Research Progress of Ionic Thermoelectric Materials for Energy Harvesting

Thermoelectric material is a kind of functional material that can mutually convert heat energy and electric energy. It can convert low-grade heat energy (less than 130°C) into electric energy. Compared with traditional electronic thermoelectric materials, ionic thermoelectric materials have higher performance. The Seebeck coefficient can generate 2–3 orders of magnitude higher ionic thermoelectric potential than electronic thermoelectric materials, so it has good application prospects in small thermoelectric generators and solar power generation. According to the thermoelectric conversion mechanism, ionic thermoelectric materials can be divided into ionic thermoelectric materials based on the Soret effect and thermocouple effect. They are widely used in pyrogen batteries and ionic thermoelectric capacitors. The latest two types of ionic thermoelectric materials are in this article. The research progress is explained, and the problems and challenges of ionic thermoelectric materials and the future development direction are also put forward

Vertical habitat preferences shape the fish gut microbiota in a shallow lake

Understanding the interactions between fish gut microbiota and the aquatic environment is a key issue for understanding aquatic microorganisms. Environmental microorganisms enter fish intestines through feeding, and the amount of invasion varies due to different feeding habits. Traditional fish feeding habitat preferences are determined by fish morphology or behavior. However, little is known about how the feeding behavior of fish relative to the vertical structure in a shallow lake influences gut microbiota. In our study, we used nitrogen isotopes to measure the trophic levels of fish. Then high-throughput sequencing was used to describe the composition of environmental microbiota and fish gut microbiota, and FEAST (fast expectation-maximization for microbial source tracking) method was used to trace the source of fish gut microbiota. We investigated the microbial diversity of fish guts and their habitats in Lake Sanjiao and verified that the sediments indeed played an important role in the assembly of fish gut microbiota. Then, the FEAST analysis indicated that microbiota in water and sediments acted as the primary sources in half of the fish gut microbiota respectively. Furthermore, we classified the vertical habitat preferences using microbial data and significant differences in both composition and function of fish gut microbiota were observed between groups with distinct habitat preferences. The performance of supervised and unsupervised machine learning in classifying fish gut microbiota by habitat preferences actually exceeded classification by fish species taxonomy and fish trophic level. Finally, we described the stability of fish co-occurrence networks with different habitat preferences. Interestingly, the co-occurrence network seemed more stable in pelagic fish than in benthic fish. Our results show that the preferences of fish in the vertical structure of habitat was the main factor affecting their gut microbiota. We advocated the use of microbial interactions between fish gut and their surrounding environment to reflect fish preferences in vertical habitat structure. This approach not only offers a novel perspective for understanding the interactions between fish gut microbiota and environmental factors, but also provides new methods and ideas for studying fish habitat selection in aquatic ecosystems

Depression and anxiety in cervical degenerative disc disease: Who are susceptible?

BackgroundPre-operative depression and anxiety are associated with poorer patient-reported outcomes following cervical spine surgery. Identification of and interventions for these disorders are key to preventing related negative effects. However, most spine surgeons do not routinely evaluate mental health disorders. Few studies have investigated which patients with cervical degenerative disc diseases (CDDD) are susceptible to depression and anxiety.ObjectiveTo determine the factors associated with depression and anxiety in patients with CDDD.MethodsThree hundred twelve patients with CDDD were recruited in this cross-sectional case-control study. Patients underwent a structured interview to acquire demographic and clinical characteristic information, which included the Neck Disability Index (NDI), modified Japanese Orthopedic Association (mJOA), and Visual Analog Scale (VAS) for neck/arm pain. Depression and anxiety were evaluated using the Zung Self-Rating Depression and Anxiety Scales. Univariate and multivariate logistic regression analyses were used to identify factors associated with depression and anxiety.ResultsOf all patients, 102 (32.7%) had depression and 92 (29.5%) had anxiety. Two hundred six (66.0%) patients with neither depression nor anxiety were defined as the control group. Univariate analysis indicated that gender, educational level, occupation type, Charlson comorbidity index, symptom duration, symptomatology, surgery history, NDI, mJOA, VAS-neck, and VAS-arm scores were associated with depression and anxiety (except for symptom duration for anxiety). Multivariate logistic regression analysis indicated that females [odds ratio (OR) 1.81, 95% confidence interval (CI) 1.01–3.23], physical work (OR 2.06, 95% CI 1.16–3.65), poor mJOA score (ORmoderate 2.67, 95% CI 1.40–5.07; ORsevere 7.63, 95% CI 3.85–15.11), and high VAS-neck score (OR 1.24, 95% CI 1.11–1.39) were independent risk factors for depression. Physical work (OR 1.84, 95% CI 1.01–3.35), poor mJOA score (ORmoderate 2.66, 95% CI 1.33–5.33; ORsevere 9.26, 95% CI 4.52–18.99), and high VAS-neck score (OR 1.34, 95% CI 1.19–1.51) were independent risk factors for anxiety.ConclusionApproximately one-third of patients with CDDD had depression or anxiety. Patients who engaged in heavy work and had severe symptoms (poor mJOA and high VAS-neck scores) are susceptible to depression and anxiety. Additionally, female patients are susceptible to depression. Our findings may help identify CDDD patients with depression and anxiety in clinical practice

Multi-omics profiling reveals resource allocation and acclimation strategies to temperature changes in a marine dinoflagellate

Temperature is a critical environmental factor that affects the cell growth of dinoflagellates and bloom formation. To date, the molecular mechanisms underlying the physiological responses to temperature variations are poorly understood. Here, we applied quantitative proteomic and untargeted metabolomic approaches to investigate protein and metabolite expression profiles of a bloom-forming dinoflagellate Prorocentrum shikokuense at different temperatures. Of the four temperatures (19, 22, 25, and 28°C) investigated, P. shikokuense at 25°C exhibited the maximal cell growth rate and maximum quantum efficiency of photosystem II (Fv/Fm) value. The levels of particulate organic carbon (POC) and nitrogen (PON) decreased with increasing temperature, while the POC/PON ratio increased and peaked at 25°C. Proteomic analysis showed proteins related to photoreaction, light harvesting, and protein homeostasis were highly expressed at 28°C when cells were under moderate heat stress. Metabolomic analysis further confirmed reallocated amino acids and soluble sugars at this temperature. Both omic analyses showed glutathione metabolism that scavenges the excess reactive oxygen species, and transcription and lipid biosynthesis that compensate for the low translation efficiency and plasma membrane fluidity were largely upregulated at suboptimal temperature. Higher accumulations of glutathione, glutarate semialdehyde, and 5-KETE at 19°C implied their important roles in low-temperature acclimation. The strikingly active nitrate reduction and nitrogen flux into asparagine, glutamine, and aspartic acid at 19°C indicated these three amino acids may serve as nitrogen storage pools and help cells cope with low temperature. Our study provides insights into the effects of temperature on dinoflagellate resource allocation and advances our knowledge of dinoflagellate bloom formation in marine environments

Navigation Route Planning for Tourism Intelligent Connected Vehicle Based on the Symmetrical Spatial Clustering and Improved Fruit Fly Optimization Algorithm

The intelligent connected vehicle (ICV) decision-making system needs to match tourist interests and search for the route with the lowest travel cost when recommending POIs (Points of Interest) and navigation tour routes. In response to this research objective, we construct a navigation route-planning model for tourism intelligent connected vehicles based on symmetrical spatial clustering and improved fruit fly optimization algorithm. Firstly, we construct the POI feature attribute clustering algorithm based on the spatial decision forest to achieve the optimal POI recommendation. Secondly, we construct the POI spatial attribute clustering algorithm based on the SA-AGNES (Spatial Accessibility-Agglomerative Nesting) to achieve the spatial modeling between POIs and ICV clusters. On the basis of POI feature attribute and spatial attribute, we construct the POI recommendation algorithm for the ICV navigation routes based on the attribute weights. On the basis of the recommended POIs, we construct the tourism ICV navigation route-planning model based on the improved fruit fly optimization algorithm. Experiments prove that the proposed algorithm can accurately output POIs that match tourists’ interests and needs, and find out the ICV navigation route with the lowest travel cost. Compared with the commonly used map route-planning methods and traditional route-searching algorithms, the proposed algorithm can reduce the travel costs by 15.22% at most, which can also effectively reduce the energy consumption of the ICV system, and improve the efficiency of sight-seeing and traveling for tourists

A Low-Carbon Decision-Making Algorithm for Water-Spot Tourists, Based on the k-NN Spatial-Accessibility Optimization Model

This study presents a low-carbon decision-making algorithm for water-spot tourists, based on the k-NN spatial-accessibility optimization model, to address the problems of water-spot tourism spatial decision-making. The attributes of scenic water spots previously visited by the tourists were knowledge-mined, to ascertain the tourists’ interest-tendencies. A scenic water-spot classification model was constructed, to classify scenic water spots in tourist cities. Then, a scenic water spot spatial-accessibility optimization model was set up, to sequence the scenic spots. Based on the tourists’ interest-tendencies, and the spatial accessibility of the scenic water spots, a spatial-decision algorithm was constructed for water-spot tourists, to make decisions for the tourists, in regard to the tour routes with optimal accessibility and lowest cost. An experiment was performed, in which the tourist city of Leshan was chosen as the research object. The scenic water spots were classified, and the spatial accessibility for each scenic spot was calculated; then, the optimal tour routes with optimal spatial accessibility and the lowest cost were output. The experiment verified that the tour routes that were output via the proposed algorithm had stronger spatial accessibility, and cost less than the sub-optimal ones, and were thus more environmentally friendly

Dynamic Properties and Fractal Characteristics of 3D Printed Cement Mortar in SHPB Test

Comparing with the traditional construction process, 3D printing technology used in construction offers many advantages due to the elimination of formwork. Currently, 3D printing technology used in the construction field is widely studied, however, limited studies are available on the dynamic properties of 3D printed materials. In this study, the effects of sand to binder ratios and printing directions on the fractal characteristics, dynamic compressive strength, and energy dissipation density of 3D printed cement mortar (3DPCM) are explored. The experiment results indicate that the printing direction has a more significant influence on the fractal dimension compared with the sand to binder ratio (S/B). The increasing S/B first causes an increase and then results in a decline in the dynamic compressive strength and energy dissipation of different printing directions. The anisotropic coefficient of 3DPCM first is decreased by 20.67%, then is increased by 10.56% as the S/B increases from 0.8 to 1.4, showing that the anisotropy is first mitigated, then increased. For the same case of S/B, the dynamic compressive strength and energy dissipation are strongly dependent on the printing direction, which are the largest printing in the Y-direction and the smallest printing in the X-direction. Moreover, the fractal dimension has certain relationships with the dynamic compressive strength and energy dissipation density. When the fractal dimension changes from 2.0 to 2.4, it shows a quadratic relationship with the dynamic compressive strength and a logarithmic relationship with the energy dissipation density in different printing directions. Finally, the printing mortar with an S/B = 1.1 is proved to have the best dynamic properties, and is selected for the 3D printing of the designed field barrack model

Templated formation of porous Mn2O3 octahedra from Mn-MIL-100 for lithium-ion battery anode materials

Octahedral Mn-MIL-100 metal-organic frameworks (MOFs) are first synthesized, which are then used as templates to fabricate the porous Mn2O3 octahedra through a post-calcination strategy. The morphologies and crystalline structures of as-prepared Mn2O3 octahedra are performed by using field-emission scanning electron microscopy (FESEM), X-ray diffraction (XRD) and transmission electron microscopy (TEM). A reversible lithium storage capacity as high as 755 mA h/g at 0.2 C after 100 cycles is measured from Lithium-ion batteries (LIBs) where the porous Mn2O3 octahedra are acted as anode. Such a high performance indicates that the porous Mn2O3 structure is an excellent anode candidate of LIBs with high capacity and long-life cycling stability.MOE (Min. of Education, S’pore

Co-synthesis of CuO-ZnO nanoflowers by low voltage liquid plasma discharge with brass electrode

Transition metal oxides CuO-ZnO nano-flowers have been simultaneously synthesized by the low voltage liquid plasma discharge method using brass cathode. The effects of discharge statue (normal and abnormal glow discharge) on the nanostructures were investigated. It was found that a lower discharge voltage 52 V in the normal glow discharge period is beneficial to produce homogeneous nano-flower structures while a higher voltage tend to result in inhomogeneous products including larger particles. The obtained products were characterized by SEM, XRD, high-resolution TEM, Raman and XPS. Moreover, the nano-flowers exhibit a favorable electrocatalytic activity of glucose oxidation.This research was funded by the Chinese Natural Science Foundation (Grant number: 21504025 and 51305230)