| 1. |
- Luo, Yifei, et al.
(författare)
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Technology Roadmap for Flexible Sensors
- 2023
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Ingår i: ACS Nano. - : American Chemical Society. - 1936-0851 .- 1936-086X. ; 17:6, s. 5211-5295
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Forskningsöversikt (refereegranskat)abstract
- Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity.
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| 2. |
- Chen, Shouzhi, et al.
(författare)
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Informing the SWAT model with remote sensing detected vegetation phenology for improved modeling of ecohydrological processes
- 2023
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Ingår i: Journal of Hydrology. - : Elsevier BV. - 0022-1694. ; 616
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Tidskriftsartikel (refereegranskat)abstract
- The Soil and Water Assessment Tool (SWAT) model has been widely applied for simulating the water cycle and quantifying the influence of climate change and anthropogenic activities on hydrological processes. A major uncertainty of SWAT stems from the poor representation of vegetation dynamics due to the use of a simplistic vegetation growth and development module. Using long-term remote sensing-based phenological data, the SWAT model's vegetation module was improved by adding a dynamic growth start date and the dynamic heat requirement for vegetation growth rather than using constant values. The new SWAT model was verified in the Han River basin, China, and found its performance was much improved in comparison with that of the original SWAT model. Specifically, the accuracy of the leaf area index (LAI) simulation improved notably (coefficient of determination (R2) increased by 0.193, Nash–Sutcliffe Efficiency (NSE) increased by 0.846, and percent bias decreased by 42.18 %), and that of runoff simulation improved modestly (R2 increased by 0.05 and NSE was similar). Additionally, it is found that the original SWAT model substantially underestimated evapotranspiration (Penman-Monteith method) in comparison with the new SWAT model (65.09 mm (or 22.17 %) for forests, 92.27 mm (or 32 %) for orchards, and 96.16 mm (or 36.4 %) for farmland), primarily due to the inaccurate representation of LAI dynamics. Our results suggest that an accurate representation of phenological dates in the vegetation growth module is important for improving the SWAT model performance in terms of estimating terrestrial water and energy balance.
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| 3. |
- Liang, Wang, et al.
(författare)
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Life cycle assessment of blast furnace ironmaking processes : A comparison of fossil fuels and biomass hydrochar applications
- 2023
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Ingår i: Fuel. - : Elsevier BV. - 0016-2361 .- 1873-7153. ; 345
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Tidskriftsartikel (refereegranskat)abstract
- The impact of the iron and steel production process on the ecological environment cannot be ignored. This study aims to assess the impact of life cycle assessment on the traditional fossil fuel blast furnace ironmaking process and the biomass hydrochar blast furnace ironmaking process. The Simapro v9.0 software is used to comprehensively evaluate the life cycle impacts of biomass hydrochar in the blast furnace ironmaking process. The results show that the life cycle impact categories of the blast furnace ironmaking process mainly include global warming, non-renewable energy and respiratory inorganics. The global warming impact of the ironmaking process using hydrochar is 2054.00 kg CO2 eq, which is 420.61 kg CO2 eq less than that of traditional blast furnace ironmaking process. The global warming impact is mainly reflected in the emission of CO2 gas, and the main source is the generation of blast furnace gas and the use of sinter. The respiratory inorganics impact is mainly manifested in the emission of nitrogen oxides, sulfur oxides and particulates, which mainly comes from the mining of iron ore and the production of sinter. The non-renewable energy impact mainly comes from the coal resources, and the use of other renewable energy such as biomass energy is an important way to reduce the impact. Therefore, biomass hydrochar used in the metallurgical process is more suitable for sustainable devel-opment of the ecological environment.
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| 4. |
- Liu, Tao, et al.
(författare)
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16% efficiency all-polymer organic solar cells enabled by a finely tuned morphology via the design of ternary blend
- 2021
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Ingår i: Joule. - : CELL PRESS. - 2542-4351. ; 5:4, s. 914-930
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Tidskriftsartikel (refereegranskat)abstract
- A SUMMARY There is an urgent demand for all-polymer organic solar cells (AP-OSCs) to gain higher efficiency. Here, we successfully improve the performance to 16.09% by introducing a small amount of BN-T, a B <- N-type polymer acceptor, into the PM6:PY-IT blend. It has been found that BN-T makes the active layer, based on the PM6:PY-IT:BN-T ternary blend, more crystalline but meanwhile slightly reduces the phase separation, leading to enhancement of both exciton harvesting and charge transport. From a thermodynamic viewpoint, BN-T prefers to reside between PM6 and PY-IT, and the fraction of this fine-tunes the morphology. Besides, a significantly reduced nonradiative energy loss occurs in the ternary blend, along with the coexistence of energy and charge transfer between the two acceptors. The progressive performance facilitated by these improved properties demonstrates that AP-OSCs can possibly comparably efficient with those based on small molecule acceptors, further enhancing the competitiveness of this device type.
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| 5. |
- Lu, Liu, et al.
(författare)
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High-performance formaldehyde prediction for indoor air quality assessment using time series deep learning
- 2024
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Ingår i: Building Simulation. - : TSINGHUA UNIV PRESS. - 1996-3599 .- 1996-8744.
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Tidskriftsartikel (refereegranskat)abstract
- Indoor air pollution resulting from volatile organic compounds (VOCs), especially formaldehyde, is a significant health concern needed to predict indoor formaldehyde concentration (Cf) in green intelligent building design. This study develops a thermal and wet coupling calculation model of porous fabric to account for the migration of formaldehyde molecules in indoor air and cotton, silk, and polyester fabric with heat flux in Harbin, Beijing, Xi'an, Shanghai, Guangzhou, and Kunming, China. The time-by-time indoor dry-bulb temperature (T), relative humidity (RH), and Cf, obtained from verified simulations, were collated and used as input data for the long short-term memory (LSTM) of the deep learning model that predicts indoor multivariate time series Cf from the secondary source effects of indoor fabrics (adsorption and release of formaldehyde). The trained LSTM model can be used to predict multivariate time series Cf at other emission times and locations. The LSTM-based model also predicted Cf with mean absolute percentage error (MAPE), symmetric mean absolute percentage error (SMAPE), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) that fell within 10%, 10%, 0.5, 0.5, and 0.8, respectively. In addition, the characteristics of the input dataset, model parameters, the prediction accuracy of different indoor fabrics, and the uncertainty of the data set are analyzed. The results show that the prediction accuracy of single data set input is higher than that of temperature and humidity input, and the prediction accuracy of LSTM is better than recurrent neural network (RNN). The method's feasibility was established, and the study provides theoretical support for guiding indoor air pollution control measures and ensuring human health and safety.
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| 6. |
- Wu, Mingxing, et al.
(författare)
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Economical Pt-Free Catalysts for Counter Electrodes of Dye-Sensitized Solar Cells
- 2012
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Ingår i: Journal of the American Chemical Society. - : American Chemical Society (ACS). - 0002-7863 .- 1520-5126. ; 134:7, s. 3419-3428
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Tidskriftsartikel (refereegranskat)abstract
- Three classes (carbides, nitrides and oxides) of nanoscaled early-transition-metal catalysts have been proposed to replace the expensive Pt catalyst as counter electrodes (CEs) in dye-sensitized solar cells (DSCs). Of these catalysts, Cr3C2, CrN, VC(N), VN, TiC, TiC(N), TiN, and V2O3 all showed excellent catalytic activity for the reduction of I-3(-) to I- in the electrolyte. Further, VC embedded in mesoporous carbon (VC-MC) was prepared through in situ synthesis. The I-3(-)/I- DSC based on the VC-MC CE reached a high power conversion efficiency (PCE) of 7.63%, comparable to the photovoltaic performance of the DSC using a Pt CE (7.50%). In addition, the carbide catalysts demonstrated catalytic activity higher than that of Pt for the regeneration of a new organic redox couple of T-2/T-. The T-2/T- DSCs using TiC and VC-MC CEs showed PCEs of 4.96 and 5.15%, much higher than that of the DSC using a Pt CE (3.66%). This work expands the list of potential CE catalysts, which can help reduce the cost of DSCs and thereby encourage their fundamental research and commercial application.
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