196746 research outputs found
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Leveraging 3D printing in microbial electrochemistry research:current progress and future opportunities
No full textMicrobial electrochemical system (MES) offers sustainable solutions for environmental applications such as wastewater treatment, energy generation, and chemical synthesis by leveraging microbial metabolism and electrochemical processes. This review explores the transformative role of 3D printing in MES research, focusing on reactor body design, electrode fabrication, and bioprinting applications. Rapid prototyping facilitated by 3D printing expedites MES development while unlocking design flexibility, which enhances performance in optimising fluid dynamics and mass transfer efficiency. Tailored ink materials further improve the conductivity and biocompatibility of electrodes, paving the way for environmental applications. 3D-printed bio-anodes and bio-cathodes offer enhanced electrogenesis and boosted electron acceptance processes, respectively, by fine-tuning electrode architectures. Additionally, 3D bioprinting presents opportunities for scaffold fabrication and bioink formulation, enhancing biofilm stability and electron transfer efficiency. Despite current challenges, including material selection and cost, the integration of 3D printing in MES holds immense promise for advancing energy generation, wastewater treatment, resource recovery, carbon utilisation, and biosensing technologies
Estimating Linear Joint Stiffness and Damping Using a Frequency-Based Optimization Framework and the Emerging Concept of DyDis
No full textAccurate estimation of stiffness and damping of linear bolted joints is a challenge. Commonly employed approaches using frequency-based substructuring techniques can fail if the measurement quality is not good enough. Even a minimal noise level can significantly impact the accuracy of estimations due to matrix inversions. This study proposes a novel approach that combines an optimization framework utilizing frequency-based substructuring with the emerging concept of Dynamic Disturbance (DyDis) to enhance the robustness of linear bolted joint parameter estimation.</p
Comparison of rigorous scattering models to accurately replicate the behaviour of scattered electromagnetic waves in optical surface metrology
No full textRigorous scattering models are based on Maxwell's equations and can provide high-accuracy solutions to model electromagnetic wave scattering from objects. Being able to calculate the scattered field from any surface geometry and considering the effect of the polarisation of the incident light, make rigorous models the most promising tools for complex light-matter interaction problems. The total intensity of the electric near-field scattering from a silicon cylinder illuminated by the transverse electric and transverse magnetic polarisation of the incident light is obtained using various rigorous models including, the local field Fourier modal method, boundary element method and finite element method. The intensity of the total electric near-field obtained by these rigorous models is compared using the Mie solution as a reference for both polarisation modes of the incident light. Additionally, the intensity of the total electric near-field scattered from a silicon sinusoid profile using the same rigorous models is analysed. The results are discussed in detail, and for the cylinder, the deviations in the intensity of the total electric field from the exact Mie solution are investigated.</p
Strong current in carbon nanoconductors:Mechanical and magnetic stability
No full textCarbon nanoconductors are known to have extraordinary mechanical strength and interesting magnetic properties. Moreover, nanoconductors based on one- or two-dimensional carbon allotropes display a very high current-carrying capacity and ballistic transport. Here, we employ a recent, simple approach based on density functional theory to analyze the impact of strong current on the mechanical and magnetic properties of carbon nanoconductors. We find that the influence of the current itself on the bond-strength of carbon in general is remarkably low compared to e.g. typical metals. This is demonstrated for carbon chains, carbon nanotubes, graphene and polyacetylene. We can trace this to the strong binding and electronic bandstructure. On the other hand, we find that the current significantly change the magnetic properties. In particular, we find that currents in graphene zig-zag edge states quench the magnetism.</p
Validation of four resistivity mixing models on field time lapse geoelectrical measurements from fine-grained soil undergoing freeze-thaw cycles
No full textResistivity mixing models relate porosity, phase composition and specific resistivities of ground materials to their bulk (effective) electrical properties. These models were typically derived for calculating hydrocarbon saturation from geophysical logs. In permafrost monitoring applications, they have been used to link ground electrical response to its phase composition, with focus on unfrozen water vs. ice content, and to derive changes in ground ice content from repeated resistivity acquisitions. Such quantitative interpretations rely on validity of the mixing models in a context different from the one they were derived in. To increase the reliability of the permafrost forecasts that are based on repeated resistivity surveys, we undertook validation of four selected resistivity mixing model formulations: i) the original Archie's law, ii) the Archie's law with an ice-content dependent cementation exponent m (Archie-M), iii) a modification of the Archie's law for multiple conducting phases (Archie-N), and iv) the geometric mean model (GM). The model application context was permafrost monitoring and fate forecasting on natural fine-grained soil undergoing cycles of freezing and thawing, based on indirect (geophysical), in-situ time-lapse resistivity measurements. The purpose of the calibrated resistivity models was to derive the phase composition of the ground from in-situ resistivity measurements, with acceptable quantitative reliability, notably with respect to the amount and changes of ice and water content. In our validation framework, daily temperature-dependent soil phase distribution was converted into an effective resistivity distribution of the ground using each of the four resistivity mixing models. From the effective resistivity model, an apparent resistivity response was forward calculated and compared to time-lapse field apparent resistivity measurements from a permafrost monitoring field site. The performance metrics were i) the root mean square error between the forward-calculated and field-measured apparent resistivities throughout the freeze-thaw season, ii) the percentage of field apparent resistivity data explained by each resistivity model, and iii) the plausibility of the calibrated model parameter estimates. We found that despite different current conducting mechanisms involved in each of the resistivity mixing model formulations, the quantitative performance of the four evaluated models was very similar. The four models typically reproduced the field-measured resistivity variations within one to two standard deviations (STD) of the field measurements, depending on the time of the year and depth in the soil profile. In the active layer, the Archie-M model most consistently reproduced the field data within 1 STD throughout the freezing and frozen periods of the year (September – May). Meanwhile, the GM best matched the actual values of resistivities during freezing. The GM also recovered porosities of the three model layers close to the true values measured on borehole samples. All the tested models were challenged by accurately simulating the thawing period – overestimating resistivities in the temperature range from −5 °C to −2 °C and underestimating them between −2 °C and thawing point. Consequently, the choice between the models should depend on the specifics of a particular application, such as available calibration data, desired parameters or ground properties to resolve, sensitivity of the modeling framework etc. An application-specific validation of several resistivity mixing models and quantification of performance of the chosen resistivity model may be called for. Additionally, the possibility of using different mixing model and water content parameterizations should be investigated, to adequately address complex ground resistivity structures and phase change processes typical of permafrost ground.</p
Quantification of contaminant mass discharge and uncertainties:Method and challenges in application at contaminated sites
No full textContaminant mass discharge (CMD) estimation involves combining multilevel concentration and flow measurements to quantify the contaminant mass passing through a control plane downgradient of a point source. However, geological heterogeneities and limited data introduce uncertainties that complicate CMD estimation and risk assessment. Although CMD is increasingly used in groundwater management, methods for quantifying and handling these uncertainties are still needed. This study develops and tests a CMD estimation method based on Bayesian geostatistics to quantify CMD uncertainties using data from a control plane perpendicular to the contaminant plume. By combining geostatistical conditional simulations of the spatial concentration distribution with the flow, an ensemble of CMD realizations is generated, from which a cumulative distribution function is derived. A key element of this approach is the use of a macrodispersive transport model to simulate the spatial concentration trend. This ensures that the estimated concentration reflects the expected physical behavior of the contaminant plume while also allowing the integration of site-specific conceptual information. The method is applicable to plumes with dissolved contaminants, such as chlorinated solvents, petroleum hydrocarbons, Per- and polyfluoroalkyl substances (PFAS) and pesticides. Site-specific conceptual understanding is used to inform the prior probability density functions of the structural model parameters and to define acceptable simulated concentration limits. We applied the method at three sites contaminated with chlorinated ethenes, demonstrating its robustness across varying information levels and data availability. Our results shows that strong site-specific conceptual knowledge and high sampling density constrain the CMD uncertainty (CV = 21 %) and results in estimated model parameters and a spatial concentration distribution that agrees well with the conceptual model. For a site with less data and limited conceptual knowledge, CMD and concentration distribution estimates are still feasible, though with higher uncertainty (CV = 41 %). Extending the method to account for multiple source zones and complex plume migration improved parameter identification and reduced the 95 % CMD confidence interval by 11 % ([4950–8750] to [5090–8480] g yr−1), while also providing a spatial concentration distribution in better agreement with the plume conceptualization. This study highlights the importance of integrating site-specific conceptual knowledge in CMD estimation, particularly for less-sampled sites. The method can furthermore assist in identifying remediation targets, evaluating remedial effectiveness, and optimizing sampling strategies
Understanding clogging mechanisms in filter media:An integration of laboratory findings and theoretical perspectives
No full textThis paper examines the intricate dynamics of clogging mechanisms in high infiltration rate filter media. Filtration, a fundamental process for removing solid particles from liquids and gases, plays a crucial role in industries such as petroleum and water treatment. Clogging, which significantly affects hydraulic performance, involves the physical blockage of media pores by suspended matter, often necessitating the periodic regeneration of filters. Through laboratory experiments, this study reviews and conceptualizes how suspended particles influence clogging in filters. The results reveal that filtration pressure has minimal influence on the initial spurt loss, where particle size, pore size, and feed density are the dominant factors. Numerical simulations, corroborated by experimental observations, show that pores smaller than the mean particle size in the fluid clog immediately, while larger pores clog progressively. This research contributes to a deeper understanding of predictive models for permeability variations and offers a refined view of clogging processes, aiming to enhance the design and effectiveness of filtration systems
Rebuilding and Reference Points Under Compensatory and Depensatory Recruitment: A Meta-Analysis of Northeast Atlantic Fish Stocks
No full textModern management of fish stocks is based on integrating the precautionary approach with the maximum sustainable yield framework. It relies on accurate estimation of precautionary limits, defined as levels of spawning biomass where a stock has reduced reproductive capacity, and harvesting targets aimed to maximise future yields. Therefore, it is heavily depending on productivity assumptions. Most fish stocks are managed assuming that productivity will increase as the stock size decreases (i.e., density dependent compensatory stock and recruitment relationship). However, several biological and ecological processes will result in a decreased productivity below a certain population size, referred to as the Allee effect or depensation. Through a meta-analysis of 81 Northeast Atlantic fish stocks, we investigated the impact of assuming compensatory recruitment in the presence of depensation in fisheries management. Across life histories, depensation results in a 22 the maximum reproductive rate per spawning biomass was found at 35 which was also the biomass where stocks have a 5 the presence of depensation resulted in increased rebuilding times when stock spawning biomass falls below the limit reference point. When depensatory effects are present, assuming increasing productivity at low biomass will generally result in over-optimistic perceptions of rebuilding and stock status at biomass below 255 and for pelagic stocks respectively. When not accounted for, depensation will potentially lead to unsustainable harvesting practices of marine living resources
A comparison between black-, gray- and white-box modeling for the bidirectional Raman amplifier optimization
No full textDesigning and optimizing optical amplifiers to maximize system performance is becoming increasingly important as optical communication systems strive to increase throughput. Offline optimization of optical amplifiers relies on models ranging from white-box models deeply rooted in physics to black-box data-driven and physics-agnostic models. Here, we compare the capabilities of white-, gray- and black-box models on the challenging test case of optimizing a bidirectional distributed Raman amplifier to achieve a target frequency-distance signal power profile. We show that any of the studied methods can achieve similar frequency and distance flatness of between 1 and 3.6 dB (depending on the definition of flatness) over the C-band in an 80-km span. Then, we discuss the models’ applicability, advantages, and drawbacks based on the target application scenario, in particular in terms of flexibility, optimization speed, and access to training data.</p
First detection of infectious haematopoietic necrosis virus in farmed rainbow trout in North Macedonia
No full textInfectious haematopoietic necrosis virus (IHNV) is the causative agent of infectious haematopoietic necrosis (IHN), a disease of salmonids responsible for great economic losses. The disease occurs in most parts of the world where rainbow trout is reared but has not been previously reported in North Macedonia. In this study, 150 pooled samples in total, each consisting of organ mix of 10 freshly killed rainbow trout Oncorhynchus mykiss, were collected from 50 trout farms by the Food and Veterinary Agency of North Macedonia as part of the annual surveillance plan for IHN and viral haemorrhagic septicaemia (VHS) control. Screening of samples was done by cell culture and real-time RT-PCR (qRT-PCR). All 150 tested samples were VHS virus (VHSV) qRT-PCR negative. Two samples from different trout farms were IHNV qRT-PCR positive. On cell culture, 1 IHNV qRT-PCR positive sample caused cytopathic effect after 2 passages on EPC cells. The virus, isolated from an asymptomatic rainbow trout fry, was identified by qRT-PCR and designated as MAKIHNV1. The phylogenetic reconstruction indicates that the isolated virus belongs to the European E genogroup, more specifically within the E-1 clade, and is similar to the German, Italian and Iranian isolates. This study has revealed for the first time the presence of IHNV in rainbow trout in North Macedonia. However, it is not possible to make interpretations about the source of infection from the phylogenetic analysis, and the origin of MAKIHNV1 remains unclear