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Three polymorphs of a N,S-chelated four-coordinated difluoroboron-based emitter display high contrast green (G), yellow (Y) and red (R) emissions with TADF character for the G- and R-Crystals, and RTP for the Y-Crystal, tuned by different molecular conformations and packings.

In this paper, we propose to use the data augmentation of batch drop-block with varying dropping ratios for constructing diversity-achieving branches in person re-identification. Since a considerable portion of input images may be dropped, this reinforces feature learning of the un-dropped region but makes the training process hard to converge. Hence, we propose a novel double-batch-split co-training approach for remedying this problem. In particular, we show that the feature diversity can be well achieved with the use of multiple dropping branches by setting individual dropping ratio for each branch. Empirical evidence demonstrates that the proposed method performs competitively on popular person Re-ID datasets, including Market-1501, DukeMTMC-reID and CUHK03, and the use of more dropping branches can further boost the performance. Source code is available at url.

The organic emitters based on 9H-selenoxanthen-9-one can realize fluorescent-to-phosphorescent transformation at room temperature by structural isomerism, and the room temperature phosphorescence quantum yield of the doped film is up to 66.7%.

Metal-free star-shaped organic luminogens exhibit persistent phosphorescence in neat films with phosphorescence lifetime up to 166 ms under ambient conditions, which can be used in information encryption and white electroluminescent devices.

Increasing the twist angles between donor and acceptor units in 9H-selenoxanthen-9-one-based emitters can modulate the lowest triplet excited states (3LE or 3CT), achieving pure RTP and dual emission of RTP and TADF, respectively.

Deep learning models are widely used in the field of Device-Free Localization (DFL), and their performances rely heavily on the labeled dataset. In this letter, a novel data preprocessing method is proposed for exploiting the full potential of a collected dataset in DFL. Firstly, we propose to employ a pix-level multimodal representation of radio images for a fusion of both amplitude and phase features from Channel State Information (CSI). Then, a novel Conditional Generative Adversarial Network with Auxiliary Classifier (AC-GAN) model is used to generate artificial samples for further expanding the collected dataset. We further employ a regression formulation to train the Convolutional Neural Networks (CNNs) for positioning. The experimental results show that using the proposed method gains approximately 18% Root-Mean-Square-Error (RMSE) improvement over the CSI amplitude-only method and 22% over the CSI phase-only method.

In this paper, a novel decoupled device-free activity detection and position estimation scheme is proposed using convolutional neural networks and channel state information (CSI) measurement as inputs. For the proposed scheme, the two processes of activity recognition and localization are realized in parallel but independently. Compared with the existing joint approaches, the proposed decoupled scheme is free of error propagation between two processes and achieves better performance especially for position estimation. We also propose a CSI based radio image construction using the amplitude measurement with temporal, spatial and frequency domain information. This has been proven very competitive for feature extraction, compared with the state-of-the-art methods. Extensive experimental and simulation results under a real test setup show the superiority of the proposed scheme.

Device activity detection has been extensively investigated for grant-free massive machine-type communications. Instead of using deep Multi-Layer Perception (MLP) networks, this letter proposes a novel convolutional neural network (CNN) architecture for learning device activity from multiple-measurement vectors (MMV). With the use of $1\times 1$ convolutional layers, the proposed CNN could exploit the full potential of shared sparsity among multiple measurements. Extensive simulations show that the proposed CNN outperforms the existing deep MLP network in both performance and computational complexity, especially when the number of measurements increases.

Thermally activated delayed fluorescent (TADF) materials based tetracoordinate boron acceptors hold great potential for organic light emitting diode (OLED) applications; however, these emitters suffer from a low luminescent efficiency (ΦPL) and a large device efficiency roll-off. Herein, we design and synthesize two D–A-type emitters based on two fused boron ketoiminate (FBKI and aza-FBKI) acceptors and a 9,9-dimethylacridine (DMAC) donor. Through changing the carbon atom to a nitrogen atom in the fused boron ketoiminate units, a conventional fluorescent molecule (DMAC-FBKI) can be transformed into a highly efficient TADF molecule (DMAC-aza-FBKI) due to the dramatically reduced ΔEST (0.46–0.04 eV). DMAC-aza-FBKI exhibits a short delay lifetime of 2.2 μs and a large reverse intersystem crossing rate constant (kRISC ≈ 1.5 × 106 s−1), which is attributed to the rather close energy levels of its 3LE, 3CT and 1CT states based on theoretical calculations. Furthermore, compared with DMAC-FBKI (ΦPL ≈ 44%), DMAC-aza-FBKI has a higher ΦPL of 82% due to the existence of intramolecular hydrogen bonding. Consequently, the corresponding electroluminescent device achieves the highest external quantum efficiency (EQE) of 16.2% and an efficiency roll-off of only 9.3% at 1000 cd m−2, which are much better than those of the device based on the TADF-inactive DMAC-FBKI.

Developing purely organic room-temperature phosphorescence (RTP) materials with high phosphorescence efficiency in film states is important for their applications but remains a great challenge. Herein, a donor–acceptor type RTP molecule (SeX-CzPh) with 9H-selenoxanthen-9-one and 9-phenyl-9H-carbazole as the acceptor and donor units, respectively, is designed and synthesized. SeX-CzPh shows a high phosphorescence quantum yield of 44.3% in a doped polystyrene film at room temperature, which is 4-fold higher than that of its analogue TX-CzPh with 9H-thioxanthene-9-one as the acceptor unit. The heavy selenium atom plays a key role in achieving high phosphorescence quantum yields, owing to the effective ISC process through strong spin–orbit coupling. Moreover, the SeX-CzPh-based oxygen film sensor exhibits a wide detection range (0–2.1 × 105 ppm), a high KSV (1.27 × 10−4 ppm−1) and a low detection limit (4.9 ppm). This work demonstrates that 9H-selenoxanthen-9-one is a promising building block for the rational design of highly efficient purely organic RTP materials.

The most recently-proposed concept of capsule network consists of capsules, a structural group of neurons with activation, as building blocks, and dynamic routing between them as connections. Semantic information for final tasks can be extracted by stacking such capsule layers as construction of deep models. In this paper, we formulate the dynamic routing problem from the perspective of feature compression and reconstruction, proposing a novel routing algorithm which explicitly minimizes the reconstruction error with sparsity constraints. By alternately updating poses and routing weights with closed-form solutions, the method ensures a theoretical convergence of the dynamic routing. Benefitting from the strict convergence, we further prompt the efficiency of routing with the use of an adaptive number of iterations. Experimental results on different datasets prove that our proposed routing method outperforms existing routing methods on both performance as well as efficiency with obvious margin. Furthermore, we empirically show that the reconstruction based routing method can be embedded into backbone networks as a representative compression module. The integration of convolutional networks and capsule routing delivers both reduction of model parameters and improvement of performance to original backbones.

We present an overview of quantum-safe cryptography (QSC) with a focus on post-quantum cryptography (PQC) and information-theoretic security. From a cryptographic point of view, lattice and code-based schemes are among the most promising PQC solutions. Both approaches are based on the hardness of decoding problems of linear codes with different metrics. From an information-theoretic point of view, lattices and linear codes can be constructed to achieve certain secrecy quantities for wiretap channels as is intrinsically classical- and quantum-safe. Historically, coding theory and cryptography are intimately connected since Shannon’s pioneering studies but have somehow diverged later. QSC offers an opportunity to rebuild the synergy of the two areas, hopefully leading to further development beyond the NIST PQC standardization process. In this paper, we provide a survey of lattice and code designs that are believed to be quantum-safe in the area of cryptography or coding theory. The interplay and similarities between the two areas are discussed. We also conclude our understandings and prospects of future research after NIST PQC standardisation.

Entropy minimization has been widely used in unsupervised domain adaptation (UDA). However, existing works reveal that the use of entropy-minimization-only may lead to collapsed trivial solutions for UDA. In this article, we try to seek possible close-to-ideal UDA solutions by focusing on some intuitive properties of the ideal domain adaptation solution. In particular, we propose to introduce diversity maximization for further regulating entropy minimization. In order to achieve the possible minimum target risk for UDA, we show that diversity maximization should be elaborately balanced with entropy minimization, the degree of which can be finely controlled with the use of deep embedded validation in an unsupervised manner. The proposed minimal-entropy diversity maximization (MEDM) can be directly implemented by stochastic gradient descent without the use of adversarial learning. Empirical evidence demonstrates that MEDM outperforms the state-of-the-art methods on four popular domain adaptation datasets.

The thieno[3,2-b]thiophene (TT) unit is a classic building block to construct high-performance fused-ring electron acceptors (FREAs) for organic solar cells (OSCs). In this work, a star-shaped FREA BTTCTT-ICF based on a new C3h-symmetric fused-ring core BTTCTT was designed and synthesized by introducing the TT unit to extend the π-conjugation. Due to its strong electron-donating ability and efficient π-electron delocalization, BTTCTT-ICF exhibited a narrow optical bandgap of 1.54 eV and a high absorption extinction coefficient of 3.6 × 105 M−1 cm−1. The blend film of BTTCTT-ICF and the wide bandgap polymer PM6 produced a complementary absorption and a broad EQE spectral range covering 300 to 850 nm. When treated via thermal annealing, the PM6:BTTCTT-ICF blend film exhibited more ordered molecular packing and a suitable phase morphology, which improved its charge mobility and device efficiency. Consequently, the PM6:BTTCTT-ICF-based inverted device achieved the best power conversion efficiency (PCE) of 10.39% with a high short-circuit current density (Jsc) of 16.77 mA cm−2, open-circuit voltage (Voc) of 0.91 V and fill factor (FF) of 68.08%. Our results demonstrate that the extension of the star-shaped fused-ring core by TT units is a rational and effective strategy for achieving highly efficient star-shaped FREAs.

Halogen bonding, as a kind of intermolecular interaction, has rarely been used to tune solid-state emission properties of luminescent materials, especially fluorescent materials. Herein, three trans-enaminone (TE) derivatives (nonbrominated TE, monobrominated BrTE, and tribrominated Br3TE) with aggregation-induced emission property have been designed and synthesized. Two types of BrTE crystals (BrTE-B and BrTE-G) with different fluorescence properties were obtained. It was observed that their solid-state fluorescence has been enhanced by the formation of halogen bonding. In particular, the crystal BrTE-G containing Br…π interactions exhibits a fluorescence quantum yield (9.6%) nearly sevenfold higher than BrTE-B, the crystal without halogen bonding (1.4%), and fivefold higher than the nonbrominated TE derivative (2.1%). By careful inspection of the single-crystal data and theoretical calculations, the high fluorescence quantum yield of BrTE-G appears to be due to halogen-bonding interactions as well as multiple stronger intermolecular interactions which may restrain molecular motions, leading to the reduced nonradiative decay rate and the enhanced radiative decay rate. Additionally, increasing the number of bromine substituents may further promote the radiative decay rate, explaining therefore the higher fluorescence quantum yield (12.5%) of Br3TE.

Room-temperature phosphorescence of most purely organic compounds can hardly be observed in their solutions. Here, we report a tetraphenylmethane derivative with four aldehyde groups, which exhibits clear blue phosphorescence in its solution at room temperature. Furthermore, its crystal displays a nearly 7-fold higher phosphorescence quantum yield as compared to the solution.

Learning part-based features with multiple branches has been proven as an effective way to deliver high performance person re-identification. Existing works mostly exploit extra constraints on different branches to ensure the diversity of extracted features, which may lead to the increased complexity in network architecture and the difficulty for training. In this letter, we propose a quite simple multi-branch structure consisting of a global branch as well as a part branch in an asymmetrical way. We empirically demonstrate that such simple architecture can provide surprisingly high performance without imposing any extra constraint. On top of this, we further prompt the performance with a lightweight implementation of attention module. Extensive experimental results prove that the proposed method, termed Asymmetrical Network (AsNet), outperforms state-of-the-art methods with obvious margin on standard benchmark datasets such as Market1501, DukeMTMC, CUHK03. We believe that AsNet can serve as a strong baseline for related research and the source code is publicly available at https://github.com/www0wwwjs1/asnet.git .

Classical fused-ring electron acceptors (FREAs) with a linear acceptor–donor–acceptor (A–D–A) architecture continuously break records of power conversion efficiency (PCE) in nonfullerene organic so...

For WiFi indoor localization, collecting the data to construct the fingerprint dataset is a time-consuming and laborious job. In this paper, we propose to alleviate the time-intensive data-labeling by generating artificial fingerprint using a single AC-GAN. Compared to the existing multi-GAN approaches, we show that the use of a single AC-GAN could achieve the improved accuracy in localization, which is validated by extensive experiments. We also discuss its potential limit.

For image semantic segmentation, a fully convolutional network is usually employed as the encoder to abstract visual features of the input image. A meticulously designed decoder is used to decoding the final feature map of the backbone. The output resolution of backbones which are designed for image classification task is too low to match segmentation task. Most existing methods for obtaining the final high-resolution feature map can not fully utilize the information of different layers of the backbone. To adequately extract the information of a single layer, the multi-scale context information of different layers, and the global information of backbone, we present a new attention-augmented module named Dense-attention Context Module (DCM), which is used to connect the common backbones and the other decoding heads. The experiments show the promising results of our method on Cityscapes dataset.

Two novel red thermally activated delayed fluorescence (TADF) emitters [triazatruxene (TAT)–dibenzo[a,c]phenazine (DBPZ) and TAT–fluorine-substituted dibenzo[a,c]phenazine (FDBPZ)] were developed b...

The nature of matter and energy exchange of an ecological process defines the applicability of the thermodynamic functions for describing an ecosystem. A plant community is an open system consisting of living species as material components. Following the basic laws of thermodynamics, the enthalpy H stored in biomass form of a plant community will be related to its total equivalent biomass quantity CT, the weighted average standard chemical potential μ0, Gibbs free energy G, entropy S and temperature T by H = G + TS = CTμ0. Using h, f and s to denote H/(RT), G/(RT) and S/R (R denoting the gas constant), respectively, the conventional function can be transformed to h = f + s = CTμ0/(RT). The relation sm/CT = SIm = ln(N) derived from the maximal discrete entropy theorem shows that sm (the maximum s) and SIm (the maximum information entropy) will increase with increase in the total number of species N, suggesting that N has an upper limit Nm subject to regional species resource. As an upper limt of SI and s/CT, ln(N) is applied as a biodiversity index. As an upper limt of ln(N), ln(Nm) can thus be regarded as a biodiversity potential index as it takes into account the available number of species distributed in the surrounding areas of the plant community, showing the potential limit for further increase in its biodiversity. The difference between ln(Nm) and ln(N) dtermines the distribution of H as G and TS, indicating that the internal energy distribution of an acosystem is a function of its productivity and biodiversity. The potential trends of increasing N towards Nm and increasing s towards sm suggest that an ecosystem can possess natural trends towards increase in both its species richness and evenness.

This paper introduces a lightweight convolutional neural network, called FDDWNet, for real-time accurate semantic segmentation. In contrast to recent advances of lightweight networks that prefer to utilize shallow structure, FDDWNet makes an effort to design more deeper network architecture, while maintains faster inference speed and higher segmentation accuracy. Our network uses factorized dilated depth-wise separable convolutions (FDDWC) to learn feature representations from different scale receptive fields with fewer model parameters. Additionally, FDDWNet has multiple branches of skipped connections to gather context cues from intermediate convolution layers. The experiments show that FDDWNet only has 0.8M model size, while achieves 60 FPS running speed on a single RTX 2080Ti GPU with a 1024 × 512 input image. The comprehensive experiments demonstrate that our model achieves state-of-the-art results in terms of available speed and accuracy trade-off on CityScapes and CamVid datasets.

Two subphthalocyanine (SubPc)-based conjugated porous polymers, SubPc-P1 and SubPc-P2, have been designed and synthesized by an A3 + B2 and A4 + B2 type Sonogashira cross-coupling reaction, in which axial coupling is absent and present, respectively. These two polymers exhibit Brunauer–Emmett–Teller surface areas of 434 m2 g−1 and 106 m2 g−1, respectively. Combining their porosity with strong light harvesting capability, SubPc-P1 and SubPc-P2 can efficiently absorb long-wavelength photons even in the near-infrared region to generate singlet oxygen rapidly. In addition, compared with SubPc-P2, SubPc-P1 shows red-shifted light absorption and a larger specific surface area, which agrees well with the better singlet oxygen generation ability upon irradiation at 690 nm.

Two star-shaped electron acceptors (STIC and STFIC) based on subphthalocyanine (SubPc) as core and 2-(3-oxo-2,3-dihydroinden-1-ylidene)malononitrile (IC) or 2-(5,6-difluoro-3-oxo-2,3-dihydro-1H-inden-1-ylidene)malononitrile (2FIC) as end groups, have been designed and synthesized. Both two acceptors show unique photophysical properties different from those of traditional SubPc based non-fullerene acceptors (NFAs): first, two strong absorption bands in the region of 400–700 nm with high extinction coefficients are observed; second, their spectra in films exhibit full width at half maximum (FWHM) over 260 nm, more than two-fold larger than those of traditional SubPc based NFAs; third, their optical bandgaps are around 1.6 eV, among the narrowest bandgaps for SubPc-based NFAs. Compared with STFIC with 2FIC end groups, the higher-lying lowest unoccupied molecular orbital (LUMO) level of STIC with IC end groups leads to the higher values of open circuit voltage (VOC) and power conversion efficiency (PCE) in organic solar cells (OSCs). Using PBDB-T as donor, the PCEs of 4.69% and 3.80% are achieved by OSCs based on STIC and STFIC, respectively. The PCE of 4.69% is also among the highest PCE values for SubPc-based solution-processed BHJ OSCs.

In this work, two thermally activated delayed fluorescence (TADF) fluorophores (TAT-BP and TAT-2BP) based on triazatruxene (TAT) and benzophenone (BP) have been successfully prepared. While both compounds show similar green emission and typical AIE behaviors, TAT-2BP with two BP units exhibits a shorter delay fluorescence lifetime (0.54 μs) than TAT-BP with one BP unit (0.79 μs) in neat films. In solution-processable non-doped organic light-emitting diodes (OLEDs), TAT-2BP shows a better EL performance with a turn-on voltage of 2.5 V, a maximum current efficiency of 32.3 cd A−1, a maximum power efficiency of 33.0 lm W−1, and a maximum external quantum efficiency of 9.8%. Remarkably, very small efficiency roll-offs of 1.0% at 1000 cd m−2 and 2.0% at 2000 cd m−2 are realized for TAT-2BP based devices, which are also much lower than those of TAT-BP (10.9% at 1000 cd m−2 and 28.0% at 2000 cd m−2), indicating that the utilization of TAT-2BP with a shorter delay fluorescence lifetime can efficiently retard the efficiency decline.

Three novel water-soluble and pH neutral small molecules based on a triazatruxene (TAT) core containing sodium propanylsulfonate, sodium butanylsulfonate and sodium butanyl-2-sulfonate as the side chains, respectively, have been designed and used as hole injection materials in solution-processable organic light-emitting diodes. These molecules show a high transmittance of nearly 100% in the visible and near infrared region (400–1000 nm). Relative to PEDOT:PSS, the comparable HOMO levels and higher LUMO levels of TAT-based materials favor the efficient injection of holes to the emitting layer and the efficient blocking of electrons and excitons. Among the TAT-based materials, the device based on TAT-4C with the sodium butanylsulfonate side chains exhibits the best performance with a maximum EQE of 11.0%, a maximal current efficiency (LE) of 38.8 cd A−1 and a power efficiency (PE) of 39.2 lm W−1, which are also higher than those of the PEDOT:PSS-based device (an EQE of 10.6%, a LE of 37.6 cd A−1 and a PE of 35.1 lm W−1). In additon, all the devices based on the TAT-based molecules show better performance than PEDOT:PSS-based device at 10 000 cd m−2, owing to their wider bandgaps and higher LUMO levels than PEDOT:PSS, indicating that the utilization of these TAT-based molecules as hole injection materials retards the efficiency decline.

There is a huge carbon pool in the lakeside, which is sensitive to environmental changes and can very easily be transformed into a carbon source as land from the lake is reclaimed. In this paper, West Mauri Lake was employed as a case study to examine soil organic carbon (SOC) and its controlling factors along the lakeside. Four transects of land use (i.e., vegetation) types along the landward lakeside were identified as the fluctuation zone, the beach zone, the mesozoic farmland rewetting zone and the xerophytic farmland rewetting zone. With the increase in soil depth, SOC in the lakeside decreased significantly (p < 0.05). SOC had an obvious seasonal variation (p < 0.001), ranking in order: winter (December) > spring (February) > summer (May). Among the aforementioned transects, SOC density differed significantly (p < 0.05), showing a significant increasing trend. Pearson correlation indicated that most soil physiochemical factors showed a significant correlation with SOC (p < 0.01), except total chromium, total copper, total zinc and total phosphorus. The relationship between SOC density and total nitrogen (N) has an obvious “S” curve, and total N accounts for 81% of the variation of SOC, suggesting that total N is the main controlling factor of SOC in the lakeside. The significant difference in SOC along the different vegetation (land use) types implied that land use affects the SOC in the lakeside. The long-term accumulation of N fertilizer after the man-made reclamation and aquaculture obviously controls SOC in the lakeside of West Mauri Lake.

Visual saliency detection plays a significant role in the fields of computer vision. In this paper, we introduce a novel saliency detection method based on weighted linear multiple kernel learning (WLMKL) framework, which is able to adaptively combine different contrast measurements in a supervised manner. As most influential factor is contrast operation in bottom-up visual saliency, an average weighted corner-surround contrast (AWCSC) is first designed to measure local visual saliency. Combined with common-used center-surrounding contrast (CESC) and global contrast (GC), three types of contrast operations are fed into our WLMKL framework to produce the final saliency map. We show that the assigned weights for each contrast feature maps are always normalized in our WLMKL formulation. In addition, the proposed approach benefits from the advantages of the contribution of each individual contrast feature maps, yielding more robust and accurate saliency maps. We evaluated our method for two main visual saliency detection tasks: human fixed eye prediction and salient object detection. The extensive experimental results show the effectiveness of the proposed model, and demonstrate the integration is superior than individual subcomponent.

A plant community is an open thermodynamic system. In accordance with the first and second laws of thermodynamics, the total internal energy U of a plant community before reaching equilibrium will increase under natural conditions because of solar energy intake and nutrient uptake. As state functions of a thermodynamic system, enthalpy H, entropy S and Gibbs free energy G are related to U by G + TS = H = U + PV. Increase in U will lead to increases in H, S and G since given volume V and temperature T or pressure P, dG + TdS = dH = dU > 0. The experimental data collected from the plant communities restored on a manganese tailing site confirmed the potential trend of increase in H, S and G of the plant communities with increase in their biomass quantity and biodiversity. The traditional criterion “ΔG

Recently, exploring features from different layers in fully convolutional networks (FCNs) has gained substantial attention to capture context information for semantic segmentation. This paper presents a novel encoder-decoder architecture, called contextual ensemble network (CENet), for semantic segmentation, where the contextual cues are aggregated via densely usampling the convolutional features of deep layer to the shallow deconvolutional layers. The proposed CENet is trained in terms of end-to-end segmentation to match the resolution of input image, and allows us to fully explore contextual features through ensemble of dense deconvolutions. We evaluate our CENet on two widely-used semantic segmentation datasets: PASCAL VOC 2012 and CityScapes. The experimental results demonstrate our CENet achieves superior performance with respect to recent state-of-the-art results. Furthermore, we also evaluate CENet on MS COCO dataset and ISBI 2012 dataset for the task of instance segmentation and biological segmentation, respectively. The experimental results show that CENet obtains promising results on these two datasets.

Unsupervised domain adaptation (DA) enables intelligent models to learn transferable knowledge from a labeled source domain and adapt to a similar but unlabeled target domain. Studies showed that knowledge could be transferred from one source domain to another unknown target domain, called Universal DA (UDA). However, there is often more than one source domain in the real-world application to be exploited for DA. In this paper, we formally propose a more general domain adaptation setting for image classification, universal multi-source DA (UMDA), where the label sets of multiple source domains can be different, and the label set of the target domain is completely unknown. The main challenge in UMDA is to identify the common label set among each source and target domain and keep the model scalable as the number of source domains increases. In the face of this challenge, we propose a universal multi-source adaptation network (UMAN) to solve the DA problem without increasing the complexity of the model in various UMDA settings. In UMAN, the reliability of each known class belonging to the common label set is estimated via a novel pseudo-margin vector and its weighted form, which helps adversarial training better align the distributions of multiple source domains and target domain. Moreover, the theoretical guarantee for UMAN is also provided. Massive experimental results show that existing UDA and multi-source DA (MDA) methods cannot be directly deployed to UMDA, and the proposed UMAN achieves the state-of-the-art performance in various UMDA settings.

Two solution processable triazatruxene-based small molecules (TATC-BP and TATP-BP) with thermally activated delayed fluorescence (TADF), aggregation-induced emission (AIE), and mechanochromic luminescence (MCL) properties are synthesized and characterized. Compared with phenyl-substituted TATP-BP, TATC-BP with hexyl substituents showed a clearer MCL with a larger emission color change from 483 to 542 nm upon mechanical grinding. On the other hand, the solution processed nondoped OLED of TATP-BP showed a better EL performance with a maximal current efficiency of 18.9 cd A−1, a maximum external quantum efficiency (EQE) of 6.0%, and a remarkable small roll-off of 3.3% in EQE even at a luminance of 1000 cd m−2.

Two star-shaped electron acceptors (E-SubPc-PDI and S-SubPc-PDI) based on subphthalocyanine (SubPc) as the core and three perylene diimides (PDIs) as wing groups linked with and without acetylene bridges, respectively, have been synthesized. Compared with conventional SubPc-based acceptors with optical absorption full width at half maximum (FWHM) values of less than 100 nm and optical bandgaps over 2 eV, both E-SubPc-PDI and S-SubPc-PDI possess broader absorption spectra with FWHM values of up to 170 nm and smaller optical bandgaps below 1.9 eV. Due to the different linkages between SubPc and PDIs, they exhibit different molecular geometries, optical properties, energy levels and stacking behaviors. With a more twisted molecular structure, the single bond linked S-SubPc-PDI exhibits reduced intermolecular aggregation and a higher lowest unoccupied molecular orbital (LUMO) level compared to the acetylene bridge-linked E-SubPc-PDI. When blending with a donor polymer PBDB-T, the active layer of PBDB-T:S-SubPc-PDI shows more favorable phase separation and more balanced carrier transport. As a result, solution-processed organic solar cells (OSCs) based on S-SubPc-PDI exhibit a power conversion efficiency (PCE) up to 4.53%, which is much higher than that of E-SubPc-PDI-based devices (1.78%).