ABSTRACT
Dual-band electrochromic energy storage (DEES) windows, which are capable of selectively controlling visible (VIS) and near-infrared (NIR) light transmittance, have attracted research attention as energy-saving devices that integrate electrochromic (EC) and energy storage functions. However, there are few EC materials with spectrally selective modulation. Herein, oxygen vacancy modulated amorphous tungsten oxide (a-WO3-x-OV) is firstly shown to be a potential material for DEES windows. Furthermore, experimental results and density functional theory (DFT) calculations demonstrate that an oxygen vacancy not only enables the a-WO3-x-OV films to modulate NIR light transmittance selectively, but also enhances ion adsorption and diffusion in the a-WO3-x host to obtain excellent EC performance and a large energy storage capacity. Consequently, the a-WO3-x-OV film can selectively control VIS and NIR light transmittance with a state-of-the-art EC performance, including high optical modulation (91.8% and 80.3% at 633 and 1100 nm, respectively), an unprecedentedly fast switching speed (tb/tc = 4.1/5.3 s), high coloration efficiency (167.96 cm2 C-1), high specific capacitance (314 F g-1 at 0.5 A g-1), and ultra-robust cycling stability (83.3% optical modulation retention after 8000 cycles). The fast-switching and ultra-stable dual-band EC properties with efficient energy recycling are also successfully demonstrated in a DEES prototype. The results demonstrate that the a-WO3-x-OV films show great potential for application in high-performance DEES smart windows.
ABSTRACT
The energy consumption in building ventilation, air, and heating conditioning systems, accounts for about 25% of the overall energy consumption in modern society. Therefore, cutting carbon emissions and reducing energy consumption is a growing priority in building construction. Electrochromic devices (ECDs) are considered to be a highly promising energy-saving technology, due to their simple structure, active control, and low energy input characteristics. At present, H+, OH- and Li+ are the main electrolyte ions used for ECDs. However, H+ and OH- based electrolytes have a high erosive effect on the material surface and have a relatively short lifetime. Li+-based electrolytes are limited due to their high cost and safety concerns. In this study, inspired by prior research on Ca2+ batteries and supercapacitors, CaF2 films were prepared by electron beam evaporation as a Ca2+-based electrolyte layer to construct ECDs. The structure, morphology, and optical properties of CaF2 films were characterized. ECDs with the structure of ITO (indium tin oxide) glass/WO3/CaF2/NiO/ITO show short switching times (22.8 s for the coloring process, 2.8 s for the bleaching process). Additionally, optical modulation of the ECDs is about 38.8% at 750 nm. These findings indicate that Ca2+ based ECDs have the potential to become a competitive and attractive choice for large-scale commercial smart windows.
ABSTRACT
OBJECTIVES: A sample with a blood clot may produce an inaccurate outcome in coagulation testing, which may mislead clinicians into making improper clinical decisions. Currently, there is no efficient method to automatically detect clots. This study demonstrates the feasibility of utilizing machine learning (ML) to identify clotted specimens. METHODS: The results of coagulation testing with 192 clotted samples and 2,889 no-clot-detected (NCD) samples were retrospectively retrieved from a laboratory information system to form the training dataset and testing dataset. Standard and momentum backpropagation neural networks (BPNNs) were trained and validated using the training dataset with a five-fold cross-validation method. The predictive performances of the models were then assessed based on the testing dataset. RESULTS: Our results demonstrated that there were intrinsic distinctions between the clotted and NCD specimens regarding differences in the testing results and the separation of the groups (clotted and NCD) in the t-SNE analysis. The standard and momentum BPNNs could identify the sample status (clotted and NCD) with areas under the ROC curves of 0.966 (95% CI, 0.958-0.974) and 0.971 (95% CI, 0.9641-0.9784), respectively. CONCLUSIONS: Here, we have described the application of ML algorithms in identifying the sample status based on the results of coagulation testing. This approach provides a proof-of-concept application of ML algorithms to evaluate the sample quality, and it has the potential to facilitate clinical laboratory automation.
Subject(s)
Laboratories, Clinical , Noncommunicable Diseases , Algorithms , Blood Coagulation Tests , Humans , Machine Learning , Retrospective StudiesABSTRACT
A blue emitting phosphor Li2Sr0.9Mg0.1SiO4:Ce3+, with long persistence, was synthesized via a high-temperature solid phase method. According to the X-ray diffraction analysis result, the introduction of Mg2+ and Ce3+ ions has no influence on the structure of the host material. Typical 5d-2F5/2 and 5d-2F7/2 transitions of Ce3+ ions were detected by PL spectra, which corresponded to the CIE chromaticity coordinates of x = 0.1584, y = 0.0338. An optimal doping concentration of Ce3+ was determined as of 0.4 at%. Furthermore, the Li2Sr0.9Mg0.1SiO4:Ce3+ phosphor showed a typical triple-exponential afterglow behavior when the UV source was switched off. The highest lifetime of the electrons within the material reached a value of 73.9 s. Thermal stimulated luminescence study indicated that the afterglow of Li2Sr0.9Mg0.1SiO4:Ce3+ was due to the recombination of the electrons with holes released from the traps generated by the doping of Ce3+ ions in the Li2Sr0.9Mg0.1SiO4 host. The afterglow mechanism of Li2Sr0.9Mg0.1SiO4:Ce3+ is illustrated and discussed in detail on the basis of the experimental results.
