Functional soft materials exhibiting distinct functionalities in response to a specific stimulus are highly desirable towards the fabrication of advanced devices with superior dynamic performances. ...Herein, two novel light‐driven chiral fluorescent molecular switches have been designed and synthesized that are able to exhibit unprecedented reversible Z/E photoisomerization behavior along with tunable fluorescence intensity in both isotropic and anisotropic media. Cholesteric liquid crystals fabricated using these new fluorescent molecular switches as chiral dopants exhibit reversible reflection color tuning spanning the visible and infrared region of the spectrum. Transparent display devices have been fabricated using both low chirality and high chirality cholesteric films that operate either exclusively in fluorescent mode or in both fluorescent and reflection mode, respectively. The dual mode display device employing short pitch cholesteric film is able to function on demand under all ambient light conditions including daylight and darkness with fast response and high resolution. Moreover, the proof‐of‐concept for a “remote‐writing board” using cholesteric films containing one of the light‐driven chiral fluorescent molecular switches with ease of fabrication and operation is disclosed herein. Such optically rewritable transparent display devices enabled by light‐driven chiral fluorescent molecular switches pave a new way for developing novel display technology under different lighting conditions.
Reversible photoresponsive chiral fluorescent molecular switches for optically rewritable transparent cholesteric liquid crystal display devices are developed. Both single‐luminescent‐mode and dual‐reflective‐photoluminescent‐mode displays with desirable resolutions are demonstrated.
Reported here is the first example of a 1,2‐dithienyldicyanoethene‐based visible‐light‐driven chiral fluorescent molecular switch that exhibits reversible trans to cis photoisomerization. The trans ...form in solution almost completely transforms into the cis form, accompanied by a 10‐fold decrease in its fluorescence intensity within 60 seconds when exposed to green light (520 nm). The reverse isomerization proceeds upon irradiation with blue light (405 nm). When doped into commercially available achiral liquid crystal hosts, this molecular switch efficiently induces luminescent helical superstructures, that is, a cholesteric phase. The intensity of the circularly polarized fluorescence as well as the selective reflection wavelength of the induced cholesteric phases can be reversibly tuned using visible light of two different wavelengths. Optically rewritable photonic devices using cholesteric films containing this molecular switch are described.
Write away: A distinct 1,2‐dithienyldicyanoethene‐based light‐driven chiral fluorescent molecular switch that exhibits reversible trans to cis photoisomerization in both isotropic solvents and a liquid crystal medium, upon visible‐light irradiation, is accomplished. Optically rewritable multimodal liquid crystal photonic devices based on this switch are demonstrated. CPL=circularly polarized luminescence, CPR=circularly polarized reflection.
The unmanned warehouse dispatching system of the ‘goods to people’ model uses a structure mainly based on a handling robot, which saves considerable manpower and improves the efficiency of the ...warehouse picking operation. However, the optimal performance of the scheduling system algorithm has high requirements. This study uses a deep Q-network (DQN) algorithm in a deep reinforcement learning algorithm, which combines the Q-learning algorithm, an empirical playback mechanism, and the volume-based technology of productive neural networks to generate target Q-values to solve the problem of multi-robot path planning. The aim of the Q-learning algorithm in deep reinforcement learning is to address two shortcomings of the robot path-planning problem: slow convergence and excessive randomness. Preceding the start of the algorithmic process, prior knowledge and prior rules are used to improve the DQN algorithm. Simulation results show that the improved DQN algorithm converges faster than the classic deep reinforcement learning algorithm and can more quickly learn the solutions to path-planning problems. This improves the efficiency of multi-robot path planning.
Through the development of large-scale natural language models with writing and dialogue capabilities, artificial intelligence (AI) has taken a significant stride towards better natural language ...understanding (NLU) and human-computer interaction (HCI). As of today, the GPT-3 model, developed by OpenAI, is the language model with the most parameters, the largest scale, and the strongest capabilities. Using a large amount of Internet text data and thousands of books for model training, GPT-3 can imitate the natural language patterns of humans nearly perfectly. This language model is extremely realistic and is considered the most impressive model as of today.
Despite its powerful modeling and description capabilities, there are significant issues and limitations. First and foremost, the GPT-3 model does not understand writing (natural language generation) well and sometimes generates uncontrollable content. Secondly, training the GPT-3 model requires a large amount of computing power, data, and capital investment, and releases significant carbon dioxide emissions. Developing similar models is only possible in laboratories with adequate resources. Furthermore, as the GPT-3 model is trained with Internet text data rife with error messages and prejudices, it often produces chapters and paragraphs with biased content similar to the training data.①①Original source in Chinese: M. Zhang, J. Li, GPT-3, Bulletin of National Natural Science Foundation of China. 35 (3) (2021) 403-406.
Polymer passivation layers can improve the open-circuit voltage of perovskite solar cells when inserted at the perovskite-charge transport layer interfaces. Unfortunately, many such layers are poor ...conductors, leading to a trade-off between passivation quality (voltage) and series resistance (fill factor, FF). Here, we introduce a nanopatterned electron transport layer that overcomes this trade-off by modifying the spatial distribution of the passivation layer to form nanoscale localized charge transport pathways through an otherwise passivated interface, thereby providing both effective passivation and excellent charge extraction. By combining the nanopatterned electron transport layer with a dopant-free hole transport layer, we achieved a certified power conversion efficiency of 21.6% for a 1-square-centimeter cell with FF of 0.839, and demonstrate an encapsulated cell that retains ~91.7% of its initial efficiency after 1000 hours of damp heat exposure.
While silicon is considered one of the most promising anode materials for the next generation of high‐energy lithium‐ion batteries (LIBs), the industrialization of Si anodes is hampered by the ...anode's large volume change during the charging and discharging process. In comparison to the traditional graphite anode used in LIBs, the Si anode places more stringent demands on the binder, which must maintain intimate contact between the electrode components and the integrity of the ion and electron transport channels when subjected to frequent large volume changes. The purpose of this review is to cover the recent advances in binder design strategies by examining the molecular structure, chemical functionalities, physical and mechanical properties of the binder materials, as well as the working strategies involved. The challenges in the design of the innovative polymer binder for commercializing Si anodes are discussed, as well as the future development direction and application prospects.
Design criteria for silicon‐based anode binders in half and full cells: an appropriate binder contributes to the structural stability of the Si electrode; ionic and electrical conductivity, high mechanical characteristics, self‐healing function, and interfacial compatibility are critical considerations in the creation of a suitable binder for Si anodes.
Subwavelength imaging requires the use of high numerical aperture (NA) lenses together with immersion liquids in order to achieve the highest possible resolution. Following exciting recent ...developments in metasurfaces that have achieved efficient focusing and novel beam-shaping, the race is on to demonstrate ultrahigh-NA metalenses. The highest NA that has been demonstrated so far is NA = 1.1, achieved with a TiO2 metalens and back-immersion. Here, we introduce and demonstrate a metalens with a high NA and high transmission in the visible range, based on crystalline silicon (c-Si). The higher refractive index of silicon compared to TiO2 allows us to push the NA further. The design uses the geometric phase approach also known as the Pancharatnam–Berry (P–B) phase, and we determine the arrangement of nanobricks using a hybrid optimization algorithm (HOA). We demonstrate a metalens with NA = 0.98 in air, a bandwidth (full width at half-maximum, fwhm) of 274 nm, and a focusing efficiency of 67% at 532 nm wavelength, which is close to the transmission performance of a TiO2 metalens. Moreover, and uniquely so, our metalens can be front-immersed into immersion oil and achieve an ultrahigh NA of 1.48 experimentally and 1.73 theoretically, thereby demonstrating the highest NA of any metalens in the visible regime reported to the best of our knowledge. The fabricating process is fully compatible with microelectronic technology and therefore scalable. We envision the front-immersion design to be beneficial for achieving ultrahigh-NA metalenses as well as immersion metalens doublets, thereby pushing metasurfaces into practical applications such as high resolution, low-cost confocal microscopy and achromatic lenses.
A number of non‐noble catalysts are developed for hydrogen production via acidic water electrolysis. Nevertheless, for the more economical alkaline hydrogen generation, the restricted kinetics of the ...water dissociation Volmer step along with its following proton recombination Tafel step for these non‐noble electrocatalysts generally lead to sluggish hydrogen‐production process. Here, a facile method is designed to nest nanometric Ni5P4 clusters on NiCo2O4 (achieving Ni5P4@NiCo2O4) by a phosphating process of NiO clusters on NiCo2O4. Acting as a high‐efficiency electrode for alkaline water electrolysis, the Ni5P4@NiCo2O4 can efficiently and preferentially convert H2O to H2 with a low overpotential of 27 mV at 10 mA cm−2 and the Tafel slope of 27 mV dec−1, which are comparable to the results for platinum and superior than those of the state‐of‐the‐art platinum‐free electrocatalysts. Density functional theory calculations confirm that NiCo2O4 species exhibit a higher ability to electrolyze water into H* intermediate and then Ni5P4 clusters facilitate the subsequent desorption of the H2 products. Profiting from the promoted kinetic steps, the Ni5P4@NiCo2O4 electrocatalyst is promising for scalable alkaline hydrogen production.
Nanometric Ni5P4 clusters nested on NiCo2O4 for highly efficient electrochemical hydrogen production are first demonstrated. Owing to the largely reduced energy barriers of both Volmer step and Tafel step, the as‐achieved Ni5P4@NiCo2O4 electrocatalyst exhibits a high hydrogen evolution reaction activity under alkaline conditions, which is highly comparable to that of Pt and outperforms many reported non‐noble electrocatalysts.
Breast cancer is one of the most common cancers in women. It is necessary to classify breast cancer subtypes because different subtypes need specific treatment. Identifying biomarkers and classifying ...breast cancer subtypes is essential for developing appropriate treatment methods for patients. MiRNAs can be easily detected in tumor biopsy and play an inhibitory or promoting role in breast cancer, which are considered promising biomarkers for distinguishing subtypes. A new method combing ensemble regularized multinomial logistic regression and Cox regression was proposed for identifying miRNA biomarkers in breast cancer. After adopting stratified sampling and bootstrap sampling, the most suitable sample subset for miRNA feature screening was determined via ensemble 100 regularized multinomial logistic regression models. 124 miRNAs that participated in the classification of at least 3 subtypes and appeared at least 50 times in 100 integrations were screened as features. 22 miRNAs from the proposed feature set were further identified as the biomarkers for breast cancer by using Cox regression based on survival analysis. The accuracy of 5 methods on the proposed feature set was significantly higher than on the other two feature sets. The results of 7 biological analyses illustrated the rationality of the identified biomarkers. The screened features can better distinguish breast cancer subtypes. Notably, the genes and proteins related to the proposed 22 miRNAs were considered oncogenes or inhibitors of breast cancer. 9 of the 22 miRNAs have been proved to be markers of breast cancer. Therefore, our results can be considered in future related research.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
As one of the most important semiconductors, silicon has been used to fabricate electronic devices, waveguides, detectors, solar cells, etc. However, the indirect bandgap and low quantum efficiency ...(10
) hinder the use of silicon for making good emitters. For integrated photonic circuits, silicon-based emitters with sizes in the range of 100-300 nm are highly desirable. Here, we show the use of the electric and magnetic resonances in silicon nanoparticles to enhance the quantum efficiency and demonstrate the white-light emission from silicon nanoparticles with feature sizes of ~200 nm. The magnetic and electric dipole resonances are employed to dramatically increase the relaxation time of hot carriers, while the magnetic and electric quadrupole resonances are utilized to reduce the radiative recombination lifetime of hot carriers. This strategy leads to an enhancement in the quantum efficiency of silicon nanoparticles by nearly five orders of magnitude as compared with bulk silicon, taking the three-photon-induced absorption into account.
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