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The ionic conducting hydrogel has attracted tremendous attention in fabricating flexible artificial skin-like devices. However, there are still unsolved challenges in hydrogel-based ionic skins, su...

Sugarcane production supports the livelihoods of millions of small‐scale farmers in developing countries, and the bioenergy needs of millions of consumers. Yet, future sugarcane yields remain uncertain due to differences in climate projections, and because the sensitivity of sugarcane ecophysiology to individual climate drivers (i.e. temperature, precipitation, shortwave radiation, VPD and CO2) and their interactions is largely unresolved. Here we ask: how sensitive is sugarcane yield to future climate change, including climate extremes, and what are its key climate drivers? We combine the Soil‐Plant‐Atmosphere model with detailed time‐series measurements from experimental plots in Guangxi, China, and São Paulo State, Brazil. We first calibrated and validated modelled carbon and water cycling against field flux and biometric data. Second, we simulated sugarcane growth under the historical climate (1980–2018), and six future climate projections (2015–2100). We computed the ‘yield‐effect’ of each climate driver by generating synthetic climate forcings in which the driver time series was alternated to that of the historical median. In Guangxi, median yield and yield lows (i.e. lower decile) were relatively insensitive to forecast climate change. In São Paulo, median yield and yield lows decreased under all future climates projections (urn:x-wiley:17571693:media:gcbb12797:gcbb12797-math-0001 = −4% and −12% respectively). At Guangxi, where moisture stress was low, radiation was the principal driver of yield variability (yield‐effect urn:x-wiley:17571693:media:gcbb12797:gcbb12797-math-0002 = −1.2%). Conversely, high moisture stress at São Paulo raised yield sensitivity to temperature (yield‐effect urn:x-wiley:17571693:media:gcbb12797:gcbb12797-math-0003 = −7.9%). In contrast, a number of other modelling studies report a positive effect of increased temperatures on sugarcane yield. We ascribe the disparity between model predictions to the representation of key phenological processes, including the link between leaf ageing and thermal time, and the role of ageing in driving leaf senescence. We highlight climate sensitivity of phenological processes as a key focus for future research efforts.

Lignin-containing cellulose nanopaper (LCNP) has emerged as a new generation of sustainable film for packaging and electronics. While compared with pure cellulose nanopaper (CNP), it still exhibits...

Mountain rivers exert critical ecological effects downstream by retaining or transmitting sediment and nutrients, providing habitat and refuge for diverse aquatic and riparian organisms, and creating migration corridors. River microhabitat heterogeneity (RMH), which plays a key role in ecological restoration and improvement, is sensitive to external disturbances in mountain rivers. However, the effects of RMH, induced by hydro-geomorphological processes, on local macroinvertebrates have not been quantitatively studied. To explore the ecological significance of RMH, we selected five debris flow-dominated mountain rivers (DMR) and five equilibrium sediment transport mountain rivers (EMR) as contrasting examples based on the richness of sediment supply. We measured water depth, flow velocity, and substrate composition in all rivers and proposed a new RMH index (RMHI) for quantitative evaluation of RMH. Macroinvertebrate standing stocks, taxonomic diversity, functional diversity, and functional traits were compared between DMR and EMR. Macroinvertebrate standing stocks in DMRs were about one-third, and α-diversity was half, of those in EMRs. The macroinvertebrate communities exhibited a turnover-dominated pattern in both DMRs and EMRs. Resource availability and utilization efficiency were also smaller in DMRs than in EMRs, which caused a macroinvertebrate community shift from R-strategy to K-strategy. Besides, RMH supported macroinvertebrate α-diversity and functional richness in both DMR and EMR, but debris flow weakened the ecological role of RMH in DMR. Our findings suggested that, in order to maintain the ecological health of mountain rivers, RMHI should be ≥ 8.0. According study results, rivers with greater macroinvertebrate species richness should be managed as a priority for biodiversity conservation by maintaining RMH above its threshold. This could be achieved through the addition of large stones to rivers, which would act to create a large range of riverbed sediment sizes and variable flow regimes, as well as increasing the space available to macroinvertebrates.

Humidity sensors have been widely used for humidity monitoring in industrial fields, while the unsatisfactory flexibility, time consumption, and expensive integration process of conventional inorganic sensors significantly limit their application in wearable electronics. Using paper-based humidity sensors is considered a feasible method to overcome these drawbacks because of their good flexibility and roll-to-roll manufacturability, while they still face problems such as poor durability and low sensitivity. In this study, we report a high-performance paper-based humidity sensor based on a rationally designed bilayered structure consisting of a nanoporous cellulose nanofiber/carbon nanotube (CNF/CNT) sensitive layer and a microporous paper substrate. The vast number of hydrophilic hydroxyl groups on the surface of CNF and paper fibers enables fast water molecule exchange between the humidity-sensitive material and the external environment via hydrogen bonding, endowing the paper-based sensor with an excellent humidity responsive property. The obtained sensor displays a maximum response value of 65.0% (ΔI/I0) at 95% relative humidity. Furthermore, the mechanical interlocking structure formed between the CNF/CNT layer and the paper layer provides the sensor with strong interlayer adhesion. Benefiting from the unique structure, the sensor also exhibits outstanding bending (with a maximum curvature of 22.2 cm-1) and folding durability (up to 50 times). Finally, as a proof of concept, a simple humidity-measuring device is assembled, which demonstrates an excellent responsive property toward human breath and the change of air humidity, indicating a great potential of our paper-based humidity sensor toward practical applications.

The Weihe River Basin plays an indispensable role in the water environment and water ecological balance in Northwest China and the lower reaches of the Yellow River. In the context of river ecosystems being affected by climate change and human activities, phytoplankton, as primary producers in food webs, serve as an important ecological indicator of environmental change. As such, systematic surveys on the water environment and phytoplankton were carried out in the Weihe River mainstem and its five tributaries from the northern foot of the Qinling Mountains from October to November 2017 and April to May 2018. In total, 154 species of phytoplankton belonging to 69 genera were identified in the heavy sediment-laden mainstem, with an average density and biomass of 177.57*104 cell L−1 and 6.53 mg L−1, respectively. Furthermore, a total of 207 species of phytoplankton belonging to 81 genera were identified in the five tributaries originating in the Qinling Mountains, with an average density and biomass of 80.98*104 cell L−1 and 1.90 mg L−1, respectively. Canonical correspondence analysis (CCA) was employed to analyze the relationship between phytoplankton communities and environmental factors. The results of data screening and Monte Carlo sequencing tests revealed that water temperature (WT), dissolved oxygen (DO), and nitrite nitrogen (NO2--N) were the primary environmental factors affecting the distribution and abundance of phytoplankton in the Weihe River mainstem. WT, flow velocity (V), pH, conductivity (Cond), and NO2--N predominantly structured the phytoplankton communities in the Weihe River tributaries. The results of this study are useful for the ecological management and conservation of the mainstem and tributaries of the Weihe River Basin.

During the next decades, combustion driven cars will be completely replaced by electrical vehicles (EVs) and it seems quite obvious that liquid electrolyte lithium-ion batteries (LIBs) will be the dominating energy storage system for the next at least 5 to 10 years. As a consequence, in Europe numerous Gigafactories have recently been planned with this state of technology. However, the current lithium-ion battery technology suffers so far from some restrictions like the inability to combine high power and high energy operations at the same time. This limitation is mainly attributed to the cathode architecture and respective mass loading. In addition, the further demand for a significantly enhanced fast charging mainly requires an optimization of the anode design flanked by a high areal capacity. Advanced 3D electrode architectures based on a thick film electrode concept seem to be the most promising approach to overcome the current limitations in battery performances. However, respective technology innovations need to provide a high compatibility grade to existing manufacturing routes in order to enable the required integration in existing and planned factories. For so-called “generation 3” materials, i.e., nickel-rich lithium nickel manganese cobalt oxide (NMC) cathode and silicon-based anode materials, structuring technologies using cutting edge ultrafast high power lasers, are being developed in order to manufacture 3D electrode architectures with high areal capacity. Multibeam laser processing using diffractive optical elements and dual scanner approaches were established in order to enable high processing speeds in roll-to-roll electrode handling systems. The technology readiness level (TRL 6) is demonstrated for pouch cells geometries. For water-based NMC 622 and silicon-graphite composites the laser structuring process was developed. Different structures including hole, grid, and line patterns, were studied regarding their impact on electrochemical performances such as high-rate capability and cell lifetime. Lithium concentration profiles of unstructured and structured electrodes were studied post mortem using laser-induced breakdown spectroscopy (LIBS) in order to evaluate lithium intercalation/deintercalation efficiencies and detect possible cell degradation processes. In comparison to unstructured electrodes, 3D electrodes could hereby always be identified as superior: unstructured thick film electrodes show a significant drop in capacity retention for high power operation and tend to form hot spots acting as starting point for cell failure. The upscaling process is flanked by a further improvement of electrode design. For this purpose very recently laser induced forward transfer (LIFT) is applied as printing technology to draw new concepts for sophisticated model electrode architectures with advanced electrochemical performances. Finally, the micro-/nano-scaled texturing of current collectors and separator material is discussed as further possible approaches for boosting the electrochemical performances of LIB pouch cells.

In this study, we developed a humidity sensor with high sensitivity based on cellulose nanofiber/carbon nanotube (CNF/CNT) hybrid foam. The porous structure of the foam not only provides more contact interface for water molecules adsorption, but also tunes the conductivity of the CCF closed to the point where the sensor is most sensitive to the change in humidity. With this porous structural design, the obtained foam sensor shows a high humidity sensitivity of 87.3% (ΔI/I

Forecasting rice grain yield prior to harvest is essential for crop management, food security evaluation, food trade, and policy-making. Many successful applications have been made in crop yield estimation using remotely sensed products, such as vegetation index (VI) from multispectral imagery. However, VI-based approaches are only suitable for estimating rice grain yield at the middle stage of growth but have limited capability at the ripening stage. In this study, an efficient convolutional neural network (CNN) architecture was proposed to learn the important features related to rice grain yield from low-altitude remotely sensed imagery. In one major region for rice cultivation of Southern China, a 160-hectare site with over 800 management units was chosen to investigate the ability of CNN in rice grain yield estimation. The datasets of RGB and multispectral images were obtained by a fixed-wing, unmanned aerial vehicle (UAV), which was mounted with a digital camera and multispectral sensors. The network was trained with different datasets and compared against the traditional vegetation index-based method. In addition, the temporal and spatial generality of the trained network was investigated. The results showed that the CNNs trained by RGB and multispectral datasets perform much better than VIs-based regression model for rice grain yield estimation at the ripening stage. The RGB imagery of very high spatial resolution contains important spatial features with respect to grain yield distribution, which can be learned by deep CNN. The results highlight the promising potential of deep convolutional neural networks for rice grain yield estimation with excellent spatial and temporal generality, and a wider time window of yield forecasting.

Flexible and highly sensitive humidity sensors are crucial for humidity detection. In this study, a flexible cellulose nanofiber/carbon nanotube (NFC/CNT) humidity sensor with high sensitivity performance was developed via fast vacuum filtration. CNTs were well dispersed in water by using 2,2,6,6-tetramethylpiperidinyl-1-oxyl (TEMPO)-oxidized NFC as a dispersant. More importantly, NFC also acted as a humidity sensitive material, achieving superior performance of NFC/CNT humidity sensors. The obtained NFC/CNT humidity sensor with 5 wt % CNT loading exhibits outstanding sensitive performance, and its response value reaches a maximum of 69.9% (Δ I/ I

The development of high areal energy density electrodes with layered metal oxide cathode materials is currently worldwide a main topic in research and industry. Water-based binders were applied in the presented work for the manufacturing of NMC 622 (Li(Ni0.6Mn0.2Co0.2)O2) cathodes in order to achieve environmental friendly and cost-reduced production, replacing the conventional organic PVDF binder and the toxic and volatile NMP solvent. However, the pH value of aqueous processed cathode slurries increases to over 12 due to the reaction between NMC 622 particles and water, which decreases the specific discharge capacity of the cells and on the other side results in the chemical corrosion of the current collector during casting. In order to avoid the damage of the current collector and protect the cathode material, different acids could be applied during mixing process to adjust the slurry pH value. In addition, the energy density at battery level can be increased with increasing electrode film thickness which corresponds to an enhanced areal capacity. However, capacity fading of thick-film electrodes at C-rates above C/2 is mainly due to a limited lithium-ion diffusion kinetics and crack formation in cathode particles. 3D electrode architectures produced by ultrafast laser ablation are proven to improve the capacity retention at high discharge rates and the electrolyte wettability of electrodes as reported in our previous works. In this work, NMC 622 electrodes with a film thickness of 150 µm (5.8 mAh/cm2) were manufactured with aqueous binders TRD 202A and CMC, and were subsequently laser structured. With increasing film thickness from 70 µm to 150 µm, the mass loading of cathodes increased from 17 mg/cm2 to 36 mg/cm2. The slurry pH values were adjusted between 9 - 10 with citric acid (CA), phosphoric acid (PA), and acetic acid (AC). The dried electrodes were characterized using XPS and SEM. The unstructured and structured electrodes with different thicknesses were afterwards assembled versus lithium in coin cells. Rate capability test and cyclic voltammetry (CV) were performed in order to investigate the electrochemical performance of the electrodes. Cells containing 70 µm cathodes with PA and CA+PA showed about 10 mAh/g higher capacity at C/20 to C/5 compared to cells with PA, while at 1C to 3C cells with CA showed the lowest capacities With increasing electrode film thickness, cells with PA and PA+CA retained 5-10 mAh/g more capacity than cells with CA at C/20 to C/5. The cells with PA maintained about 120 mAh/g discharge capacity at C/2, while cells with PA+CA and CA showed about 80 mAh/g and 45 mAh/g, respectively. In comparison to cells with unstructured electrodes, laser structuring increased the discharge capacity of cells with 70 µm electrodes from 1C to 3C, regardless of the type of added acid. As for cells with thick-film electrodes, the discharge capacity of cells containing structured electrodes with CA+PA increased to 120 mAh/g, which is about 40 mAh/g higher compared to cells with unstructured electrodes. Finally, CV proved that the addition of acids had no effect on the redox reaction of NMC 622.

In this study, fast ionic conductor Li0.33La0.557TiO3 (LLTO) nanowires with high ionic conductivity were prepared through electrospinning and subsequent high temperature calcination. It was filled in the poly(ethylene oxide) (PEO) matrix as a filler and the novel solid PEO/LiTFSI/LLTO-nanowires polymer composite electrolytes were prepared via solution casting method with low cost. The results showed that the addition of LLTO nanowires could effectively improve the ionic conductivity, electrochemical stability window, transference number, and compatibility with lithium metal of polymer composite electrolytes. The maximum ionic conductivities of composite electrolytes filled with 5 wt% LLTO nanowires at room temperature and 60 °C were 5.53 × 10−5 S cm−1 and 3.63 × 10−4 S cm−1, respectively. Its electrochemical stability window was up to 4.75 V. The LiFePO4/Li solid-state lithium-ion battery assembled with the novel polymer composite electrolytes had good cycling stability. At current rate of 0.5 C, the discharge specific capacity remained about 123 mAh g−1 after 100 cycles at 60 °C.

The introduction of laser-generated 3D capillary structures in electrodes of Li-ion cells enables a more homogenized electrolyte wetting, which helps avoiding warm aging and shortens cell storage time prior and after electrochemical formation. In this study, the roll-to-roll (R2R) laser patterning of graphite anode material with a high-power ultrafast laser source was investigated. The governing parameters of laser structuring and their impact on active mass loss, electrode architecture, and electrolyte wetting behavior were analyzed. The trade-off between the quality of the structures and the processing times for an optimized R2R-processing was assessed. The application of a diffractive beam-splitter was evaluated.

In order to effectively prevent and control bird damage, this paper proposes a bird damage risk assessment method based on K-Means clustering algorithm. Analyze the corrected historical data of bird damage in a certain area, historical data mainly includes the tower type, the environmental information of the location of the tower and the surrounding environment information, build a data set, using the K-Means clustering algorithm to process and analysis data, finally the classification of the sample can be obtained, which serves as the basis for the classification of bird hazards in the area.

The future role of the terrestrial biosphere in the global carbon cycle is highly uncertain. Modeling and predicting the terrestrial net carbon balance is difficult due to the numerous processes driving variability of gross fluxes. Many approaches to reducing this model uncertainty have focused on model structure, namely by adding additional processes (e.g., nutrient dynamics or vegetation demography) and thus increasing complexity. While these developments seek to achieve greater structural realism by mirroring the complexity of the natural world, they often rely, by necessity, on poorly-determined or over-generalized parameters. Furthermore, increased structural complexity may increase the risk that parameters with compensating errors are found during model development, thereby reducing model accuracy in prediction. It is not clear whether or to what extent carbon cycle predictability scales with structural complexity, or whether an intermediate, optimum level of complexity exists that may balance the costs of a low (more biased) or high (more variant) complexity model. Here, we explore and define the relationship between carbon cycle model complexity and prediction accuracy. To do so, we leverage the CARbon Data MOdel fraMework (CARDAMOM), a Bayesian data assimilation system that retrieves terrestrial carbon cycle variables (including pools, fluxes, and static parameters) by combining multiple observations with a relatively simple ecosystem carbon balance model. CARDAMOM includes several ecological and dynamical constraints that can prevent ecologically unrealistic parameter combinations and reduce compensating errors between parameters (also known as equifinality). Furthermore, it is a flexible framework to which process representations, parameters, and constraints can easily be added and removed. We used CARDAMOM to develop a suite of model versions spanning a broad range of structural complexity, including the number of carbon pools and the allocation of carbon to the canopy. We assessed a model’s complexity based on its inherent dimensionality, determined via a principal component analysis that reduces the parameter space to its principal components. We tested and compared the training and forecast accuracies of net ecosystem exchange predictions using 14 increasingly complex versions of CARDAMOM, each with 48 different experimental designs (i.e., combinations of data constraints and error assumptions) at 5 globally-distributed eddy covariance sites representing a range of biomes and vegetation types across a total of 70 site-years. We also compared the model performance values against a range of machine learning approaches, which are assumed to represent the limit of infinite model complexity due to their large number of underlying parameters. In this presentation, we use this population to demonstrate and explain patterns in the mapping of model complexity and other assimilation choices to prediction accuracy, offering theoretical and empirical insights into the optimal structure of a carbon cycle model.

Medical dialogue systems are promising in assisting in telemedicine to increase access to healthcare services, improve the quality of patient care, and reduce medical costs. To facilitate the research and development of medical dialogue systems, we build large-scale medical dialogue datasets -- MedDialog, which contain 1) a Chinese dataset with 3.4 million conversations between patients and doctors, 11.3 million utterances, 660.2 million tokens, covering 172 specialties of diseases, and 2) an English dataset with 0.26 million conversations, 0.51 million utterances, 44.53 million tokens, covering 96 specialties of diseases. To our best knowledge, MedDialog is the largest medical dialogue dataset to date. We pretrain several dialogue generation models on the Chinese MedDialog dataset, including Transformer, GPT, BERT-GPT, and compare their performance. It is shown that models trained on MedDialog are able to generate clinically correct and doctor-like medical dialogues. We also study the transferability of models trained on MedDialog to low-resource medical dialogue generation tasks. It is shown that via transfer learning which finetunes the models pretrained on MedDialog, the performance on medical dialogue generation tasks with small datasets can be greatly improved, as shown in human evaluation and automatic evaluation. The datasets and code are available at https://github.com/UCSD-AI4H/Medical-Dialogue-System

The successful utilization of adsorption methods in the dye wastewater treatment had promoted us to develop inexpensive adsorbents from industrial and agricultural wastes. The work appraised the application potentiality of bamboo shoot skin (BSS) as an adsorbent. Batch adsorption of methylene blue (MB) was conducted to investigate the influence of the dosage of adsorbent, concentration of MB, time of adsorption, temperature, and initial pH value on the adsorption properties of BSS. Four frequently used adsorption kinetic models were utilized to fit the experimental results, and the pseudo-second-order kinetic model with big correlation coefficient could well describe the adsorption kinetics. The experimental equilibrium data were fitted by two frequently used adsorption isotherm models, and the Langmuir isotherm model described well. Its maximum adsorption capacity was 29.88 mg/g. The obtained thermodynamic parameters indicated that the adsorption process was spontaneous and exothermic in nature. In addition, the adsorption mechanism and comparison of BSS with other adsorbents were also discussed.

Transparent paper with high transmission haze has garnered great attention due to its potential applications in light management of optoelectronics as a bulk optical material. Herein, we investigated the transmission haze and mechanical properties of transparent paper with different fiber species. The morphological dimensions of cellulose fibers (Northwood, Eucalyptus wood, Manila hemp) before and after 2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO) treatment were investigated in detail. The effect of fiber morphology and preparation methods on transmission haze and mechanical properties (tensile strength, Young’s modulus, bursting strength) of the resultant paper was then analyzed. The results showed that transparent paper with TEMPO-treated northern fibers possessed highest optical transmittance and transmission haze, as well as strongest mechanical strength compared to two other transparent paper. Furthermore, transparent paper prepared by solution casting exhibited higher mechanical strength, but lower transmission haze compared to paper prepared by vacuum filtration. It’s worth noting that sub-micro and nanofiber partially contributed to the transparency and mechanical properties of the resulting transparent paper. This work sheds light on the preparation of highly transparent and strong paper with excellent light scattering behavior for manipulating light behavior of optoelectronics as a bulk optical material.