Final Performance Research Articles

Achieving efficient and stable deep-blue tandem organic light-emitting diodes with high color purity via novel charge generator layer

Efficient and stable deep blue OLEDs with high color purity are desirable for OLEDs based displays. However, the development of deep blue OLEDs is lagging far behind green and red OLEDs for lots of years due to their higher-energy exciton, which reduces the final device performance and increases the consumption of electrical energy. Here, we designed a novel charge generator layer (CGL): Liq/Al/MoO3/SimCP2 with improved charge injection capability than traditional CGL. Based on this CGL, we successfully fabricated tandem deep blue OLEDs using thermally actively delayed fluorescence materials SYBN as guest and triplet–triplet annihilation material NPADN as host, which exhibits high external quantum efficiency of 17.7 %, current efficiency of 13.3 cd/A, power efficiency of 7.1 lm/W and stable LT95 device lifetime over 1200 h at initial luminescence of 1000 cd/m2. Simultaneously, it’s convenient to adjust the micro-cavity effect through increasing Spiro-BFA-2 thickness to reduce the full width at half maximum (FWHM) of EL spectra from 26 nm to 21 nm, realizing small CIEy value of 0.061 at a mild emission wavelength of 461 nm while maintaining a low driving voltage of 8.6 V at 1000 cd/m2. We believe that this work is significant to promote the development of deep blue OLEDs.

Improve Transmission Fault Tolerance and Speed for Distributed Machine Learning

Abstract. As a matter of fact, with the advent of the big data era, traditional stand-alone machine learning methods are facing computational as well as storage bottlenecks. On this basis, distributed machine learning has become a popular research direction. Withs this in mind, this research explores the basic principles of distributed machine learning and its application on large-scale datasets. According to the analysis, it is shown that the use of distributed architecture can effectively shorten the model training time and improve the scalability of the system. By comparing different distributed algorithms, it is found that the architecture based on parameter servers has obvious advantages in dealing with heterogeneous data. In addition, the experimental results show that a reasonable data partitioning and parallel computing strategy can significantly improve the training efficiency and the final performance of the model at the same time. The significance of this study is that it provides a theoretical foundation and practical guidance for distributed machine learning, which helps to promote its wide application in industry.

Supporting Peer Feedback at Scale

Schneider et al. (2017) conducted a systematic review of 38 meta-analyses investigating 105 variables linked to student performance. They reported peer assessment is the most influential factor in explaining students’ achievement. The benefits are even more significant when combined with self-assessment as students' meta-cognitive capacity is exercised as they calibrate their level of understanding and confidence to that of their peers (Power & Tanner, 2023). Assessing a peer digitally has been shown to be more effective than in-person settings (Li et al., 2020). Winstone & Boud (2022) recommend improving students’ learning by focusing on providing and receiving high-quality feedback rather than on grades. Despite the potential of technology to help students assess each other, there is sparse research in commerce studies, let alone on large-scale subjects, where technology use is essential to leverage students’ learning and encourage interaction, which otherwise would be limited. Such a study can shed light on students' learning and engagement benefits. Technology can also aid in creating a structured learning environment, with students being required to complete multiple tasks throughout the semester and engage with the platform repetitively, reinforcing improved curriculum retention (Schwerter et al., 2022). Our project involved implementing five tasks (a practice task and four assessed tasks) in a large-cohort first-year finance subject at a leading Australian university. The tasks contributed 15% to each student’s final mark and utilised the Feedback Fruits platform (integrated with Canvas LMS). For each task, students scanned their handwritten answers to two questions, uploaded them into the platform as a PDF file, completed self-assessment and reviewed a randomly assigned anonymous peer using a rubric designed to mimic the rubric used to mark final exam questions. We configured the system so that students had at least one week to upload their answers and another week to complete the self-assessment and peer review. Statistical analysis of the Feedback Fruits analytics files, which include completion rates and performance, involved regression analysis and propensity score matching and indicates a significant positive relationship between platform engagement (measured by time spent on the platform, provided comments length and task performance) and final exam performance, even after controlling for a student’s finance aptitude. The study also involved an end-of-semester survey and focus groups to capture students’ perceptions. Students indicated the positive learning benefits they gained from reviewing their peers, being reviewed, and reflecting on their performance. Furthermore, we found a high correlation between the number of comments and the average number of words per comment students left themselves and those left for their peers. The innovation of our research is in the scale, continuous use of the platform throughout the semester with multiple access points per task and better exam preparedness by simulating exam conditions utilising rubrics and handwriting requirements. This poster describes the project’s implementation process map, including task setup, timelines, and the support provided, which is the technical part of the bigger project. We review fundamental mechanisms to improve participation and the quality of the reviews. In addition, the poster presents the challenges faced and offers practical steps to overcome them.

Preparation and characterization of nanocomposites based on natural rubber/chlorobutyl rubber/graphene nanoparticle: Effect of feeding sequences, mixer type and nanoparticle concentration

AbstractBlends of natural rubber (NR)/chlorobutyl rubber (CIIR) (50/50), along with their nanocomposites containing varying amounts of graphene nanoparticles (GNPs) with and without silane, were prepared using different feeding sequences and mixers. The results indicated that when GNP was first mixed with NR before adding CIIR, the highest GNP content was found in NR; the oxygen permeability and diffusion coefficient decreased significantly by 37% and 55%, respectively. Conversely, when graphene was initially added to CIIR, it was distributed in both rubbers and at the interface, enhancing phase interaction and achieving a finer morphology. This sample exhibited the greatest improvement in mechanical properties due to specific localization of GNP and increased crosslink density. Using an internal mixer instead of a two‐roll mill improved graphene dispersion, reduced curing time, and enhanced mechanical properties. Although the presence of silane reduced graphene dispersion, it improved mechanical and barrier properties due to increased crosslink density. Oxygen permeability was reduced by up to 36% at 3 phr of GNP content with silane. This research proves that the mixing order and the type of mixing equipment are crucial in determining the final performance of rubber composites, as they significantly influence the dispersion and localization of fillers.Highlights NR/CIIR/GNP nanocomposites were prepared by different methods and evaluated. Adding NR/GNP to CIIR led to more presence of GNP in NR and lower permeability. By adding CIIR/GNP to NR, GNP were placed more at interface, modulus improved. Mixing in an internal mixer improved GNP dispersion compared to a two‐roll mill. Silane improved mechanical and barrier properties of NR/CIIR/GNP nanocomposite.

Development and Validation of a Predictive Model for Maternal Cardiovascular Morbidity Events in Patients With Hypertensive Disorders of Pregnancy.

Hypertensive disorders of pregnancy (HDP) are a major contributor to maternal morbidity, mortality, and accelerated cardiovascular (CV) disease. Comorbid conditions are likely important predictors of CV risk in pregnant people. Currently, there is no way to predict which people with HDP are at risk of acute CV complications. We developed and validated a predictive model for all CV events and for heart failure, renal failure, and cerebrovascular events specifically after HDP. Models were created using the Premier Healthcare Database. The inclusion criteria for the model dataset were delivery with an HDP with discharge from October 1, 2015 to December 31, 2020. Machine learning methods were used to derive predictive models of CV events occurring during delivery hospitalization (Index Model) or during readmission (Readmission Model) using a training set (60%) to estimate model parameters, a validation set (20%) to tune model hyperparameters and select a final model, and a test set (20%) to evaluate final model performance. The total model cohort consisted of 553,658 deliveries with an HDP. A CV event occurred in 6501 (1.2%) of the delivery hospitalizations. Multilabel neural networks were selected for the Index Model and Readmission Model due to favorable performance compared to alternatives. This approach is designed for prediction of multiple events that share risk factors and may cooccur. The Index Model predicted all CV events with area under the receiver operating curve (AUROC) 0.878 and average precision (AP) 0.239 (cerebrovascular events: AUROC 0.941, heart failure: AUROC 0.898, and renal failure: AUROC 0.885). With a positivity threshold set to achieve ≥90% sensitivity, model specificity was 65.0%, 83.5%, 68.6%, and 65.6% for predicting all CV events, cerebrovascular events, heart failure, and renal failure, respectively. CV events within 1 year of delivery occurred in 3018 (0.6%) individuals. The Readmission Model predicted all CV events with AUROC 0.717 and AP 0.022 (renal failure: AUROC 0.748, heart failure: AUROC 0.734, and cerebrovascular events AUROC 0.698). Feature importance analysis indicated that the presence of chronic renal disease, cardiac disease, pulmonary hypertension, and preeclampsia with severe features had the greatest effect on the prediction of CV events. Among individuals with HDP, our multilabel neural network model predicted CV events at delivery admission with good classification and events within 1 year of delivery with fair classification.

Comparing attendance versus exam performance for topics within first year physics courses

Abstract This paper revisits the relationship between lecture attendance and final exam performance of students attending first year Advanced Physics classes at The University of Newcastle. The results compare attendance to non-attendance for topics within the Advanced Physics courses and indicate that, on average, students attending lectures always score higher than students not attending with variations on the size of this effect depending on topic such that the gap is generally larger for topics not covered in secondary school. This correlation may indicate the importance for students to attend classes covering new material.

Quasi-visualizable detection of deep sub-wavelength defects in patterned wafers by breaking the optical form birefringence

Abstract In integrated circuit (IC) manufacturing, fast, nondestructive, and precise detection of defects in patterned wafers, realized by bright-field microscopy, is one of the critical factors for ensuring the final performance and yields of chips. With the critical dimensions of IC nanostructures continuing to shrink, directly imaging or classifying deep-subwavelength defects by bright-field microscopy is challenging due to the well-known diffraction barrier, the weak scattering effect, and the faint correlation between the scattering cross-section and the defect morphology. Herein, we propose an optical far-field inspection method based on the form-birefringence scattering imaging of the defective nanostructure, which can identify and classify various defects without requiring optical super-resolution. The technique is built upon the principle of breaking the optical form birefringence of the original periodic nanostructures by the defect perturbation under the anisotropic illumination modes, such as the orthogonally polarized plane waves, then combined with the high-order difference of far-field images. We validated the feasibility and effectiveness of the proposed method in detecting deep subwavelength defects through rigid vector imaging modeling and optical detection experiments of various defective nanostructures based on polarization microscopy. On this basis, an intelligent classification algorithm for typical patterned defects based on a dual-channel AlexNet neural network has been proposed, stabilizing the classification accuracy of λ/16-sized defects with highly similar features at more than 90%. The strong classification capability of the two-channel network on typical patterned defects can be attributed to the high-order difference image and its transverse gradient being used as the network’s input, which highlights the polarization modulation difference between different patterned defects more significantly than conventional bright-field microscopy results. This work will provide a new but easy-to-operate method for detecting and classifying deep-subwavelength defects in patterned wafers or photomasks, which thus endows current online inspection equipment with more missions in advanced IC manufacturing.

Layout optimization and Performance of Large Array of imaging atmospheric Cherenkov Telescope (LACT)

Abstract Large Array of imaging atmospheric Cherenkov Telescope (LACT) is an array of 32 Cherenkov telescopes with 6-meter diameter mirrors to be constructed at the LHAASO site. In this work, we present a study on the layout optimization and performance analysis of LACT. We investigate two observation modes: large zenith angle observations for ultra-high energy events and small zenith angle observations for lower energy thresholds. For large zenith angles (60°), simulations show that an 8-telescope subarray can achieve an effective area of $3 ~\rm km^2$ and excellent angular resolution. For small zenith angles, we optimize the layout of 4-telescope cells and the full 32-telescope array. The threshold of the full array is about $200~\rm GeV$, which is particularly crucial for studying transient phenomena, including gamma-ray bursts (GRBs) and active galactic nuclei (AGNs). This study provides important guidance for the final LACT layout design and performance estimates under different observational conditions, demonstrating LACT's potential for deep observations of ultra-high energy \gray sources and morphological studies of PeVatrons, as well as time-domain \gray astronomy.

On-line inspection of lattice structures and metamaterials via in-situ imaging in Additive Manufacturing

As advanced production capabilities are moving towards novel types of geometries as well as higher customization demands, a new and more efficient approach for process and part qualification is becoming an urgent need in industry. The layerwise nature of additive manufacturing (AM) potentially allows anticipating qualification tasks in-line and in-process, aiming at reducing the time and costs devoted to post-process inspections, enabling at the same time an early detection of defects since their onset stage. Such opportunity is particularly attractive in the presence of highly complex shapes like lattice structures or metamaterials, which have been increasingly investigated for industrial adoption in various sectors, aiming to achieve enhanced mechanical properties and innovative functionalities. This paper presents a novel methodology to inspect the geometry of lattice structures while the part is being built. The method is specifically designed to tackle the natural variability affecting layerwise images gathered in laser powder bed fusion. To this aim, it combines the segmentation of in-situ powder bed images of solidified layers with a data modelling approach to synthesize the 3-D shape of each unit cell into a 1-D profile representation. Such low-dimensional representation is suitable to quickly detect undesired distortions that may have a detrimental impact on final quality and performance. By using post-process X-ray computed tomography as ground truth reference, this study shows the effectiveness of the proposed approach for in-line inspection, opening a novel and cost-efficient way to address complex shape qualification for lattice structures in AM.

The Input of Nanoclays to the Synergistic Flammability Reduction in Flexible Foamed Polyurethane/Ground Tire Rubber Composites.

Currently, postulated trends and law regulations tend to direct polymer technology toward sustainability and environmentally friendly solutions. These approaches are expressed by keeping materials in a loop aimed at the circular economy and by reducing the environmental burdens related to the production and use of polymers and polymer-based materials. The application of recycled or waste-based materials often deals efficiently with the first issue but at the expense of the final products' performance, which requires various additives, often synthetic and petroleum-based, with limited sustainability. Therefore, a significant portion of research is often required to address the drawbacks induced by the application of secondary raw materials. Herein, the presented study aimed to investigate the fire performance of polymer composites containing highly flammable matrix polyurethane (PU) foam and filler ground tire rubber (GTR) originating from car tire recycling. Due to the nature of both phases and potential applications in the construction and building or automotive sectors, the flammability of these composites should be reduced. Nevertheless, this issue has hardly been analyzed in literature and dominantly in our previous works. Herein, the presented work provided the next step and investigated the input of nanoclays to the synergistic flammability reduction in flexible, foamed PU/GTR composites. Hybrid compositions of organophosphorus FRs with expandable graphite (EG) in varying proportions and with the addition of surface-modified nanoclays were examined. Changes in the parameters obtained during cone calorimeter tests were determined, discussed, and evaluated with the fire performance index and flame retardancy index, two parameters whose goal is to quantify the overall fire performance of polymer-based materials.

Voxel-wise segmentation for porosity investigation of additive manufactured parts with 3D unsupervised and (deeply) supervised neural networks

AbstractAdditive Manufacturing (AM) has emerged as a manufacturing process that allows the direct production of samples from digital models. To ensure that quality standards are met in all samples of a batch, X-ray computed tomography (X-CT) is often used in combination with automated anomaly detection. For the latter, deep learning (DL) anomaly detection techniques are increasingly used, as they can be trained to be robust to the material being analysed and resilient to poor image quality. Unfortunately, most recent and popular DL models have been developed for 2D image processing, thereby disregarding valuable volumetric information. Additionally, there is a notable absence of comparisons between supervised and unsupervised models for voxel-wise pore segmentation tasks. This study revisits recent supervised (UNet, UNet++, UNet 3+, MSS-UNet, ACC-UNet) and unsupervised (VAE, ceVAE, gmVAE, vqVAE, RV-VAE) DL models for porosity analysis of AM samples from X-CT images and extends them to accept 3D input data with a 3D-patch approach for lower computational requirements, improved efficiency and generalisability. The supervised models were trained using the Focal Tversky loss to address class imbalance that arises from the low porosity in the training datasets. The output of the unsupervised models was post-processed to reduce misclassifications caused by their inability to adequately represent the object surface. The findings were cross-validated in a 5-fold fashion and include: a performance benchmark of the DL models, an evaluation of the post-processing algorithm, an evaluation of the effect of training supervised models with the output of unsupervised models. In a final performance benchmark on a test set with poor image quality, the best performing supervised model was UNet++ with an average precision of 0.751 ± 0.030, while the best unsupervised model was the post-processed ceVAE with 0.830 ± 0.003. Notably, the ceVAE model, with its post-processing technique, exhibited superior capabilities, endorsing unsupervised learning as the preferred approach for the voxel-wise pore segmentation task.

Research on the microstructure and properties of metastable β type Ti-8V4Mo3Cr3Zr3Al alloy with high strength and toughness

In this paper, a new type of Ti-8V-4Mo-3Cr-3Zr-3Al metastable β-type titanium alloy is designed based on alloy design parameters such as valence electron concentration (VEC), Bo, and Md, and combines them with the empirical criterion of molybdenum equivalent fractionation. Optimize the microstructure of the alloy through processes such as cold rolling, annealing, and aging treatment to obtain good mechanical properties. The microstructure of cold rolling and post rolling heat treatment was observed and analyzed using optical microscopy (OM), scanning electron microscopy (SEM), X-ray diffraction (XRD), and transmission electron microscopy (TEM), and the tensile properties of the alloy were tested. The characteristics of the alloy in terms of microstructure and properties were summarized and analyzed. The results show that the cold formability of the alloy after solid solution treatment is good, with a cold rolling reduction of over 85 %, and a large number of deformation twins generated during the cold rolling process. The yield strength after annealing and recrystallization is up to 1160 MPa, and elongation is 18.9 %. The final performance of the aged alloy is 1510 MPa for yield strength and 5 % for elongation.

An artificial intelligence-based landmark model for early detection of aortic stenosis in Cardiac MRI

Abstract Background The utilisation of ratiometric intensity measures from cardiac magnetic resonance imaging (CMR) offers a promising biomarker for the diagnosis of aortic stenosis (AS)[1]. The Ao:LV ratio, an approximation of AS severity, is determined by comparing the relative blood signal intensity in the aorta and left ventricle (LV) within three-chamber (3Ch) cine sequences[1]. In patients with AS, the blood signal intensity of aortic blood above the valve is diminished compared to that within the LV in steady-state free precession (SSFP) 3Ch CMR cine. Although the Ao:LV ratio serves as a reliable indicator of AS severity, manual computation of this ratio may encounter difficulties due to visual obstructions caused by small LV cavities or prominent papillary muscles. Purpose This study aims to leverage artificial intelligence (AI) to automate the Ao:LV ratio analysis from 3Ch SSFP cine images and introduce the Ao:LA (aorta to left atrium) ratio, thus enhancing the diagnostic accuracy for AS severity. Methods Our methodology extends the ratiometric analysis to include both Ao:LV and Ao:LA ratios in three main steps: first, developing an AI-based algorithm for automated detection and tracking of cardiac landmarks; second, using these landmarks to automatically compute regions of interest (ROIs) in the cardiac chambers; and third, training and validating classification models with 5-fold cross-validation to select the most effective for AS severity classification. The final model's performance is assessed on a holdout test set. Data for model training and evaluation were sourced from CMR scans across three hospitals and two scanner types. Results We analysed 220 patients of which 78 patients had no AS and 122 had AS (mild AS, n = 29; moderate AS; n = 35, severe AS; n = 78). Our model yielded high efficacy in classifying AS, particularly in differentiating any grade of AS from a normal classification, which is clinically relevant. The Ao:LA ratio demonstrated a stronger correlation with AS severity class than Ao:LV, suggesting a more reliable biomarker. The proposed model demonstrated robust performance in AS classification, achieving an Area Under the Curve (AUC) of 0.845 in the automated binary classification of AS at any grade. The model exhibited a precision of 0.889, recall of 0.865, and an F1 score of 0.877 on a holdout set. Conclusion By the integration of AI in automating the analysis of CMR images through a novel ratiometric approach that includes Ao:LV and Ao:LA ratios, our model significantly improves the accuracy of early AS detection. This advancement enhances imaging strategies for AS evaluation, promoting more efficient and effective use of cardiac MRI sequences as a diagnostic tool for aortic stenosis. It has the potential to be implemented in real-time scanning protocols as a clinical decision support system.AI-inferred cardiac landmarks.ROC curve delineating AS prediction.

Prediction of atrial fibrillation recurrence after ablation: the impact of machine learning and recurrence during the blanking period

Abstract Introduction Pulmonary vein isolation is superior to antiarrhythmic drugs for rhythm control in patients with atrial fibrillation (AF). Nevertheless, recurrences within the first year are still significant. Several clinical risk scores were developed to predict AF recurrence; however, despite good results in development cohorts, the results in external validation are usually poor, limiting the general applicability of these scores. Purpose We aimed to develop machine learning (ML) models to predict AF recurrences within the first year after catheter ablation and compare their performance with conventional risk scores. Methods We used a retrospective dataset of a tertiary hospital center, including all consecutive patients submitted to the first AF ablation between 2017 and 2021. The dataset included 76 features: patient characteristics, medical history, baseline echocardiography, calcium score, procedure variables, and early recurrence (ER) during the 90-day blanking period. The outcome was defined as the occurrence of an electrocardiographically documented late recurrence (LR) of atrial tachycardia/AF > 30s between 3-12 months. Three different supervised ML models (penalized logistic regression, random forests, and XGBoost) were developed on the training cohort, and hyperparameters were tuned using 10-fold cross-validation. A testing dataset was held out to estimate the final performance (25%). The following risk scores were calculated: APPLE, CAAP-AF, and BASE-AF2 (the last includes ER). Areas under the curve (AUC) of receiver operating characteristic curves were compared using DeLong’s test. Results 679 patients were included: 62.7% males, median age of 59±16 years, and 78.2% with paroxysmal AF. The median time from diagnosis to ablation was 2±4 years. 74.2% underwent radiofrequency ablation and the remaining cryoablation. ER occurred in 12.5%, and most of these patients also experienced LR (68.7 vs 19.6% in those without ER, p<0.001). LR was observed in 25.6%. The XGBoost model showed the best performance with an AUC 0.774, 95% confidence interval (CI) 0.688–0.844) outperforming existing scores (picture 1): APPLE score AUC 0.607, 95% CI 0.562–0.653, p-value<0.001; CAAP-AF score AUC 0.622, 95% CI 0.576–0.668, p-value=0.002; BASE-AF2 AUC 0.628, 95% CI 0.581–0.675, p-value= 0.003. Variable importance analysis showed a significant drop in the performance of the models when ER was not considered, indicating its high importance in predicting LR (picture 2). Conclusions Recurrence during the blanking period was the most important predictor of LR in our population. The ML model was superior to conventional risk scores. ML models might be an essential tool to improve the prediction of outcomes and clinical decision-making for optimal follow-up.ROC curvesFeature Importance

Automatic noise detection for ambulatory electrocardiogram in presence of ventricular arrhythmias through a machine learning approach

Noise detection in ambulatory electrocardiography is investigated as a machine learning binary classification problem on a set of twelve noise indices. Ten of these noise indices are replicated from relevant scientific literature. Two novel noise indices are also introduced: the electrocardiogram derivative pattern similarity index (edp) and the number of inversions of the signal’s slope over a minimum amplitude threshold (inv). Noise indices were computed on ex-novo labeled data, which included ventricular arrhythmias such as ventricular tachycardia, flutter, and fibrillation. Signal quality labels were assigned on windows of 2 seconds, from which a set of labels on 10-second windows was also created. Six classification models were trained on 2 s and 10 s window data with a multi-stage optimization procedure. A feature selection stage was performed for each model, probing the usefulness of different noise indices, including noise and ventricular arrhythmias. Hyperparameter selection and model assessment were conducted through nested cross-validation. Final test performance metrics reached values as high as Matthew’s correlation coefficient of 0.854 and balanced accuracy of 0.923. Particular attention was placed on the correct quality classification of records with ventricular arrhythmias, and test specificity on records with ventricular arrhythmias was as high as 0.976. This study provided new insights into the role of different noise indices in a challenging context with ventricular arrhythmias.

Modifying the buried interface by a sulfamate enable efficient perovskite solar cells with high stability

The buried interface between the pivotal perovskite layer and the electron transfer layer (ETL) greatly affects the photoelectric property of perovskite layer and the final performance of a perovskite solar cell (PSC). Effectively modifying the buried interface is feasible to improve the photoelectric conversion efficiency (PCE) and stability of PSCs. Herein, we employ a sulfamate sodium 4-aminoazobenzene-4′-sulfonate (SABS) with zwitterion to modify the SnO2 film surface to improve the performance of PSCs. The post-treatment makes the surface of SnO2 film smoother and also passivate the anion and cation vacancies of SnO2, which are benefit to the growth of perovskite crystals and strengthen the interface contact between SnO2 layer and perovskite layer. Furthermore, SABS molecules can passivate defects of perovskite layer at the buried interface. By using this post-treating method, the PCE of the final PSC is increased from 21.76 % to 23.86 %, and the storage and operational stability are also significantly improved. This promotion the buried interface via post-treating the SnO2 film with a zwitterion passivator provides a convenient strategy to effectively improve the performance and stability of PSCs.

Nanocellulose‐polypropylene composites with novel antimicrobial complex salt

AbstractPolypropylene composites with different contents of zinc‐ammonium salts (3, 5 and 7 wt%) and nanocellulose (1 wt%) after enzymatic bioconversion were characterized by structural, dispersion‐morphological, thermal, antimicrobial and mechanical analysis. It was shown that the use of a newly synthesized salt significantly affected the formation of the supermolecular structure of the polymer matrix and increased the nucleation activity, thermostability and flexibility of the composite materials. Moreover, the addition of the inorganic filler allowed to obtain composites with very good antimicrobial properties against Staphylococcus aureus, Escherichia coli and Candida albicans. An interesting relationship between the degree of filler dispersion in the polymer matrix and the formation of polymorphic varieties was elucidated in the study, which contributes to the improvement of final performance characteristics of composite materials. It is noteworthy that a hybrid filler in the form of nanometric cellulose and a biocidal additive containing zinc‐ammonium complex salt was used for the first time. Research has shown that such polymer composites can find application during the manufacture of smart packaging materials with enhanced barrier properties against oxygen and water vapor as well as improved biocidal characteristics, which will notably extend food storage time. At the same time, the presented process of obtaining the innovative composite materials is an excellent example of sustainable technologies for the design of antimicrobial treatments, while guaranteeing the possibility of further processing of these composite materials through material recycling.Highlights Zinc‐ammonium complex with antimicrobial properties was obtained The strength properties and thermal stability of the composites were improved Degree of filler dispersion in polymer affects formation of polymorphic varieties The composites can be applied in the production of smart packaging materials

Don’t put all the eggs in one basket: examining adaptive selling behavior in salespeople’s performance

AbstractIn a B2B selling context, salespeople are encouraged to perform adaptive selling strategies to attend to diversified customer needs and achieve sales performance (i.e., market share) and relational performance (i.e., trust, loyalty, and satisfaction) by performing adaptive selling behavior or showing the ability to recognize customers’ emotions. However, based on the resource allocation theory, the current research proposes that the two aforementioned factors seem to interfere with each other to affect the final performances. Results show that from Study 1, adaptive selling behavior, which is consequential of salespeople’s interpersonal mentalizing skills, is positively associated with relational performance, and the relationship is moderated by emotional intelligence. From Study 2, perceived service climate, a consequence of perceived transformational leadership is positively associated with sales performance. There was a significant interaction between adaptive selling behavior and service climate on sales performance, indicating that salespeople who do not perceive that firms emphasize services, they have sufficient resources to apply adaptive selling strategies. Conversely, if they were encouraged to focus on providing service, this might affect their adaptive selling on sales performance. Based on the results, we provide insights for B2B salespeople and managers to focus on one salient goal to achieve the best outcome.

Surface treatment processed electron transport layers for efficient Sb2S3 solar cells

Solution-processed Sb2S3 thin films and solar cell devices have recently gained popularity due to their easy procedure and outstanding final device performance. Nevertheless, although the substrate has a significant influence on thefollowing functional films, the effect of the substrate in the Sb-based solar cells appears to have not been adequately examined. In this study, we performed an interesting thioacetamide (TA)-assisted surface treatment strategy on the electron transport layer (ETL) to achieve high-quality development of Sb2S3 thin films and based solar devices. The surface treatment of ETLs resulted in a uniform surface, Zn(OH)2 impurity reduction, and excellent wettability. The above enhanced the growth of Sb2S3 thin films with features like extremely large crystal sizes (∼8 um), more homogeneous and dense film morphology, preferred crystal orientation, reduced oxygen content, and fewer defects, which benefits carrier transport. Finally, a device structure constructed of FTO/TiO2/Zn(O,S)/Sb2S3/Spiro/Au was created, resulting in an almost 30 % increase in efficiency, from 4.05 % to 5.21 %. The proposed approach not only gives new insights into generating higher-quality Sb2S3 thin films and devices, but it also presents new concepts and methods for future high-quality sulfide thin film preparation.

Bernard Shaw and the Charles Macdona Partnership: Part II

ABSTRACT This article is the second half of a two-part article (the first of which appeared in SHAW: The Journal of Shaw Studies, vol. 43, no. 2) on the mostly overlooked professional partnership between Bernard Shaw and producer Charles Macdona, which claimed over 7,000 performances of Shaw’s plays from 1912 to 1939. This part explores Macdona’s expanding ambitions for his partnership with Shaw, which begins with fulfilling 1924 plans. The 1925–1940 years cast the Shaw Repertory tours further afield, launched an expansive Saint Joan tour, mounted West End productions, staged a premier, and presented its final performances as the World War II commenced. When Macdona died in 1946, obituary notices and remembrance articles appeared throughout the counties, colonies, and countries where his tours delivered Shaw’s plays. Expanding the Shavian gospel was one of Macdona’s distinct achievements.