification using spectral Doppler waveforms alone. When incorporated into automated screening tools, this approach provides a low-cost, specialist-independent pathway for earlier CVD detection, particularly suited to resource-constrained environments.

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Modelling of spatially misaligned wastewater-based surveillance data

(2026-08) Torresa, M; Docrata, R; Rose, D; Le Roux, Wouter J; Schaefer, Lisa M; Jele, Jabulani; Dudeni-Tlhone, Nontembeko; Holloway, Jennifer P; Debba, Pravesh; Ludick, Chantel J

Wastewater-based epidemiology (WBE) has emerged as a promising approach to infectious disease modelling and early detection of disease outbreaks in general. Herein, the application of this approach to COVID-19 is explored through spatio-temporal models. The goal of predicting COVID-19 cases at a small administrative area level (sub-place) while using data collected at catchment area level introduces the issue of spatial misalignment. Spatial misalignment in the data must be accounted for by the modelling process, as is the case for many spatial disease models. Appropriate handling of the data requires disaggregation and matching of different spatial regions. The models are developed in a Bayesian framework using the INLA package in R, which is particularly intuitive in the context of latent Gaussian mixed models (LGMM). Misalignment is explored using a distance based approach which incorporates uncertainty as a component of the LGMM. COVID-19 cases were modelled at sub-place level and spatio-temporal trends were explored. The best performing model was able to utilise a link between the wastewater data and the COVID-19 cases. The modelling approach deals with data available at different spatial resolution, data covering misaligned and overlapping spatial areas, disaggregated and missing data, thereby accounting for complexity of real data. The methodology provides a novel structure to deal with the intricacy of the sampled wastewater and related data, and poses a proof of concept for wastewater-based spatial modelling for disease surveillance.