Correction for ‘Phosphorescent [3 + 2 + 1] coordinated Ir(iii) cyano complexes for achieving efficient phosphors and their application in OLED devices’ by Yuan Wu et al., Chem. Sci., 2021, 12, 10165–10178, https://doi.org/10.1039/D1SC01426A.

Correction for 'Phosphorescent [3 + 2 + 1] coordinated Ir(iii) cyano complexes for achieving efficient phosphors and their application in OLED devices' by Yuan Wu et al., Chem. Sci., 2021, 12, 10165-10178, https://doi.org/10.1039/D1SC01426A.

Highly efficient blue and warm white tandem OLEDs were designed and realized by doping blue thermally activated delayed fluorescence (TADF) emitter and red phosphorescent emitter into mixed hosts system together. Based on the optimization of charge generation unit (CGU), blue tandem device with external quantum efficiency of 30.93 % and color coordinates of (0.165, 0.365) was firstly realized. To achieve efficient and high-quality white OLEDs (WOLEDs), red phosphorescent emitter PQ2Ir (dpm) was doped into TCTA:26DCzPPy (1:1) to construct another electroluminescent (EL) unit near the anode. By optimizing the doping concentration of PQ2Ir (dpm), maximizing excitons utilization probability and controlling energy transfer processes, the optimal tandem WOLED with turn-on voltage, maximum brightness, current efficiency (CE), power efficiency (PE) and external quantum efficiency (EQE) of 5.2 V, 29,595 cd m- 2, 65.19 cd A-1, 38.94 lm W- 1 and 40.46 %, respectively, was demonstrated. Excitingly, this device can still maintain an EQE up to 25.59 % at the certain brightness of 1000 cd m- 2, which is important for lighting application. The correlated color temperature (CCT) of this device tunes from 4401 K at 1000 cd m- 2 to 2642 K at 20,000 cd m- 2, thereby accommodating various scene requirements.

Recent developments in the field of thermally activated delayed fluorescence (TADF) emitter have led to significantly improved performances of hybrid solution processing prepared white organic light-emitting diodes (s-WOLEDs). Importantly, wide coverage of electroluminescent spectra and balanced transfer of carriers are conclusive factors to obtain high quality lighting sources. This paper demonstrated high quality s-WOLEDs grounded on blue TADF and red phosphorescent emitters. In optimal WOLED, Fo center dot rster energy transfer (ET) distance and ET rate were calculated to be 1.81 nm and 1.29 x 106 s-1, respectively. Also, exciplex host were employed to decrease operation voltage and modulate carriers' distribution. The fabricated optimal solutionprocessed warm white electroluminescence device exhibited high color rendering index of 80 and maximum luminescent efficiencies up to 29.22 cd A-1, 28.68 lm W-1 and 13.5%. Furthermore, the Commission Internationale de I'Eclairage coordinates of this device are (0.39, 0.41) at the current density of 10 mA cm-2, while the turn-on voltage of this device is 3.0 V.

We devised single-EML SP-WOLEDs with luminescent gold(iii) emitters featuring high EQEmax of 12.72%, CIE coordinates of (0.40, 0.40), and CRI of 93, which is among the best values for the single-EML SP-WOLEDs with CRI > 90 reported in the literature.

Induced polarization (IP) is a widely used geophysical exploration technique. Continuous random noise is one of the most prevalent interferences that can seriously contaminate the IP signal and distort the apparent electrical characteristics. We develop a noise separation algorithm based on deep learning to overcome this issue. The standard IP signals are first produced by combining the Cole-Cole model and Fourier series decomposition, and then the mathematical simulation is used to generate various types of random noise interferences, which are subsequently added to the IP signals. Then, a denoising autoencoder deep neural network structure is built and trained by using noisy signals as input samples and pure signals as output samples. The resulting optimum network is capable of automatically reconstructing a clean IP signal from the noisy input. This network is tested using synthetic data sets. The trained neural network can perform the noise reduction of thousands of survey points in a matter of sec-onds and reduce signal distortion from approximately 25% to less than 5%. Deep learning-based denoising provides superior com-putation speed and precision compared with the wavelet denois-ing and smoothing filtering approach. The data for high-quality signals do not vary considerably before and after noise reduction. The noise interferences are successfully suppressed for low -qual-ity signals. Based on the findings, the denoising autoencoder deep neural network has a promising future for suppressing random noise interferences, which can aid in improving the quality of IP data with high efficiency and precision.

In the audio magnetotellurics (AMT) sounding data processing, the absence of sferic signals in some time ranges typically results in a lack of energy in the AMT dead band, which may cause unreliable resistivity estimate. We propose a deep convolutional neural network (CNN) to automatically recognize sferic signals from redundantly recorded data in a long time range and use them to compensate for the resistivity estimation. We train the CNN by using field time series data with different signal to noise rations that were acquired from different regions in mainland China. To solve the potential overfitting problem due to the limited number of sferic labels, we propose a training strategy that randomly generates training samples (with random data augmentations) while optimizing the CNN model parameters. We stop the training process and data generation until the training loss converges. In addition, we use a weighted binary cross-entropy loss function to solve the sample imbalance problem to better optimize the network, use multiple reasonable metrics to evaluate network performance, and carry out ablation experiments to optimally choose the model hyperparameters. Extensive field data applications show that our trained CNN can robustly recognize sferic signals from noisy time series for subsequent impedance estimation. The subsequent processing results show that our method can significantly improve S/N and effectively solve the problem of lack of energy in dead band. Compared to the traditional processing method without sferic compensation, our method can generate a smoother and more reasonable apparent resistivity-phase curves and depolarized phase tensor, correct the estimation error of sudden drop of high-frequency apparent resistivity and abnormal behavior of phase reversal, and finally better restore the real shallow subsurface resistivity structure.

In audio magnetotellurics (AMT) sounding data processing, the absence of sferic signals in some time ranges results in a lack of energy in the AMT dead-band, causing unreliable resistivity estimations. To address this issue, we propose a deep convolutional neural network (CNN) to automatically recognize sferic signals from redundantly recorded data over a long-time range and use these sferic signals to accurately estimate resistivity. The CNN was trained using field time series data with different signal-to-noise ratios (S/Ns) acquired from different regions of mainland China. To solve the potential overfitting due to the limited number of sferic labels, we propose a training strategy that randomly generates training samples with random data augmentations while optimizing the CNN model parameters. The training process and data generation were stopped when the training loss converges. In addition, we use a weighted binary cross-entropy loss function to solve the sample imbalance problem to optimize the network better, use multiple reasonable metrics to evaluate the network performance, and perform ablation experiments to optimize the model hyperparameters. Extensive field data applications show that our trained CNN can robustly recognize sferic signals from noisy time series for subsequent impedance estimation. The results show that our method can significantly improve the S/Ns and effectively solve the lack of energy in the dead-band. Compared with the traditional processing method, our method can generate smoother and more reasonable apparent resistivity-phase curves and depolarized phase tensors, correct the estimation error of the sudden drop in high-frequency apparent resistivity and abnormal behavior of phase reversal, and better estimate the real shallow resistivity structure.

Controlled-source audio-frequency magnetotellurics (CSAMT) has been seriously affected by strong electromagnetic interferences including large-scale drift, durative outbreak interference, and impulsive outliers. To improve the efficiency of noise reduction, a deep learning strategy was proposed to identify the type of noise interference, so as to select the appropriate noise reduction method. First, a CSAMT time series simulation algorithm was developed based on current decomposition and one-dimensional (1D) forward modeling. Three kinds of noise interferences were also generated by simulation and randomly added to the pure signals. A total of 30000 groups of simulated noisy electromagnetic signals were generated. Together with 210 sets of practically measured data, these samples were used to train a long-short-term memory network (LSTM) noise classifier. Then, three targeted de-noising algorithms were adopted to separate the three interferences in the CSAMT time series according to the identifications. The test results by simulated data showed that the identification accuracy of LSTM for noise interferences can reach more than 95%. Finally, the noise identification and suppression methods were applied to a practical CSAMT dataset and the effect was further verified.

Clarifying the effects of the atomization gases on the mechanical properties and their interactions with scanning strategies is of vital importance in additively manufactured materials. In the present work, two batches of 304L specimens, i.e., N2-specimens and Ar-specimens, were produced by laser powder bed fusion using 304L stainless steel powders atomized by argon (Ar-powder) or nitrogen (N2-powder), respectively. The Ar-specimens have similar microstructures with finer grains, which do not change with the scanning strategies, while the N2- specimens have large-grained microstructures with textures varying with scanning strategies. The tensile test shows that the Ar-specimens have better consistency of mechanical properties, higher elongation, and higher strain hardening rates than that of the N2-specimens. While the tensile strength of the N2-specimens varies with the scanning strategies, in which the specimen with 67 degrees laser scanning rotation has the highest tensile strength (694.80 MPa). Microstructural observation shows that the higher strain hardening rates of the Ar-specimens are caused by the simultaneous occurrence of deformation twinning and strain-induced martensite transformation, while the higher tensile strength of 67 degrees rotation N2-specimen results from the texture-controlled Schmid factor (an average value of 0.45-0.47). This work may give guidance for the production of metal powders and for quickly tailoring the mechanical properties of the additively manufactured stainless steels.

White organic light-emitting diodes (WOLEDs) incorporating a blend of blue, green and red phosphorescent small molecular materials are presented in this article. 4,4′,4″-Tris(carbazol-9-yl)triphenylamine (TcTa) and 9-(4-tert-Butylphenyl)-3,6-bis(triphenylsilyl)-9H-carbazole (CzSi) with different transmission characteristics were selected as hosts for different emitting layers aim to promote holes transport, which will reinforce carriers’ balance and broaden carrier composite. On account of adaptive energy levels of the utilized dopants and hosts, secured phosphorescent WOLED displayed high efficiencies, low operating voltage and slow efficiency roll-off. In addition, distribution of carriers’ recombination zone and spectral of change were studied in detail to further understand the light-emitting mechanisms of obtained WOLEDs. Finally, by majorizing the dosage concentration of (fbi)2Ir(acac) (bis(2-(9,9-diethyl-9H-fluoren-2-yl)-1-phenyl-1H-benzoimidazol-N,C3)iridium(acetylacetonate)) and the architectures of WOLEDs, the optimal device exhibited the maximum efficiencies of 44.92 cd A−1, 42.85 lm W−1, 16.8%, respectively, turn on voltage of 2.6 V and Commission International de l’Eclairage coordinates of (0.337, 0.458) at the brightness level of 3000 cd m−2.

Broad-spectrum white organic light-emitting diodes (WOLEDs) based on all-fluorescent materials with excellent color stability were realized by precisely optimizing the doping concentrations of guests and the thickness of each functional layer. High-efficiency blue fluorescent emissive material 9,10-bis[4-(6-methylbenzothiazol-2-yl) phenyl]anthracene (DBzA) was selected as blue emitter and doped into first light emitting layer (EML), while red-emitting dopant 4-(dicyanomethylene)-2-tert-butyl-6-(1,1,7,7-tetramethyljulolidn-4-yl-vinyl)- 4H pyran (DCJTB) was doped into green-emitting host material tris(8-hydroxyquinoline) aluminum (Alq(3)) as the second EML. Thin hole limit layer (HLL) was inserted to balance carriers' distribution within the two EMLs and to modulate the luminous intensity ratio of different emissions. Finally, the optimal WOLED exhibited the maximum current efficiency of 9.34 cd/A, power efficiency of 10.06 lm/W, brightness up to 29,364 cd/m(2) and turn-on voltage of only 2.7 V. In addition, this device displayed stable Commission International de I'Eclairage coordinates from (0.339, 0.382) to (0.324, 0.354) with increasing current density. The highest color rendering index and corresponding correlated color temperature reach 85 and 5492 K, respectively, and the T50 lifetime reaches 7912 h. The achievement of these results fully exhibits the effectiveness of the HLL in improving spectral stability and operation lifetime.

Accurate recognition of system health states is the key to ensure the safe operation of the system. In view of the shortcomings of the existing methods, a new method of manufacturing system health states assessment and prediction based on brittleness is proposed. Firstly, according to the real-time effective performance parameters, the brittle risk entropy model of equipment is constructed, and the brittleness of corresponding equipment on each station is calculated; Secondly, based on the structural characteristics of manufacturing system, the calculation model of system brittleness is constructed by analysing from equipment to system step by step, and the mapping relationship between system brittleness and health states is established to complete the assessment of system health states; Thirdly, based on the historical data samples, the quintic polynomial regression model of system brittleness and time is constructed to predict the future health states of system and the time nodes that need to be maintained. Finally, an assembly manufacturing system is taken as an example to verify the correctness and effectiveness of the proposed method.

Knee osteoarthritis (KOA) occurs mostly in the elderly and often causes physical disability and limitation. Early detection and intervention are particularly important in reducing the deterioration of knee disease. Currently, early detection of KOA is mainly evaluated by a Kellgren-Lawrence (KL) Classification with five class. KL is an ordered classification problem. The cross-entropy loss (CEL) and fixed penalty loss (FPL) are commonly used in KL system. Both CEL and FPL do not take into account the ease of classification between samples, which results in that the obtained model has deficiency. In this paper, a novel focal ordinal loss (FOL) is proposed by combining FPL and focal loss (FL) for KL. In the training algorithm based on FOL, the difficult or easy example is first identified according to the predicted probability toward to the true label at each epoch. The sample with a high predicted probability is considered as an easy sample. On the contrary, it is considered as a difficult sample. The weights for easy samples in the loss are then adjusted down in the next epoch, which results in that the training process can focus on difficult samples. The performance of FOL is validated on an X-ray image dataset from the Osteoarthritis Initiative (OAI) with several classical CNN models, such as VGG, Resnet, Densenet and Googlenet. The experimental results show that FOL achieves significant improvements in many performance measures, especially in mean square error (MSE). In addition, the experimental results on the augmented dataset and the Resnet with the convolutional block attention module (CBAM) also show similar improvements from FOL. It indicates that FOL is effective and superior to CEL and FPL in the numerous models and two data types (original and augmented) for KL grading.

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The utilization of triplet metal‐metal‐to‐ligand‐charge‐transfer ( 3 MMLCT) emissions of Pt(II) complexes having a large radiative decay rate is a promising strategy to develop efficient red and deep‐red emitters for practical organic light‐emitting diodes (OLEDs). The panel of robust luminescent dinuclear Pt(II) emitters described here features pyridine‐/pyrazine‐fused N‐heterocyclic carbene‐based cyclometalating ligands and ditopic bis‐ µ 2 ‐formamidinate bridging ligands. These complexes show intramolecular Pt–Pt distances of 2.85–2.87 Å, are thermally stable up to 446 °C, and display strong red and deep‐red 3 MMLCT emission (604–689 nm) with emission quantum yields close to unity. Under laboratory conditions, red and deep‐red OLEDs with these complexes show high external quantum efficiencies (up to 21.3%) and prolonged operational lifetimes (LT 97 up to 2446 h) at an initial luminance of 1000 cd m −2 , highlighting the practicality of these dinuclear Pt(II) emitters in organic optoelectronics application.

Efficient solution-processed white organic light-emitting diodes with FIrpic, Ir(mppy) 3 and (MDQ) 2 Ir(acac) as the blue, green and red emitters, respectively, were realized by employing TcTa and CzSi as hosts.

To address the problems of unreasonable maintenance measures and high maintenance cost of manufacturing equipment, a method combining the brittleness theory with condition-based maintenance is proposed to formulate an optimal maintenance strategy for equipment. First, the model of brittle risk entropy is constructed based on real-time performance parameters to measure the brittleness of equipment. Then, the mapping relationship between the degree of brittleness and health states is established to classify the health states of equipment from the perspective of brittleness. Second, a maintenance strategy optimisation model is established based on the health states of the equipment. Furthermore, the optimal maintenance strategy corresponding to each health state of equipment is obtained to minimise the total maintenance cost. Finally, the effectiveness of the method is verified by a case study of tightening equipment. The results show that the method could effectively realise the maintenance optimisation of the tightening equipment and provide a theoretical basis for a reasonable selection of maintenance strategy.

为实现对发动机缸盖装配系统健康状态的准确评估,针对发动机缸盖装配系统评估过程中存在的随机性和模糊性问题,提出一种基于脆性度和云模型的健康状态评估方法.基于发动机缸盖装配系统自身特点及指标体系构建原则,建立了表征系统健康状态的三级评估指标体系;利用脆性度划分发动机缸盖装配系统健康状态等级,通过运用云模型理论实现评估指标值和评语集之间的不确定映射,基于云重心评判法完成对系统健康状态的评估,有效解决了评估过程中定性概念和定量数值间的不确定性转换问题;结合脆性理论和多态理论构建系统脆性度计算模型,基于系统实时脆性度值对上述系统健康状态评估结果进行验证分析;以某实际发动机缸盖装配系统为例,验证了所提方法的正确性和有效性.