Evaluation of electrode and solution area-based resistances enables quantitative comparisons of factors impacting microbial fuel cell performance

Direct comparisons of microbial fuel cells based on maximum power densities are hindered by different reactor and electrode sizes, solution conductivities, and materials. We propose an alternative method here, the electrode potential slope (EPS) analysis, to enable quantitative comparisons based on anodeand cathode area-based resistances and operating potentials. Using EPS analysis, the brush anode resistance (RAn = 10.6 ± 0.5 mΩ m2) was shown to be 28% lower than the resistance of a 70% porosity diffusion layer (70% DL) cathode (RCat = 14.8 ± 0.9 mΩ m2) and 24% lower than the solution resistance (RΩ = 14 mΩ m2) (acetate in a 50 mM phosphate buffer solution). Using a less porous cathode (30% DL) did not impact the cathode resistance but did reduce the cathode performance due to a lower operating potential. With low conductivity domestic wastewater (RΩ = 87 mΩ m2), both electrodes had higher resistances [RAn = 75 ± 9 mΩ m2, and RCat = 54 ± 7 mΩ m2 (70% DL)]. Our analysis of the literature using EPS analysis shows how electrode resistances can easily be quantified to compare system performance when the electrode distances are changed or the sizes of the electrodes are different

Energy Analysis of a Space-Energy Driven Laser-Ablation Debris Removal System

Space-energy driven laser-ablation debris removal is a feasible way to address the worsening space debris environment. Energy analysis is crucial for the design and run of a space-energy driven laser-ablation debris removal system. This study details the energy analysis of a space-energy driven laser-ablation debris removal system as affected by laser energy, frequency and range. The results show that the laser irradiation time and energy efficiency are decreased with increases in the laser energy and frequency, and the energy efficiency in the case of different planes is significantly lower than that in the case of coplanar. However, laser range has no effect on the perigee changing and energy efficiency. The results can effectively guide the removal scheme design and evaluation

Four-Dimensional Superimposition Techniques to Compose Dental Dynamic Virtual Patients: A Systematic Review

Four-dimensional virtual patient is a simulation model integrating multiple dynamic data. This study aimed to review the techniques in virtual four-dimensional dental patients. Searches up to November 2022 were performed using the PubMed, Web of Science, and Cochrane Library databases. The studies included were based on the superimposition of two or more digital information types involving at least one dynamic technique. Methodological assessment of the risk of bias was performed according to the Joanna Briggs Institute Critical Appraisal Checklist. Methods, programs, information, registration techniques, applications, outcomes, and limitations of the virtual patients were analyzed. Twenty-seven full texts were reviewed, including 17 case reports, 10 non-randomized controlled experimental studies, 75 patients, and 3 phantoms. Few studies showed a low risk of bias. Dynamic data included real-time jaw motion, simulated jaw position, and dynamic facial information. Three to five types of information were integrated to create virtual patients based on diverse superimposition methods. Thirteen studies showed acceptable dynamic techniques/models/registration accuracy, whereas 14 studies only introduced the feasibility. The superimposition of stomatognathic data from different information collection devices is feasible for creating dynamic virtual patients. Further studies should focus on analyzing the accuracy of four-dimensional virtual patients and developing a comprehensive system

Data-driven interpretable analysis for polysaccharide yield prediction

Cornstalks show promise as a raw material for polysaccharide production through xylanase. Rapid and accurate prediction of polysaccharide yield can facilitate process optimization, eliminating the need for extensive experimentation in actual production to refine reaction conditions, thereby saving time and costs. However, the intricate interplay of enzymatic factors poses challenges in predicting and optimizing polysaccharide yield accurately. Here, we introduce an innovative data-driven approach leveraging multiple artificial intelligence techniques to enhance polysaccharide production. We propose a machine learning framework to identify highly accurate polysaccharide yield prediction modeling methods and uncover optimal enzymatic parameter combinations. Notably, Random Forest (RF) and eXtreme Gradient Boost (XGB) demonstrate robust performance, achieving prediction accuracies of 93.0% and 95.6%, respectively, while an independently developed deep neural network (DNN) model achieves 91.1% accuracy. A feature importance analysis of XGB reveals the enzyme solution volume's dominant role (43.7%), followed by time (20.7%), substrate concentration (15%), temperature (15%), and pH (5.6%). Further interpretability analysis unveils complex parameter interactions and potential optimization strategies. This data-driven approach, incorporating machine learning, deep learning, and interpretable analysis, offers a viable pathway for polysaccharide yield prediction and the potential recovery of various agricultural residues

The Ecology-Economy-Transport Nexus: Evidence from Fujian Province, China

The coordinated relationship between ecology, economy and transportation is essential for regional sustainable development. Does the high-quality ecological environment mean the lagging development of economy and transportation, or does the rapid growth of the economy and transportation lead to the deterioration of the ecological environment? To shed new light on the complicated relationship between ecology, economy and transportation, our study aims to construct three comprehensive indicators, including an ecological index (EI), economic development level (EC) and transport superiority degree (TR), to reflect the systems mentioned above, and to measure the coordination of the three indicators’ development and evolution using a model of the coordination degree (CD). Specifically, and by applying methods for the indicators’ normalization, including superposition analysis and principal component analysis, the three indicators’ values are reasonably evaluated for measuring their coordination relationship. The above three indicators use data from 58 counties in Fujian province from 2000 to 2018 in our study. All three indicators show differences in the west and east of Fujian province; the EI is relatively low in the eastern coastal areas and relatively high in the western mountainous areas, the EC shows a relatively discrete and irregular distribution and the distribution pattern of the TR is almost the opposite of the EI. The CD shows a relationship among the three indicators, with the EI and EC coordinated in most counties and the EI and TR coordinated in most counties, while the highly coordinated counties are mainly distributed in the northwest and east coastal regions of Fujian province in 2000, and the northwest, south and northeast of Fujian province in 2018. More than 50% of the county EC and TR values are kept in a coordinated state, and are mainly distributed in the eastern coast and central part of Fujian province. Over 50% of counties’ CD between EI and EC, EI and TR and EC and TR are in a coordinated state. The CD of the EI and EC and TR, in most counties, are in a coordinated state, mainly distributed in the eastern coast and central areas of Fujian province. In other words, the findings show that the coordinated state of ecology, economy and transportation can be achieved at the county level of Fujian province. These conclusions have significant reference value for understanding regional sustainable development

Assessment of a metal\u2013organic framework catalyst in air cathode microbial fuel cells over time with different buffers and solutions

Metal\u2013organic framework (MOF) on activated carbon (AC) enhanced the performance of cathodes but longevity needs to be considered in the presence of metal chelators or ligands, such as phosphate, present in wastewaters. MOF catalysts on AC initially produced 2.78\ua0\ub1\ua00.08\ua0W\ua0m 122, but power decreased by 26% after eight weeks in microbial fuel cells using a 50\ua0mM phosphate buffer (PBS) and acetate due to decreased cathode performance. However, power was still 41% larger than that of the control AC (no MOF). Power generation using domestic wastewater was initially 0.73\ua0\ub1\ua00.01\ua0W\ua0m 122, and decreased by 21% over time, with power 53% larger than previous reports, although changes in wastewater composition were a factor in performance. Adding phosphate salts to the wastewater did not affect the catalyst performance over time. While MOF catalysts are therefore initially adversely affected by chelators, performance remains enhanced compared to plain AC