CSIRO Manufacturing

Superhydrophobic thermally rearranged (TR) nanofibre membranes are developed for membrane distillation (MD) to enable energy-efficient water desalination. Hydrophobic activated carbon (AC) particles are incorporated into porous nanofibre membranes to enhance water vapour transport by enhancing membrane hydrophobicity through favourable interactions with non-polar organic species. The composite membranes are fabricated via electrospinning of polyimide precursors with AC particles, followed by thermal conversion to form TR nanofibre membranes. This conversion rearranges the polymer backbone into polybenzoxazole, inducing robust superhydrophobicity and chemical stability. The resulting TR nanofibre membranes exhibited improved wetting resistance, enhanced desalination performance, and potential scalability for sustainable water treatment. Notably, the composite nanofibre membranes containing 3.0 wt% of AC (TR-AC-3.0) achieved a high water flux of 76.0 kg m −2 h −1 with a salt rejection rate of 99.99% when tested using a simulated seawater (3.5 wt% NaCl) at 70 °C in direct contact MD. Our approach shows a promising pathway toward designing high efficiency and durable MD membranes that address global challenges in freshwater production. • Superhydrophobic thermally rearranged nanofibre membranes are developed for direct contact membrane distillation. • Activated carbon embedded in nanofibres enhances membrane hydrophobicity, increasing water flux. • At higher AC loadings, performance declines due to reduced porosity by excess particles.

Toroidal moments in single‐molecular toroics (SMTs) are promising degrees of freedom for next‐generation qubits that are protected from weak, short‐range magnetic interactions, with immediate application to quantum computation, molecular spintronics and information storage. While the intramolecular entanglement of toroidal moments has been achieved by bridging triangular Dy SMT moieties with ions, an analogous coupling via ions has been only partially explored. Here, we utilise a joint computational and theoretical approach to study two previously synthesised heterometallic complexes, CuDy and CuDy, comprised of two and four triangular Dy magnetic subunits, respectively, bridged by trivalent dysprosium ions. Using multiconfigurational ab initio calculations in conjunction with a microscopic model, we reproduce the experimentally observed magnetic responses and determine the ground and excited state spin textures of each molecule. Unlike the MLn systems studied previously in the literature, the maximal toroidal moments in CuDy are hosted by high‐lying excited states and are delocalised across both SMT moieties, forming a planar, quadrilateral magnetic vortex configuration perpendicular to the triangles’ pseudo‐C symmetry axis. These unconventional excited states also exist in the larger complex, CuDy and strongly couple in ferrotoroidic and antiferrotoroidic configurations with coupling constants of unprecedented magnitude.

The extent to which microbial processes control soil organic carbon (SOC) dynamics remains uncertain. Carbon use efficiency (CUE), that is, the fraction of assimilated carbon allocated to growth, has been used as a key parameter but its relationship with SOC reflects carbon partitioning rather than the absolute magnitude of microbial fluxes. The microbial growth rate could provide a more mechanistic link to SOC accumulation because it quantifies biomass production and reflects necromass formation. Here we combine a global ¹⁸O-H2O dataset (n = 268 paired observations) with outputs from four land surface models to test whether growth rate predicts SOC more strongly than CUE. In the incubation experiments, growth rates are more closely associated with SOC than CUE, although soil properties and climate explain equal or greater variance. Models reproduce the stronger role of growth rate over CUE but tend to underestimate the abiotic controls. The models also emphasize CUE as the main predictor of the SOC-to-net primary production ratio, in contrast to observations, which indicates the soil's capacity to retain plant carbon inputs. Together, these findings identify the microbial growth rate as a diagnostic that can help bridge models with empirical data and guide a more balanced representation of microbial and mineral controls in SOC projections.

Composite membranes between polymeric membranes and inorganic substrates have been widely investigated for gas separation applications. Soft polymeric coatings on hard substrates are favourable due to the high mechanical strength imparted by the hard substrates and the high separation capacity conferred by the thin polymeric selective layer. To achieve these desirable properties whilst ensuring that the membrane manufacturing process is scalable and reproducible, this study systematically investigated the design of a vacuum-assisted, dip-coating process for gutter-free, tubular membranes as a function of vacuum pressure and drying temperature to produce a thin film polyetherimide (PEI) selective layer on an alumina substrate for gas molecular-sieving applications. Well-defined PEI alumina membranes were discovered for specific sets of synthesis conditions pertaining to a low vacuum (> −1 bar) dip-coating and drying (110 °C) film consolidation. The composite PEI membrane demonstrated interlayer confinement effect within the selective layer upon second deposition further improving the CO 2 /N 2 permselectivity from 1 to 21.9 ± 0.9 and CO 2 /CH 4 permselectivity from 1 to 20.5 ± 1.4 with a pore aperture of 4.6 ± 0.1 Å allowing He, H 2 , and CO 2 passage. The findings of this study demonstrated important correlations between the vacuum-assisted dip-coating, drying parameter and gas separation performance in controlling the membrane sieving property for He, H 2 , and CO 2 in N 2 and CH 4 permeations. • Vacuum-assisted, dip-coating process resulted well-defined PEI membrane structure. • Membrane pore aperture of 4.6 ± 0.1 Å allowing He, H 2 , and CO 2 passage. • Best membranes achieved He/CH 4 permselectivity of 79.5, H 2 /N 2 of 76.4, and CO 2 /CH 4 of 20.5. • Excellent gas permeation consistency demonstrating membrane reproducibility and scalability.

Abstract Quantum machine learning (QML) is an emerging field that promises advantages such as faster training, improved reliability and superior feature extraction over classical counterparts. However, its implementation on quantum hardware is challenging due to the noise inherent in these systems, necessitating the use of quantum error correction (QEC) codes. Current QML research remains primarily theoretical, often assuming noise-free environments and offering little insight into the integration of QEC with QML implementations. To address this, we investigate the performance of a simple, parity-classifying Variational Quantum Classifier (VQC) implemented with the [[4,2,2]] error-detecting stabiliser code in a simulated noisy environment. To our knowledge, this is the first implementation of a stabiliser-based error detection code to a QML algorithm. We invoke ancilla qubits to logically encode rotation gates, and classically simulate the logically-encoded VQC under two simple noise models representing gate noise and environmental noise. We demonstrate that the stabiliser code improves the training accuracy at convergence compared to noisy implementations without error detection. However, we find that the effectiveness and reliability of error detection is contingent upon keeping the ancilla qubit error rates below a specific threshold, due to the propagation of ancilla errors to the physical qubits. Our results provide an important insight that extends to QML implemented with full error-correction: when the QEC code requires ancilla qubits for logical rotations but cannot fully correct errors propagated between ancilla and physical qubits, the maximum achievable accuracy of the QML model is constrained. This highlights the need for additional error correction or mitigation strategies to support the practical implementation of QML algorithms with QEC on quantum devices.

The 1997–2009 Millennium drought changed the rainfall-runoff response across large parts of Victoria in south-eastern Australia. This study investigates the hydrological non-stationarity in Victoria, using water balance analysis and a modelling experiment with observed hydroclimate and remotely sensed data over 1982–2020 from 120 catchments. Most catchments in Victoria exhibit hydrological non-stationarity, where there is less streamflow generated during, and for many catchments even after, the drought from the same amount of annual rainfall. This can be partly explained by the low percentage reduction in actual evapotranspiration (AET) during the drought compared to the percentage reduction in rainfall, causing the large reduction in streamflow which is a small difference between rainfall minus AET. There is also an underlying greening trend that has been only partly stalled during the Millennium drought. The AET is higher in the post-drought period compared to the pre-drought period, suggesting that the proportion of rainfall that becomes streamflow may continue to decline if the greening trend continues. The observed hydrological non-stationarity is much more significant in North-West Victoria, where the 15% lower mean annual rainfall over the Millennium drought translated to 80% reduction in mean annual streamflow. This can be partly explained by the lower rainfall and low runoff coefficient in the region. The catchments in North-West Victoria have not fully recovered from the Millennium drought. Rainfall-runoff models that consider multi-year lags in the rainfall, as well as improvements in the AET conceptualisation and baseflow generation only above a subsurface store threshold, also simulates runoff much more robustly in North-West Victoria, compared to elsewhere in Victoria. The water balance analysis and modelling exploration suggest that the hydrological regime in North-West Victoria may have shifted, and it may be more prudent to use the post-1997 hydrological baseline for modelling and water resources planning for the region.

Direct air capture (DAC) is crucial for mitigating climate change by directly removing CO2 from the atmosphere. However, the large-scale deployment of DAC is hindered by the lack of durable...

Abstract The collection of contributions from the Leibniz Institute for Plasma Science and Technology presents findings from experimental and modelling studies of the physical and chemical processes in non-thermal and thermal plasmas produced by various kinds of plasma sources. The characterisation and properties of micro-scale discharges and suitable gases for switching technologies are explored in view of their applications. Contributions to plasma medicine as an innovative research field focus on cold atmospheric-pressure plasma sources for specific biomedical applications. The development and optimisation of plasma-based processes is considered in works focused on bioeconomy and waste treatment. Finally, a modern concept of research data management for low-temperature plasmas demonstrates the digital workflows being developed to link metadata standards, laboratory notebooks and data repositories.

Polyesters are widely used plastics that persist in the environment due to their resistance to degradation. Microbial polyesterases potentially offer recycling and remediation solutions. However, most polyesterases lack the thermostability and catalytic efficiency required for practical application. Here, we have identified and characterized thermostable bacterial polyesterases from an undercharacterized subfamily of SGNH‐hydrolases (related to PpEST from Pseudomonas oleovorans) and uncovered a previously unreported thermal stabilization mechanism mediated by conformationally flexible N‐terminal regions with features of intrinsically disordered regions. Biochemical assays, structural analysis, small angle X‐ray scattering, X‐ray crystallography, and molecular dynamics simulations suggest that these flexible N‐terminal regions enhance thermal resilience without affecting the catalytic rate or oligomerization in some homologs, while they promote oligomerization and reduce kcat in others. These findings suggest that flexible terminal regions can act as modular stabilizing elements through diverse mechanisms. Our work provides mechanistic insight into an unusual route to protein thermostability, expanding strategies for enzyme engineering, and contributing to the development of robust biocatalysts for polyester degradation.

Abstract. The accuracy of soil organic carbon (SOC) models and their ability to capture the relationship between SOC and environmental variables are critical for reducing uncertainties in future projection of soil carbon balance. In this study, we evaluate the performance of two state-of-the-art process-based SOC models, the vertically resolved MIcrobial-MIneral Carbon Stabilisation (MIMICS) and the Microbial Explicit Soil Carbon (MES-C) model, against a machine learning (ML) approach on predicting global SOC content. By applying multiple interpretable ML methods, we find that the poor performance of the two process-based models is associated both with the missing of key variables, and the underrepresentation of the role of existing variables such as net primary production (NPP). Soil cation exchange capacity (CEC) is identified as an important predictor missing from process-based models, and soil texture is given more importance in models than indicated by ML results. Although the overall relationships between SOC and individual predictors are reasonably captured, the varying sensitivity across entire predictor range is not replicated by process-based models, most notably for NPP. Observations exhibit a nonlinear relationship between NPP and SOC while models show a monotonic positive trend. Additionally, MES-C largely diminishes interacting effects of variable pairs, whereas MIMICS produces mismatches relating to the interactions between NPP and both soil temperature and moisture. Process-based models also fail to reproduce the interactions among soil moisture, soil texture, and soil pH, hindering our understanding on SOC stabilisation and destabilisation processes. Our study highlights the importance in improving the representation of environmental variables in process-based models to achieve a more accurate projection of SOC under future climate conditions.