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Abstract The development of high-performance metal-free organic X-ray scintillators (OXSTs), characterized by a synergistic combination of robust X-ray absorption, efficient exciton utilization, and short luminescence lifetimes, poses a considerable challenge. Here we present an effective strategy for achieving augmented X-ray scintillation through the utilization of halogenated open-shell organic radical scintillators. Our experimental results demonstrate that the synthesized scintillators exhibit strong X-ray absorption derived from halogen atoms, display efficacious X-ray stability, and theoretically achieve 100% exciton utilization efficiency with a short lifetime (∼18 ns) due to spin-allowed doublet transitions. The superior X-ray scintillation performance exhibited by these organic radicals is not only exploitable in X-ray radiography for contrast imaging of various objects but also applicable in a medical high-resolution micro-computer-tomography system for the clear visualization of fibrous veins within a bamboo stick. Our study substantiates the promise of organic radicals as prospective candidates for OXSTs, offering valuable insights and a roadmap for the development of advanced organic radical scintillators geared towards achieving high-quality X-ray radiography.

Abstract Background Taraxacum kok-saghyz Rodin (TKS) is a highly potential source of natural rubber (NR) due to its wide range of suitable planting areas, strong adaptability, and suitability for mechanized planting and harvesting. However, current methods for detecting NR content are relatively cumbersome, necessitating the development of a rapid detection model. This study used near-infrared spectroscopy technology to establish a rapid detection model for NR content in TKS root segments and powder samples. The K445 strain at different growth stages within a year and 129 TKS samples hybridized with dandelion were used to obtain their near-infrared spectral data. The rubber content in the root of the samples was detected using the alkaline boiling method. The Monte Carlo sampling method (MCS) was used to filter abnormal data from the root segments of TKS and powder samples, respectively. The SPXY algorithm was used to divide the training set and validation set in a 3:1 ratio. The original spectrum was preprocessed using moving window smoothing (MWS), standard normalized variate (SNV), multiplicative scatter correction (MSC), and first derivative (FD) algorithms. The competitive adaptive reweighted sampling (CARS) algorithm and the corresponding chemical characteristic bands of NR were used to screen the bands. Partial least squares (PLS), random forest (RF), Lightweight gradient augmentation machine (LightGBM), and convolutional neural network (CNN) algorithms were employed to establish a model using the optimal spectral processing method for three different bands: full band, CARS algorithm, and chemical characteristic bands corresponding to NR. The model with the best predictive performance for high rubber content intervals (rubber content > 15%) was identified. Result The results indicated that the optimal rubber content prediction models for TKS root segments and powder samples were MWS–FD CASR–RF and MWS–FD chemical characteristic band RF, respectively. Their respective $${\text{R}}_{{\text{P}}}^{2}$$ R P 2 , RMSEP, and RPDP values were 0.951, 0.979, 1.814, 1.133, 4.498, and 6.845. In the high rubber content range, the model based on the LightGBM algorithm had the best prediction performance, with the RMSEP of the root segments and powder samples being 0.752 and 0.918, respectively. Conclusions This research indicates that dried TKS root powder samples are more appropriate for constructing a rubber content prediction model than segmented samples, and the predictive capability of root powder samples is superior to that of root segmented samples. Especially in the elevated rubber content range, the model formulated using the LightGBM algorithm has superior predictive performance, which could offer a theoretical basis for the rapid detection technology of TKS content in the future.

Abstract Creating circularly polarized organic afterglow system with elevated triplet energy levels, suppressed non-radiative transitions, and effective chirality, which are three critical prerequisites for achieving blue circularly polarized afterglow, has posed a formidable challenge. Herein, a straightforward approach is unveiled to attain blue circularly polarized afterglow materials by covalently self-confining isolated chiral chromophore within polymer matrix. The formation of robust hydrogen bonds within the polymer matrix confers a distinctly isolated and stabilized molecular state of chiral chromophores, endowing a blue emission band at 414 nm, lifetime of 3.0 s, and luminescent dissymmetry factor of ~ 10−2. Utilizing the synergistic afterglow and chirality energy transfer, full-color circularly polarized afterglow systems are endowed by doping colorful fluorescent molecules into designed blue polymers, empowering versatile applications. This work paves the way for the streamlined design of blue circularly polarized afterglow materials, expanding the horizons of circularly polarized afterglow materials into various domains.

The development of organic afterglow materials with sufficient color purity and high quantum efficiency for hyperafterglow is a great challenging. Here, the authors demonstrate an efficient strategy to achieve hyperafterglow emission through sensitizing and stabilizing isolated fluorescence chromophores by integrating multiresonance fluorescence chromophores into afterglow host in a singlecomponent copolymer.

Organic afterglow is a fascinating phenomenon with exceptional applications. However, it encounters challenges such as low intensity and efficiency, and typically requires UV-light excitation and facile intersystem crossing (ISC) due to its spin-forbidden nature. Here, we develop a novel strategy that bypasses the conventional ISC pathway by promoting singlet-triplet transition through the synergistic effects of the intra/intermolecular heavy-atom effect in aromatic crystals, enabling the direct population of triplet excited states from the ground state. The resulting materials exhibit a bright organic afterglow with a remarkably enhanced quantum efficiency of up to 5.81%, and a significantly increased organic afterglow lifetime of up to 157 microseconds under visible light. Moreover, given the high-efficiency visible-light excitable organic afterglow emission, the potential application is demonstrated in lifetime-resolved, color-encoded, and excitation wavelength-dependent pattern encryption. This work demonstrates the importance of the direct population method in enhancing the organic afterglow performance and red-shifting the excitation wavelength, and provides crucial insights for advancing organic optoelectronic technologies that involve triplet states.

Circularly polarized organic afterglow (CPOA) with both long-lived room-temperature phosphorescence and circularly polarized luminescence is attracting great interest, but the development of multicolor-tunable CPOA in a single-component material remains challenging. Here, the authors report a strategy to achieve multicolor CPOA through chiral clusterization by inserting a chirality center into a non-conjugated organic cluster.

Abstract High‐performance perovskite solar cells (PSCs) depend heavily on the quality of perovskite films, which is closely related to the lattice distortion, perovskite crystallization, and interfacial defects when being spin‐coated and annealed on the substrate surface. Here, a dynamic strategy to modulate the perovskite film formation by using a soft perovskite–substrate interface constructed by employing amphiphilic soft molecules (ASMs) with long alkyl chains and Lewis base groups is proposed. The hydrophobic alkyl chains of ASMs interacted with poly(triarylamine) (PTAA) greatly improve the wettability of PTAA to facilitate the nucleation and growth of perovskite crystals, while the Lewis base groups bound to perovskite lattices significantly passivate the defects in situ. More importantly, this soft perovskite–substrate interface with ASMs between PTAA and perovskite film can dynamically match the lattice distortion with reduced interfacial residual strain upon perovskite crystallization and thermal annealing owing to the soft self‐adaptive long‐chains, leading to high‐quality perovskite films. Thus, the inverted PSCs show a power conversion efficiency approaching 20% with good reproducibility and negligible hysteresis. More impressively, the unencapsulated device exhibits state‐of‐the‐art photostability, retaining 84% of its initial efficiency under continuous simulated 1‐sun illumination for more than 6200 h at elevated temperature (≈65 °C).

Osteoclasts undergo active metabolic reprogramming to acquire the energy needed during differentiation and bone resorption. Compared with immature osteoclasts, mature osteoclasts comprise higher levels of electron transport chain enzymes and more metabolically active mitochondria. Of all energy metabolism pathways, oxidative phosphorylation is considered to be the most efficient in supplying energy to osteoclasts. We found that the malate-aspartate shuttle inhibitor aminooxyacetic acid hemihydrochloride inhibits osteoclastogenesis and bone resorption by inhibiting exchange of reducing equivalents between the cytosol and the mitochondrial matrix and attenuating mitochondrial oxidative phosphorylation in vitro. The weakening of the oxidative phosphorylation pathway resulted in reduced mitochondrial function and inadequate energy supply along with reduced reactive oxygen species production. Furthermore, treatment with aminooxyacetic acid hemihydrochloride helped recover bone loss in ovariectomized mice. Our findings highlight the potential of interfering with the osteoclast intrinsic energy metabolism pathway as a treatment for osteoclast-mediated osteolytic diseases.

Scheduling is one of the key technologies used in unmanned aerial vehicle (UAV) swarms. Scheduling determines whether a task can be completed and when the task is complete. The distributed method is a fast way to realize swarm scheduling. It has no central node and UAVs can freely join or leave it, thus making it more robust and flexible. However, the two most representative methods, the Consensus-Based Bundle Algorithm (CBBA) and the Performance Impact (PI) algorithm, pursue the minimum cost impact of tasks, which have optimization limitations and are easily cause task conflicts. In this paper, a new concept called “task consideration” is proposed to quantify the impact of tasks on scheduling and the regression of the task itself, balancing the exploration of the UAV for the minimum-impact task and the regression of neighboring tasks to improve the optimization and convergence of scheduling. In addition, the conflict resolution rules are modified to fit the proposed method, and the exploration of tasks is increased by a new removal method to further improve the optimization. Finally, through extensive Monte Carlo experiments, compared with CBBA and PI, the proposed method is shown to perform better in terms of task allocation and total travel time, and with the increase in the number of average UAV tasks, the number of iterations is less and the convergence is faster.

An unmanned aerial vehicle (UAV) swarm has broad application prospects, in which scheduling is one of the key technologies determining the completion of tasks. A market-based approach is an effective way to schedule UAVs distributively and quickly, meeting the real-time requirements of swarm scheduling without a centre. In this paper, a double-loop framework is designed to enhance the performance of scheduling, where a new task removal method in the outer loop and a local redundant auction method in the inner loop are proposed to improve the optimization of scheduling and reduce iterations. Furthermore, a deadlock detection mechanism is introduced to avoid endless loops and the scheduling with the lowest local cost will be adopted to exit the cycle. Extensive Monte Carlo experiments show that the iterations required by the proposed method are less than the two representative algorithms consensus-based bundle algorithm (CBBA) and performance impact (PI) algorithm, and the number of allocated tasks is increased. In addition, through the deadlock avoidance mechanism, PI can completely converge as the method in this paper.

Coalition-based unmanned aerial vehicle (UAV) swarms havebeen widelyused in urgent missions. To fasten the completion, mobile edge computing (MEC) has been introduced into UAV networks where coalition leaders act as servers to help members with data computing. This paper investigates a relative delay optimization in MEC-assisted UAV swarms. Considering that the scheduling methods have great impact on the delay, some theoretical analyses are made and a scheduling method based on the shortest effective job first (SEJF) is proposed. Based on the coupled relationship between scheduling and resource allocation, the computation offloading and channel access problems are then jointly optimized. To solve the problem in distributed UAV networks, the optimization problem is formulated as an offloading game. It is proved that the game is an exact potential game (EPG) and it has at least one pure strategy Nash Equilibrium (PNE). To reach the PNE, a distributed offloading algorithm based on concurrent best-better response (CBBR) is designed. Finally, the simulations show that the performance of the proposed CBBR algorithm is better than traditional algorithms. Compared with other scheduling methods, the proposed scheduling method based on SEJF reduces the delay by up to 30%.

Though realizing organic materials with deep blue emission is attractive for next-generation display technologies, achieving this emission in afterglow molecules remains a challenge. Here, the authors report blue organic afterglow via a strategy involving guest sensitization and matrix rigidification.

The development of organic afterglow materials that do not contain heavy metals is of interest for biological applications. Here, the authors report on the development of a thermally activated organic molecule with tri-mode afterglow and an afterglow efficiency of up to 45%.

The unmanned swarm system (USS) has been seen as a promising technology, and will play an extremely important role in both the military and civilian fields such as military strikes, disaster relief and transportation business. As the “nerve center” of USS, the unmanned swarm communication system (USCS) provides the necessary information transmission medium so as to ensure the system stability and mission implementation. However, challenges caused by multiple tasks, distributed collaboration, high dynamics, ultra-dense and jamming threat make it hard for USCS to manage limited spectrum resources. To tackle with such problems, the machine learning (ML) empowered intelligent spectrum management technique is introduced in this paper. First, based on the challenges of the spectrum resource management in USCS, the requirement of spectrum sharing is analyzed from the perspective of spectrum collaboration and spectrum confrontation. We found that suitable multi-agent collaborative decision making is promising to realize effective spectrum sharing in both two perspectives. Therefore, a multi-agent learning framework is proposed which contains mobile-computing-assisted and distributed structures. Based on the framework, we provide case studies. Finally, future research directions are discussed.

UAV swarms have attracted great attention, and are expected to be used in scenarios, such as search and rescue, that require many urgent jobs to be completed in a minimum time by multiple vehicles. For complex missions with tight constraints, careful assigning tasks is inseparable from the scheduling of these tasks, and multi-task distributed scheduling (MTDS) is required. The Performance Impact (PI) algorithm is an excellent solution for MTDS, but it suffers from the suboptimal solution caused by the heuristics for local task selection, and the deadlock problem that it may fall into an infinite cycle of exchanging the same task. In this paper, we improve the PI algorithm by integrating a new task-removal strategy and a conflict prediction mechanism into the task-removal phase and the task-inclusion phase, respectively. Specifically, the task-removal strategy results in better exploration of the inclusion of more tasks than the original PI by freeing up more space in the local scheduler, improving the suboptimal solution caused by the heuristics for local task selection, as done in PI. In addition, we design a conflict prediction mechanism that simulates adjacent vehicles performing inclusion operations as the criteria for local task inclusion. Therefore, it can reduce the deadlock ratio and iteration times of the MTDS algorithm. Furthermore, by combining the protocol stack with the physical transmission model, an ad-hoc network simulation platform is constructed, which is closer to the real-world network, and serves as the supporting environment for testing the MTDS algorithms. Based on the constructed ad-hoc network simulation platform, we demonstrate the advantage of the proposed algorithm over the original PI algorithm through Monte Carlo simulation of search and rescue tasks. The results show that the proposed algorithm can reduce the average time cost, increase the total allocation number under most random distributions of vehicles-tasks, and significantly reduce the deadlock ratio and the number of iteration rounds.

Distributed task scheduling is an ongoing concern in the field of multi-vehicles, especially in recent years; UAV swarm performing complex tasks endows it with new characteristics, such as self-organization, scalability, reconfigurability, etc. This requires the swarm to have distributed rescheduling capability to dynamically include as many unassigned tasks or new tasks as possible, while satisfying tight time constraints. As one of the most advanced rescheduling methods, the Performance Impact (PI)-MaxAss algorithm provides an important reference for this paper. However, its task exchange-based strategy faces the deadlock problem, and the task rescheduling method should not be limited to this. To this end, a new distributed rescheduling method is proposed for UAV swarms, which combines the local task reordering strategy and the improved task exchange strategy. On the one hand, based on the analysis of the fact that the scheduler is unreasonable for individuals, this paper proposes a local task reordering strategy denoted as PI-Reorder, which simply adds the reordering strategy to the recursive inclusion phase of the PI-MinAvg algorithm, so that unassigned tasks or new tasks can be included without relying on the task exchange. On the other hand, from the phenomenon that two or more vehicles occasionally get caught in an infinite cycle of exchanging the same tasks, the deadlock problem of PI-MaxAss is analyzed, which is then solved by introducing a deadlock-free task exchange strategy, where some defined counters are used to detect and isolate the deadlocks. Then, a rescue scenario is used to demonstrate the performance of the proposed methods, PI-Hybrid compared with PI-MaxAss. Monte Carlo simulation results show that, compared with PI-MaxAss, this method can not only increase the number of allocations to varying degrees, but also reduce the average waiting time, while ensuring deadlock avoidance. The methods can be used not only for the secondary optimization of the existing task exchange scheduling algorithms to escape local optima, but also for task reconfiguration of swarm tasks after adding or removing tasks.

This article was submitted to Experimental Pharmacology and Drug Discovery, a section of the journal Frontiers in Pharmacology. Postmenopausal osteoporosis (PMOP), which increases the risk of fracture, is the most common bone disease in women. PMOP not only increases the risk of death but also imposes a financial burden on countless families. At present, most of the drugs used to treat osteoporosis have significant side effects, so it is important to find effective anti-osteoporosis medications without major side effects. Sesamolin (Ses) is a kind of natural lignan extracted from sesame oil. Many researches have shown that Ses has anti-inflammatory, antioxidative, and anticancer effects, however it is still unknown whether it has any effect on osteoporosis. In this research, we explored the therapeutic effect of Ses in the process of osteoclast formation and bone resorption and found that Ses effectively inhibited osteoclast formation in vitro through TRAcP staining and hydroxyapatite resorption assays. Through Western blot analysis of the NF-κB pathway, MAPK pathway, c-Fos and NFATc1, it was found that Ses not only effectively inhibited the activation of NF-κB and MAPK signaling pathways induced by RANKL but also significantly reduced the protein expression of c-Fos and NFATc1. Several genes specifically expressed in osteoclasts were determined by qPCR, and Ses was also found to play a significant inhibitory role on the expression of these genes. Besides, an osteoporosis model induced in ovariectomized (OVX) mice was employed to verify that Ses could effectively reduce bone loss caused by estrogen deficiency in vivo. In conclusion, Ses showed promise as a new treatment for postmenopausal osteoporosis.

Inverted perovskite solar cells (PSCs) often employ triphenylamine‐based materials, such as poly(triarylamine) (PTAA), as hole‐transporting layer (HTL). However, the high hydrophobic PTAA usually induces unsatisfactory perovskite growth, resulting in inferior efficiency and reproducibility. Herein, the vanadium oxide (VOx) film is introduced to modify hydrophobic PTAA layer and establish a hydrophilic surface for high‐quality perovskite film due to the decreased surface tension with improved perovskite solution spread, nucleation, and growth. The PSCs based on PTAA/VOx HTL exhibit high power conversion efficiency up to 18.9% with a sharp increase in comparison to the 16.5% of the control device. Moreover, the reproducibility is also significantly improved, showing a much smaller standard deviation of 1.75 for 40 devices compared to the control devices of 3.64. This work demonstrates that the introduction of VOx is a promising approach in producing high‐quality perovskite films on poor‐wetting PTAA layer for high‐performance inverted PSCs.

Organic semiconductors with bipolar transporting character are highly attractive as they offer the possibility to achieve high optoelectronic performance in simple device structures. However, the continual efforts in preparing bipolar materials are focusing on donor-acceptor (D-A) architectures by introducing both electron-donating and electron-withdrawing units into one molecule in static molecular design principles. Here, we report a dynamic approach to construct bipolar materials using only electron-donating carbazoles connected by N-P=X resonance linkages in a donor-resonance-donor (D-r-D) structure. By facilitating the stimuli-responsive resonance variation, these D-r-D molecules exhibit extraordinary bipolar properties by positively charging one donor of carbazole in enantiotropic N+=P-X- canonical forms for electron transport without the involvement of any acceptors. With thus realized efficient and balanced charge transport, blue and deep-blue phosphorescent organic light emitting diodes hosted by these D-r-D molecules show high external quantum efficiencies up to 16.2% and 18.3% in vacuum-deposited and spin-coated devices, respectively. These results via the D-r-D molecular design strategy represent an important concept advance in constructing bipolar organic optoelectronic semiconductors dynamically for high-performance device applications.

Trap-assisted recombination loss in the cathode buffer layers (CBLs) is detrimental to the electron extraction process and severely restricts the power conversion efficiencies (PCEs) of organic solar cells (OSCs). Herein, a novel organic–inorganic hybrid film composed of zinc oxide (ZnO) and 2,3,5,6-tetrafluoro-7,7,8, 8-tetracyanoquinodimethane (F4TCNQ) is designed to fill the intrinsic charge traps of ZnO-based CBLs by doping F4TCNQ for high-performance inverted OSCs. Thus, constructed ZnO:F4TCNQ hybrid film exhibits enhanced surface hydrophobicity and adjustable energy levels, providing favorable interfacial condition for electron extraction process. Consequently, trap-assisted recombination loss in the CBLs was efficiently suppressed, leading to the significantly improved fill factor and PCEs of both fullerene- and non-fullerene-based OSCs using the ZnO:F4TCNQ hybrid CBLs. This work illustrates a convenient organic acceptor doping approach to suppress the internal charge traps of traditional inorganic CBLs, which will shed new light on the fabrication of high-performance CBLs with facile electron extraction processes in inverted OSC devices.

The exploitation of thermally activated delayed fluorescence (TADF) emitters with aggregation-induced emission is highly prerequisite for the construction of highly efficient electroluminescent devices in materials science. Herein, two asymmetric TADF emitters of SFCOCz and SFCODPAC with charming aggregation-induced emission are expediently designed and prepared based on highly twisted strong electron-withdrawing acceptor (A) of sulfurafluorene (SF)-modified ketone (CO) and arylamine donor (D) in D1−A–D2 architecture by simple synthetic procedure in high yields. High photoluminescence quantum yields up to 73% and small singlet–triplet splitting of 0.03 eV; short exciton lifetimes are obtained in the resultant molecules. Strikingly, efficient non-doped and doped TADF organic light-emitting diodes (OLEDs) facilitated by these emitters show high luminance of 5,598 and 11,595 cd m−2, current efficiencies (CEs) of 16.8 and 35.6 cd/A, power efficiencies (PEs) of 9.1 and 29.8 lm/W, and external quantum efficiencies (EQEs) of 7.5 and 15.9%, respectively. This work furnishes a concrete instance in exploring efficient TADF emitter, which is highly conducive and encouraging in stimulating the development of TADF OLEDs with high brightness and excellent efficiencies simultaneously.

Organic ultralong room-temperature phosphorescence (OURTP) with a long-lived triplet excited state up to several seconds has triggered widespread research interests, but most OURTP materials are excited by only ultraviolet (UV) or blue light owing to their unique stabilized triplet- and solid-state emission feature. Here, we demonstrate that near-infrared- (NIR-) excitable OURTP molecules can be rationally designed by implanting intra/intermolecular charge transfer (CT) characteristics into H-aggregation to stimulate the efficient nonlinear multiphoton absorption (MPA). The resultant upconverted MPA-OURTP show ultralong lifetimes over 0.42 s and a phosphorescence quantum yield of ~37% under both UV and NIR light irradiation. Empowered by the extraordinary MPA-OURTP, novel applications including two-photon bioimaging, visual laser power detection and excitation, and lifetime multiplexing encryption devices were successfully realized. These discoveries illustrate not only a delicate design map for the construction of NIR-excitable OURTP materials but also insightful guidance for exploring OURTP-based nonlinear optoelectronic properties and applications.

The faults of the landing gear retraction/extension(R/E) system can result in the deterioration of an aircraft’s maneuvering conditions; how to identify the faults of the landing gear R/E system has become a key issue for ensuring aircraft take-off and landing safety. In this paper, we aim to solve this problem by proposing the 1-D dilated convolutional neural network (1-DDCNN). Aiming at developing the limited feature information extraction and inaccurate diagnosis of the traditional 1-DCNN with a single feature, the 1-DDCNN selects multiple feature parameters to realize feature integration. The performance of the 1-DDCNN in feature extraction is explored. Importantly, using padding dilated convolution to multiply the receptive field of the convolution kernel, the 1-DDCNN can completely retain the feature information in the original signal. Experimental results demonstrated that the proposed method has high accuracy and robustness, which provides a novel idea for feature extraction and fault diagnosis of the landing gear R/E system.

The class of BODIPY dyes has high solubility and high quantum yields and is widely used in imaging applications. Here Tian et al. synthesize new dye molecules and demonstrate extended emission properties and application scope controllable both by the excitation wavelength and aggregation states.

PHM technology plays an increasingly significant role in modern aviation condition-based maintenance. As an important part of prognostics and health management (PHM), a health assessment can effectively estimate the health status of a system and provide support for maintenance decision making. However, in actual conditions, various uncertain factors will amplify assessment errors and cause large fluctuations in assessment results. In this paper, uncertain factors are incorporated into flight control system health assessment modeling. First, four uncertain factors of health assessment characteristic parameters are quantified and described by the extended λ-PDF method to acquire their probability distribution function. Secondly, a Monte Carlo simulation (MCS) is used to simulate a flight control system health assessment process with uncertain factors. Thirdly, the probability distribution of the output health index is solved by the maximum entropy principle. Finally, the proposed model was verified with actual flight data. The comparison between assessment results with and without uncertain factors shows that a health assessment conducted under uncertain conditions can reduce the impact of the uncertainty of outliers on the assessment results and make the assessment results more stable; therefore, the false alarm rate can be reduced.

Three phosphine sulfide-based bipolar host materials, viz CzPhPS, DCzPhPS, and TCzPhPS, were facilely prepared through a one-pot synthesis in excellent yields. The developed hosts exhibit superior thermal stabilities with the decomposition temperatures (Td) all exceeding 350 °C and the melting temperatures (Tm) over 200 °C. In addition, their triplet energy (ET) levels are estimated to be higher than 3.0 eV, illustrating that they are applicable to serve as hosts for blue phosphorescent organic light-emitting diodes (PhOLEDs). The maxima luminance, current efficiency (CE), power efficiency (PE), and external quantum efficiency (EQE) of 17,223 cd m−2, 36.7 cd A−1, 37.5 lm W−1, and 17.5% are achieved for the blue PhOLEDs hosted by CzPhPS. The PhOLEDs based on DCzPhPS and TCzPhPS show inferior device performance than that of CzPhPS, which might be ascribed to the deteriorated charge transporting balance as the increased number of the constructed carbazole units in DCzPhPS and TCzPhPS molecules would enhance the hole-transporting ability of the devices to a large extent. Our study demonstrates that the bipolar hosts derived from phosphine sulfide have enormous potential applications in blue PhOLEDs, and the quantity of donors should be well controlled to exploit highly efficient phosphine sulfide-based hosts.

Abstract Exciton transformation, a non-radiative process in changing the spin multiplicity of an exciton usually between singlet and triplet forms, has received much attention recently due to its crucial effects in manipulating optoelectronic properties for various applications. However, current understanding of exciton transformation mechanism does not extend far beyond a thermal equilibrium of two states with different multiplicity and it is a significant challenge to probe what exactly control the transformation between the highly active excited states. Here, based on the recent developments of three types of purely organic molecules capable of efficient spin-flipping, we perform ab initio structure/energy optimization and similarity/overlap extent analysis to theoretically explore the critical factors in controlling the transformation process of the excited states. The results suggest that the states having close energy levels and similar exciton characteristics with same transition configurations and high heteroatom participation are prone to facilitating exciton transformation. A basic guideline towards the molecular design of purely organic materials with facile exciton transformation ability is also proposed. Our discovery highlights systematically the critical importance of vertical transition configuration of excited states in promoting the singlet/triplet exciton transformation, making a key step forward in excited state tuning of purely organic optoelectronic materials.

This paper tackles the problem of sensing coverage for multiple Unmanned Aerial Vehicles (UAVs) with an approach that takes into account the reciprocal between neighboring UAVs to reduce the oscillation of their trajectories. The proposed reciprocal decision approach, which is performed in three steps, is self-organized, distributed and autonomous. First, in contrast to the traditional method modeled and optimized in configuration space, the sensing coverage problem is directly presented as an optimal reciprocal coverage velocity (ORCV) in velocity space that is concise and effective. Second, the ORCV is determined by adjusting the action velocity out of weak coverage velocity relative to neighboring UAVs to demonstrate that the ORCV supports a collision-avoiding assembly. Third, a corresponding random probability method is proposed for determining the optimal velocity in the ORCV. The results from the simulation indicate that the proposed method has a high coverage rate, rapid convergence rate and low deadweight loss. In addition, for up to 103-size UAVs, the proposed method has excellent scalability and collision-avoiding ability.