Bauhaus-Universität Weimar

ABSTRACT Flowchart of a model paper recycling mill water circuit, highlighting recycling, recovery, and discharge pathways. Icons indicate each unit (from http://www.flaticon.com). This study investigates nanofiltration (NF) and reverse osmosis (RO) treatment of the liquid fraction from digestate of short-fiber residues from paper recycling mills, focusing on water reuse and ammonium (NH4-N) recovery. NF removed up to 86% chemical oxygen demand (COD) and fully retained phosphorus (Ptot), but separated only 40–55% of total nitrogen (Ntot) and NH4-N. RO performed better, removing 96% COD, 86% NH4-N and Ntot, while fully retaining Ptot. RO permeate met the freshwater quality for paper recycling, with small NH4-N remaining. To improve NH4-N recovery, natural zeolite was tested with RO in three scenarios: treating the liquid fraction of digestate, RO retentate, and RO permeate. The highest NH4-N adsorption (79%, 7.8 mg/g) occurred with RO retentate, and the highest desorption (53%, 2.9 mg/g) with RO permeate. Adsorption performance varied with NH4-N concentration and pretreatment, with higher NH4-N levels leading to greater adsorption. COD was also reduced by up to 70% during adsorption. RO treatment of digestate in a model paper mill (0.7 Mt/year production capacity) could recover about 38,000 m3 of water annually, saving 71,000 €. Combined savings from water reuse and avoided digestate disposal could alone reach 0.2 million € annually.

Mineral processing may decisively influence recycled aggregate (RA) production, yet it is systematically underreported. This critical review screened 338 Scopus-indexed publications (2004–2024) and retained 204 studies after eligibility assessment. Reporting on comminution was limited: ~52% (105 studies) of studies did not explicitly mention crushing, while ~26% (53 studies) identified the crusher type, and only about 1% (two articles) reported operating conditions, which undermines reproducibility and cross-study comparability. RA quality is application-/market-dependent. The literature was classified into cement-based materials (46.1%), pavement applications (44.6%), and fundamental studies without application (9.3%). For cement-based materials, water absorption and compressive strength were the most frequently reported primary and secondary properties, respectively. For pavement applications, particle-size distribution and optimum moisture content predominated. Overall, mineral processing directly governs the primary attributes of recycled aggregates (RAs) and indirectly influences their secondary performance outcomes. The main gap identified in the literature is the lack of clear recommendations for processing procedures, which limits the reproducibility and comparability of reported results. To address this limitation, this article proposes a mineral-processing framework intended to standardize both RA processing and reporting practices, thereby improving crosslink study comparability, experimental reproducibility, and evidence-based specification according to end-use requirements.

Abstract Clay‐rich soils and sediments are key components of near‐surface systems, influencing water retention, ion exchange, and structural stability. Their complex dielectric response under moist conditions arises from surface–ion electrostatics and diffuse double layers that govern transport and retention processes. This study explores the broadband dielectric spectra (1 MHz–5 GHz) of four water‐saturated clays (kaolinite, illite, and two sodium‐activated bentonites) in a coaxial transmission‐line setup. The spectra were parameterized with two phenomenological relaxation models–the Generalized Dielectric Relaxation Model (GDR) and the Combined Permittivity‐Conductivity Model (CPCM)–and two mixture models: the Augmented Broadband Complex Dielectric Mixture Model (ABC‐M) and the Complex Refractive Index Model (CRIM). These approaches were evaluated for their ability to link dielectric relaxation behavior to key petrophysical parameters such as Cation Exchange Capacity (CEC), Volumetric Water Content (VWC), and porosity. The results demonstrate distinct spectral signatures correlating with clay mineralogy, particularly at low frequencies. Relaxation strength and apparent DC conductivity show systematic relationships with CEC, emphasizing the influence of clay‐specific surface properties. While expandable clays like bentonites displayed enhanced relaxation due to ion‐exchange dynamics, deviations in a soda‐activated bentonite highlighted the impact of chemical treatments on dielectric behavior. Overall, the study highlights both the potential and the limitations of broadband dielectric spectroscopy for soil and clay characterization. This study provides a systematic framework for linking clay mineral physics to applied electromagnetic methods. The results have significant implications for non‐invasive, frequency‐domain methods for characterizing soils and sediments, hydrological modeling, geotechnical evaluation, and environmental monitoring.

Cyclic freezing and thawing (FT) are a primary cause of cracking in concrete, yet current assessment procedures in Germany rely heavily on qualitative estimation using the International Union of Laboratories and Experts in Construction Materials, Systems and Structures (RILEM) capillary suction, internal damage and freeze-thaw (CIF) and Capillary de-icing freeze-thaw (CDF) tests. Although these standard tests provide a general overview of the condition of concrete damage in specimens through the estimation of water saturation through capillary suction, mass of surface delamination, qualitative open surface damage, and relative dynamic modulus of elasticity, they do not take quantitative analysis of voids, including cracks and air pores, directly into account. To address this, we propose a novel workflow utilizing deep learning-based semantic segmentation with Fourier-based slice-to-volume registration by combining 2D light microscopy (LM) of petrographic sections and 3D micro-computed tomography (μCT). We segment cracks, air pores, and aggregates in both modalities and employ feature matching alongside spatial harmonics analysis for 3D shape description. The best proposed 3D registration framework through feature matching demonstrated a success rate of 89.75%, achieving a dissimilarity of 5.21% in relative root mean square error (RRMSE) terms and thereby significantly surpassing the performance of compared 2D-only methods adapted from the body of research. Our approach enables precise, automated, and verifiable quantification of voids across CT and LM modalities and paves the way for advanced computational modeling-based methods to investigate moisture transfer mechanisms for more accurate assessments of frost damage in concrete, service life prediction models, deep learning applications for multimodal data fusion, and more comprehensive FT damage simulations.

Invasive brain‐computer interface (BCI) holds great promise for restoring motor, sensory, and cognitive functions in patients with disabilities, yet chronic implantation induces neuroinflammation and degeneration at the electrode–tissue interface, impairing neural connectivity and device long‐term stability. Current brain injury assessment approaches cannot simultaneously meet the requirements of efficiency and interpretability in healthcare with high‐risk diagnoses and treatment. Meanwhile, limited and expensive biomechanics data pose significant challenges in AI training. Herein, feature‐based Gaussian process emulators are proposed to enable interpretable data‐driven modeling with limited biomechanics data under noise. Furthermore, a reliable AI platform, BrainGuard is developed, for efficiently providing a reliable and quantitative patient‐specific basis and real‐time monitoring of BCI caused brain injury. These results demonstrate exceptional performance of BrainGuard in rapidly and accurately predicting and monitoring the full‐field von Mises strain revealing the brain injury even under challenging noise conditions. By constructing interpretable digital brain twins to offer reliable digital healthcare solutions, the platform enhances real‐time patient protection and improves the security and durability of long‐term BCI‐based measurement and treatment strategies.

ABSTRACT This comprehensive review evaluates Iraq's energy landscape, examining the spatial distribution of renewable and conventional resources, carbon emissions from power generation, and the technoeconomic viability of energy projects. Iraq's electricity generation is overwhelmingly dominated by thermal power plants, accounting for 96.6% of total production, while hydropower contributes 3.39% and solar only 0.059% of Iraq's overall electricity. Despite vast oil and gas reserves, the country faces chronic electricity shortages due to aging infrastructure, reliance on imports, and limited renewable adoption. Iraq possesses significant but underexploited renewable potential across hydropower, solar, wind, biomass, geothermal, wave, and blue energy. Hydropower remains dominant but is constrained by water scarcity and outdated infrastructure. On the other hand, solar and wind demonstrate strong technical and economic feasibility but face grid and financial barriers, while biomass and geothermal resources remain largely untapped. The energy transition is uneven, with CO 2 reductions in governorates such as Al‐Muthanna and Kirkuk achieved through partial fuel switching, whereas others continue to experience rising emissions from high‐carbon generation. Technoeconomic assessments underscore the competitiveness of renewables, with solar photovoltaic in Al‐Nasiriyah and Al‐Rutba yielding low‐levelized cost of energy values of 0.033–0.035 $/kWh and high‐capacity factors, and wind projects in Al‐Qaim and Rawa achieving 0.025–0.05 $/kWh. By integrating Iraq's energy challenges, renewable potential, environmental trends, and technoeconomic insights, this review provides policymakers, researchers, and investors with evidence‐based guidance to support strategic planning, targeted investments, and the adoption of technologies for a resilient, low‐carbon, and economically sustainable energy future.