Municipal solid waste management challenges in developing regions: A comprehensive review and future perspectives for Asia and Africa.

The rapid urbanization witnessed in developing countries in Asia and Africa has led to a substantial increase in municipal solid waste (MSW) generation. However, the corresponding disposal strategies, along with constraints in land resources and finances, compounded by unorganized public behaviour, have resulted in ineffective policy implementation and monitoring. This lack of systematic and targeted orientation, combined with blind mapping, has led to inefficient development in many areas. This review examines the key challenges of MSW management in developing countries in Asia and Africa from 2013 to 2023, drawing insights from 170 academic papers. Rather than solely focusing on recycling, the study proposes waste sorting at the source, optimization of landfill practices, thermal treatment measures, and strategies to capitalize on the value of waste as more pertinent solutions aligned with local realities. Barriers to optimizing management systems arise from socio-economic factors, infrastructural limitations, and cultural considerations. The review emphasizes the importance of integrating the study area into the circular economy framework, with a focus on enhancing citizen participation in solid waste reduction and promoting recycling initiatives, along with seeking economic assistance from international organizations.

The Toxicity of Mercury and Its Chemical Compounds: Molecular Mechanisms and Environmental and Human Health Implications: A Comprehensive Review.

Mercury is a type of hazardous and toxic pollutant that can result in detrimental effects on the environment and human health. This review is aimed at discussing the state-of-the-art progress on the recent developments on the toxicity of mercury and its chemical compounds. More than 210 recent works of literature are covered in this review. It first delineates the types (covering elemental mercury, inorganic mercury compounds, organic mercury compounds), structures, and sources of mercury. It then discusses the pharmacokinetic profile of mercury, molecular mechanisms of mercury toxicity, and clinical manifestation of acute and chronic mercury toxicity to public health. It also elucidates the mercury toxicity to the environment and human health in detail, covering ecotoxicity, neurotoxicity diseases, neurological diseases, genotoxicity and gene regulation, immunogenicity, pregnancy and reproductive system damage, cancer promotion, cardiotoxicity, pulmonary diseases, and renal disease. In order to mitigate the adverse effects of mercury, strategies to overcome mercury toxicity are recommended. Finally, some future perspectives are provided in order to advance this field of research in the future.

A review of remediation technologies for uranium-contaminated water.

Uranium is a naturally existing radioactive element present in the Earth's crust. It exhibits lithophilic characteristics, indicating its tendency to be located near the surface of the Earth and tightly bound to oxygen. It is ecotoxic, hence the need for its removal from the aqueous environment. This paper focuses on the variety of water treatment processes for the removal of uranium from water and this includes physical (membrane separation, adsorption and electrocoagulation), chemical (ion exchange, photocatalysis and persulfate reduction), and biological (bio-reduction and biosorption) approaches. It was observed that membrane filtration and ion exchange are the most popular and promising processes for this application. Membrane processes have high throughput but with the challenge of high power requirements and fouling. Besides high pH sensitivity, ion exchange does not have any major challenges related to its application. Several other unique observations were derived from this review. Chitosan/Chlorella pyrenoidosa composite adsorbent bearing phosphate ligand, hydroxyapatite aerogel and MXene/graphene oxide composite has shown super-adsorbent performance (>1000 mg/g uptake capacity) for uranium. Ultrafiltration (UF) membranes, reverse osmosis (RO) membranes and electrocoagulation have been observed not to go below 97% uranium removal/conversion efficiency for most cases reported in the literature. Heat persulfate reduction has been explored quite recently and shown to achieve as high as 86% uranium reduction efficiency. We anticipate that future studies would explore hybrid processes (which are any combinations of multiple conventional techniques) to solve various aspects of the process design and performance challenges.

Digitalization and innovation in green ports: A review of current issues, contributions and the way forward in promoting sustainable ports and maritime logistics.

As a fundamental transportation mode, maritime logistics has become an indispensable component on a global scale. However, there are multiple drawbacks associated with ports operating in traditional ways, such as higher cost, lower efficiency and generating more environmental pollution. Digital technologies have been researched and implemented gradually in green ports, especially in data collection and real-time monitoring, and these advances help to promote higher work efficiency and reduce detrimental environmental impacts. It was found that green ports (e.g. ports of Raffina, Los Angeles, and Long Beach) generally perform better in energy conservation and pollutant emission reduction. However, considering the variability in the level of digitalization, there are challenges in achieving effective communications between individual ports. Therefore, to optimize and update green port practices, a systematic review is necessary to comprehensively analyze the beneficial contributions of green ports. This review adopted bibliometric analysis to examine the shipping framework focusing on green ports digitalization and innovation. After that, with regards to the bibliometric results, five aspects were analyzed, including environment, performance, policy, technology, and management. Besides, intelligent life-cycle management was systematically discussed to improve green ports and maritime logistics performance and sustainability in three aspects, namely waste discharge, shipping management system and green ports management. The findings revealed that green ports and maritime logistics require digital cooperation, transformation, and management to achieve sustainable development goals, including route selection and control of ships' numbers, weather prediction, and navigational effluent monitoring, albeit with some obstacles.

Bamboo-Modulated Helical Carbon Nanotubes for Rechargeable Zn-Air Battery.

The high-performance and sustainable electrocatalysts toward oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) are essential for rechargeable Zn-air batteries (ZABs). In this paper, a natural all-components bamboo is provided as the carbon source, and melamine and cobalt chloride are provided as the nitrogen and cobalt sources, respectively. As a result, the unique helical carbon nanotubes (HCNTs) encapsulated cobalt nanoparticles are prepared, which are acted as ORR/OER electrocatalysts to improve ZABs performance. The resultant HCNTs contribute to high ORR/OER activities via exposing more Co─N sites, providing excellent electron conductivity, and facilitating mass transfer of the reactant. The HCNTs assembled rechargeable liquid ZABs showed a maximum output power density of 226 mW cm-2 and a low voltage gap of 0.85 V for 330 h cycles. The flexible all-solid-state ZABs achieved the maximum power density with 59.4 mW cm-2 and charge-discharge cycles over 25 h. The density functional theory (DFT) calculations reveal that the increase of Co─N at HCNTs effectively regulates the electronic structure of Co, optimizing the binding affinity of oxygen intermediates and resulting in the low ORR/OER overpotentials. This work paves the way for transforming renewable bamboo biomass into versatile electrocatalysts, which boosts the development of next-generation energy storage and conversion devices.

Exploitation of lignocellulosic-based biomass biorefinery: A critical review of renewable bioresource, sustainability and economic views.

Urbanization has driven the demand for fossil fuels, however, the overly exploited resource has caused severe damage on environmental pollution. Biorefining using abundant lignocellulosic biomass is an emerging strategy to replace traditional fossil fuels. Value-added lignin biomass reduces the waste pollution in the environment and provides a green path of conversion to obtain renewable resources. The technology is designed to produce biofuels, biomaterials and value-added products from lignocellulosic biomass. In the biorefinery process, the pretreatment step is required to reduce the recalcitrant structure of lignocellulose biomass and improve the enzymatic digestion. There is still a gap in the full and deep understanding of the biorefinery process including the pretreatment process, thus it is necessary to provide optimized and adapted biorefinery solutions to cope with the conversion process in different biorefineries to further provide efficiency in industrial applications. Current research progress on value-added applications of lignocellulosic biomass still stagnates at the biofuel phase, and there is a lack of comprehensive discussion of emerging potential applications. This review article explores the advantages, disadvantages and properties of pretreatment methods including physical, chemical, physico-chemical and biological pretreatment methods. Value-added bioproducts produced from lignocellulosic biomass were comprehensively evaluated in terms of encompassing biochemical products , cosmetics, pharmaceuticals, potent functional materials from cellulose and lignin, waste management alternatives, multifunctional carbon materials and eco-friendly products. This review article critically identifies research-related to sustainability of lignocellulosic biomass to promote the development of green chemistry and to facilitate the refinement of high-value, environmentally-friendly materials. In addition, to align commercialized practice of lignocellulosic biomass application towards the 21st century, this paper provides a comprehensive analysis of lignocellulosic biomass biorefining and the utilization of biorefinery green technologies is further analyzed as being considered sustainable, including having potential benefits in terms of environmental, economic and social impacts. This facilitates sustainability options for biorefinery processes by providing policy makers with intuitive evaluation and guidance.

Investigation of the Nonionic Acidizing Retarder AAO for Reservoir Stimulation.

In the process of matrix acidizing, reducing the reaction rate between hydrochloric acid and carbonate rock to increase oil and gas production has become one of the biggest challenges in reservoir stimulation. An adsorption film formed on rocks can effectively postpone the contact between the hydrogen ion and rock, which is of great significance in decreasing the rate of an acid-rock reaction. In this study, nonionic acidizing retarder AAO was synthesized by acrylamide, allyl poly(ethylene glycol), and octadecyl methacrylate. The structure of AAO was characterized by Fourier transform infrared (FT-IR) spectrometry and 1H nuclear magnetic resonance (1H NMR). The reaction of AAO retard acid and 20% hydrochloric acid with CaCO3 was studied at 50 °C, and the amount of CO2 generated at different times was recorded. The etching time of 0.8% AAO retard acid to CaCO3 could be up to 120 min, whereas 20% hydrochloric acid (without AAO) ended at 45 min, which showed that AAO had the potential to defer the acid-rock reaction. The adsorption behavior of AAO on CaCO3 matched the pseudo-second-order kinetic model well. Meanwhile, the addition of urea greatly reduced the adsorption amount of AAO on CaCO3, which showed that the hydrogen bond was the driving force for the adsorption process. Additionally, the results of X-ray photoelectron spectroscopy (XPS) showed that the N element from acrylamide appeared on the surface of CaCO3 after adsorption. Scanning electron microscopy (SEM) demonstrated that a smooth and dense thin film existed on the surface of CaCO3 treated with AAO retard acid. The change in the vibration peak of C=O from 1720 to 1650 cm-1 indicated that the ester groups in AAO had been hydrolyzed, which was beneficial to film desorption and the reduction of reservoir damage. Therefore, this paper could help with research on carbonate acidizing for reservoir stimulation.

Strategies to save energy in the context of the energy crisis: a review.

New technologies, systems, societal organization and policies for energy saving are urgently needed in the context of accelerated climate change, the Ukraine conflict and the past coronavirus disease 2019 pandemic. For instance, concerns about market and policy responses that could lead to new lock-ins, such as investing in liquefied natural gas infrastructure and using all available fossil fuels to compensate for Russian gas supply cuts, may hinder decarbonization efforts. Here we review energy-saving solutions with a focus on the actual energy crisis, green alternatives to fossil fuel heating, energy saving in buildings and transportation, artificial intelligence for sustainable energy, and implications for the environment and society. Green alternatives include biomass boilers and stoves, hybrid heat pumps, geothermal heating, solar thermal systems, solar photovoltaics systems into electric boilers, compressed natural gas and hydrogen. We also detail case studies in Germany which is planning a 100% renewable energy switch by 2050 and developing the storage of compressed air in China, with emphasis on technical and economic aspects. The global energy consumption in 2020 was 30.01% for the industry, 26.18% for transport, and 22.08% for residential sectors. 10-40% of energy consumption can be reduced using renewable energy sources, passive design strategies, smart grid analytics, energy-efficient building systems, and intelligent energy monitoring. Electric vehicles offer the highest cost-per-kilometer reduction of 75% and the lowest energy loss of 33%, yet battery-related issues, cost, and weight are challenging. 5-30% of energy can be saved using automated and networked vehicles. Artificial intelligence shows a huge potential in energy saving by improving weather forecasting and machine maintenance and enabling connectivity across homes, workplaces, and transportation. For instance, 18.97-42.60% of energy consumption can be reduced in buildings through deep neural networking. In the electricity sector, artificial intelligence can automate power generation, distribution, and transmission operations, balance the grid without human intervention, enable lightning-speed trading and arbitrage decisions at scale, and eliminate the need for manual adjustments by end-users.

Artificial intelligence for waste management in smart cities: a review.

The rising amount of waste generated worldwide is inducing issues of pollution, waste management, and recycling, calling for new strategies to improve the waste ecosystem, such as the use of artificial intelligence. Here, we review the application of artificial intelligence in waste-to-energy, smart bins, waste-sorting robots, waste generation models, waste monitoring and tracking, plastic pyrolysis, distinguishing fossil and modern materials, logistics, disposal, illegal dumping, resource recovery, smart cities, process efficiency, cost savings, and improving public health. Using artificial intelligence in waste logistics can reduce transportation distance by up to 36.8%, cost savings by up to 13.35%, and time savings by up to 28.22%. Artificial intelligence allows for identifying and sorting waste with an accuracy ranging from 72.8 to 99.95%. Artificial intelligence combined with chemical analysis improves waste pyrolysis, carbon emission estimation, and energy conversion. We also explain how efficiency can be increased and costs can be reduced by artificial intelligence in waste management systems for smart cities.

Eggshell biometrics for individual egg identification based on convolutional neural networks.

Individual egg identification technology has potential applications in breeding, product tracking/tracing, and anti-counterfeit. This study developed a novel method for individual egg identification based on eggshell images. A convolutional neural network-based model, named Eggshell Biometric Identification (EBI) model, was proposed and evaluated. The main workflow included eggshell biometric feature extraction, egg information registration, and egg identification. The image dataset of individual eggshell was collected from the blunt-end region of 770 chicken eggs using an image acquisition platform. The ResNeXt network was then trained as a texture feature extraction module to obtain sufficient eggshell texture features. The EBI model was applied to a test set of 1,540 images. The testing results showed that when an appropriate Euclidean distance threshold for classification was set (17.18), the correct recognition rate and the equal error rate reached 99.96% and 0.02%. This new method provides an efficient and accurate solution for individual chicken egg identification, and can be extended to eggs of other poultry species for product tracking/tracing and anti-counterfeit.

A reliable and low-cost deep learning model integrating convolutional neural network and transformer structure for fine-grained classification of chicken Eimeria species.

Chicken coccidiosis is a disease caused by Eimeria spp. and costs the broiler industry more than 14 billion dollars per year globally. Different chicken Eimeria species vary significantly in pathogenicity and virulence, so the classification of different chicken Eimeria species is of great significance for the epidemiological survey and related prevention and control. The microscopic morphological examination for their classification was widely used in clinical applications, but it is a time-consuming task and needs expertise. To increase the classification efficiency and accuracy, a novel model integrating transformer and convolutional neural network (CNN), named Residual-Transformer-Fine-Grained (ResTFG), was proposed and evaluated for fine-grained classification of microscopic images of seven chicken Eimeria species. The results showed that ResTFG achieved the best performance with high accuracy and low cost compared with traditional models. Specifically, the parameters, inference speed and overall accuracy of ResTFG are 1.95M, 256 FPS and 96.9%, respectively, which are 10.9 times lighter, 1.5 times faster and 2.7% higher in accuracy than the benchmark model. In addition, ResTFG showed better performance on the classification of the more virulent species. The results of ablation experiments showed that CNN or Transformer alone had model accuracies of only 89.8% and 87.0%, which proved that the improved performance of ResTFG was benefit from the complementary effect of CNN's local feature extraction and transformer's global receptive field. This study invented a reliable, low-cost, and promising deep learning model for the automatic fine-grain classification of chicken Eimeria species, which could potentially be embedded in microscopic devices to improve the work efficiency of researchers and extended to other parasite ova, and applied to other agricultural tasks as a backbone.

In vivo prediction of abdominal fat and breast muscle in broiler chicken using live body measurements based on machine learning.

The purpose of this study was to predict the carcass characteristics of broilers using support vector regression (SVR) and artificial neural network (ANN) model methods. Data were obtained from 176 yellow feather broilers aged 100-day-old (90 males and 86 females). The input variables were live body measurements, including external measurements and B-ultrasound measurements. The predictors of the model were the weight of abdominal fat and breast muscle in male and female broilers, respectively. After descriptive statistics and correlation analysis, the datasets were randomly divided into train set and test set according to the ratio of 7:3 to establish the model. The results of this study demonstrated that it is feasible to use machine learning methods to predict carcass characteristics of broilers based on live body measurements. Compared with the ANN method, the SVR method achieved better prediction results, for predicting breast muscle (male: R2 = 0.950; female: R2 = 0.955) and abdominal fat (male: R2 = 0.802; female: R2 = 0.944) in the test set. Consequently, the SVR method can be considered to predict breast muscle and abdominal fat of broiler chickens, except for abdominal fat in male broilers. However, further revaluation of the SVR method is suggested.

Recent advances in the utilization of immobilized laccase for the degradation of phenolic compounds in aqueous solutions: A review.

Phenolic compounds such as phenol, bisphenol A, 2,4-dichlorophenol, 2,4-dinitrophenol, 4-chlorophenol and 4-nitrophenol are well known to be highly detrimental to both human and living beings. Thus, it is of critical importance that suitable remediation technologies are developed to effectively remove phenolic compounds from aqueous solutions. Biodegradation utilizing enzymatic technologies is a promising biotechnological solution to sustainably address the pollution in the aquatic environment as caused by phenolic compounds under a defined environmentally optimized strategy and thus should be investigated in great detail. This review aims to present the latest developments in the employment of immobilized laccase for the degradation of phenolic compounds in water. The review first succinctly delineates the fundamentals of biological enzyme degradation along with a critical discussion on the myriad types of laccase immobilization techniques, which include physical adsorption, ionic adsorption, covalent binding, entrapment, and self-immobilization. Then, this review presents the major properties of immobilized laccase, namely pH stability, thermal stability, reusability, and storage stability, as well as the degradation efficiencies and associated kinetic parameters. In addition, the optimization of the immobilized enzyme, specifically on laccase immobilization methods and multi-enzyme system are critically discussed. Finally, pertinent future perspectives are elucidated in order to significantly advance the developments of this research field to a higher level.

Humoral and Cellular Immune Responses of COVID-19 vaccines against SARS-Cov-2 Omicron variant: a systemic review.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has undergone multiple mutations since its emergence, and its latest variant, Omicron (B.1.1.529), is the most contagious variant of concern (VOC) which poses a major and imminent threat to public health. Since firstly reported by World Health Organization (WHO) in November 2021, Omicron variant has been spreading rapidly and has become the dominant variant in many countries worldwide. Omicron is the most mutated variant so far, containing 60 mutations in its genome, including 37 mutations in the S-protein. Since all current COVID-19 vaccines in use were developed based on ancestral SARS-CoV-2 strains, whether they are protective against Omicron is a critical question which has been the center of study currently. In this article, we systemically reviewed the studies regarding the effectiveness of 2- or 3-dose vaccines delivered in either homologous or heterologous manner. The humoral and cellular immune responses elicited by various vaccine regimens to protect against Omicron variant are discussed. Current understanding of the molecular basis underlying immune escape of Omicron was also analyzed. These studies indicate that two doses of vaccination are insufficient to elicit neutralizing antibody responses against Omicron variant. Nevertheless, Omicron-specific humoral immune responses can be enhanced by booster dose of almost all type vaccines in certain degree, and heterologous vaccination strategy may represent a better choice than homogenous regimens. Intriguingly, results of studies indicate that all current vaccines are still able to elicit robust T cell response against Omicron. Future focus should be the development of Omicron variant vaccine, which may induce potent humoral as well as cellular immune responses simultaneously against all known variants of the SARS-CoV-2 virus.

Impact of Primary Tumor Resection on Survival of Patients with Metastatic Gallbladder Carcinoma: A Population-Based, Propensity­Matched Study.

BACKGROUND Gallbladder carcinoma (GBC) is relatively rare but highly aggressive and it has poor prognosis, especially for metastatic GBC. We aimed to determine the prognostic significance of primary tumor resection on patients with metastatic GBC. MATERIAL AND METHODS The records of patients with GBC with distant metastasis from 2010 to 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Kaplan-Meier methods were used to compare overall survival (OS) and carcinoma-specific survival (CSS) between patients receiving primary tumor resection and those without surgery. Cox regression analysis was conducted to identity independent factors significantly associated with survival. In addition, a propensity score-matched analysis was performed to adjust for the heterogeneity between the groups. RESULTS Of the 1337 patients included, 496 patients underwent primary tumor resection and 841 patients did not. Multivariate Cox regression analysis showed that OS (hazard ratio [HR]: 0.56, 95% confidence interval [CI]: 0.48-0.66, P<0.001) and CSS (HR: 0.57, 95% CI: 0.48-0.66, P<0.001) were significantly improved in patients receiving surgical resection of the primary tumor lesion in the unmatched cohort. Additionally, in the matched cohort, univariate Cox regression analysis similarly indicated that performing surgery at the primary site was associated with better OS (HR: 0.62, 95% CI: 0.50-0.77, P<0.001) and CSS (HR: 0.61, 95% CI: 0.50-0.762, P<0.001). CONCLUSIONS This study indicated that primary tumor resection might prolong survival in patients with metastatic GBC.

A 3D-2D Multibranch Feature Fusion and Dense Attention Network for Hyperspectral Image Classification.

In recent years, hyperspectral image classification (HSI) has attracted considerable attention. Various methods based on convolution neural networks have achieved outstanding classification results. However, most of them exited the defects of underutilization of spectral-spatial features, redundant information, and convergence difficulty. To address these problems, a novel 3D-2D multibranch feature fusion and dense attention network are proposed for HSI classification. Specifically, the 3D multibranch feature fusion module integrates multiple receptive fields in spatial and spectral dimensions to obtain shallow features. Then, a 2D densely connected attention module consists of densely connected layers and spatial-channel attention block. The former is used to alleviate the gradient vanishing and enhance the feature reuse during the training process. The latter emphasizes meaningful features and suppresses the interfering information along the two principal dimensions: channel and spatial axes. The experimental results on four benchmark hyperspectral images datasets demonstrate that the model can effectively improve the classification performance with great robustness.

Hybrid Dilated Convolution with Multi-Scale Residual Fusion Network for Hyperspectral Image Classification.

The convolutional neural network (CNN) has been proven to have better performance in hyperspectral image (HSI) classification than traditional methods. Traditional CNN on hyperspectral image classification is used to pay more attention to spectral features and ignore spatial information. In this paper, a new HSI model called local and hybrid dilated convolution fusion network (LDFN) was proposed, which fuses the local information of details and rich spatial features by expanding the perception field. The details of our local and hybrid dilated convolution fusion network methods are as follows. First, many operations are selected, such as standard convolution, average pooling, dropout and batch normalization. Then, fusion operations of local and hybrid dilated convolution are included to extract rich spatial-spectral information. Last, different convolution layers are gathered into residual fusion networks and finally input into the softmax layer to classify. Three widely hyperspectral datasets (i.e., Salinas, Pavia University and Indian Pines) have been used in the experiments, which show that LDFN outperforms state-of-art classifiers.

Architecture of the herpesvirus genome-packaging complex and implications for DNA translocation.

Genome packaging is a fundamental process in a viral life cycle and a prime target of antiviral drugs. Herpesviruses use an ATP-driven packaging motor/terminase complex to translocate and cleave concatemeric dsDNA into procapsids but its molecular architecture and mechanism are unknown. We report atomic structures of a herpesvirus hexameric terminase complex in both the apo and ADP•BeF3-bound states. Each subunit of the hexameric ring comprises three components-the ATPase/terminase pUL15 and two regulator/fixer proteins, pUL28 and pUL33-unlike bacteriophage terminases. Distal to the nuclease domains, six ATPase domains form a central channel with conserved basic-patches conducive to DNA binding and trans-acting arginine fingers are essential to ATP hydrolysis and sequential DNA translocation. Rearrangement of the nuclease domains mediated by regulatory domains converts DNA translocation mode to cleavage mode. Our structures favor a sequential revolution model for DNA translocation and suggest mechanisms for concerted domain rearrangements leading to DNA cleavage.

Structure of the HRV-C 3C-Rupintrivir Complex Provides New Insights for Inhibitor Design.

Human rhinoviruses (HRVs) are the predominant infectious agents for the common cold worldwide. The HRV-C species cause severe illnesses in children and are closely related to acute exacerbations of asthma. 3C protease, a highly conserved enzyme, cleaves the viral polyprotein during replication and assists the virus in escaping the host immune system. These key roles make 3C protease an important drug target. A few structures of 3Cs complexed with an irreversible inhibitor rupintrivir have been determined. These structures shed light on the determinants of drug specificity. Here we describe the structures of HRV-C15 3C in free and inhibitor-bound forms. The volume-decreased S1' subsite and half-closed S2 subsite, which were thought to be unique features of enterovirus A 3C proteases, appear in the HRV-C 3C protease. Rupintrivir assumes an "intermediate" conformation in the complex, which might open up additional avenues for the design of potent antiviral inhibitors. Analysis of the features of the three-dimensional structures and the amino acid sequences of 3C proteases suggest new applications for existing drugs.

Radio Frequency Plasma-Enhanced Reactive Magnetron Sputtering Deposition of α-SiN x on Photonic Crystal-Laser Diodes for Facet Passivation.

Amorphous silicon nitride (α-SiN x ) films were coated on a photonic crystal-laser diode by the radio frequency magnetron sputtering method. Sputtering deposition conditions were changed to obtain α-SiN x films with different properties. The optical parameters and morphologies of the products were systemically characterized by spectroscopic ellipsometry fitting, energy-dispersive X-ray spectroscopy, atomic force microscopy, and performance of LDs coated with α-SiN x films were tested at 25 °C. Physical mechanisms of sputtering were explained in detail. α-SiN x with a band gap of 4.4 eV and a refractive index of 2.03 at 980 nm were grown. The extinction coefficient equal to 0 at 980 nm, and the surface morphology tended to be homogeneous and dense. The main influencing factors related to the catastrophic optical mirror damage (COMD) phenomenon were investigated. Then plasma pretreatment was implemented to eliminate defects and improve the cavity surface quality and further optimized by measuring the intensity of photoluminescence. Afterward, a rapid annealing method was also carried out to improve coating performance. Finally, α-SiN x acted as a passivation layer in the antireflection film coated on the LD facet, and the COMD threshold increased from 5 to 15.2 W, which led to a higher reliability than nonoptimized LDs and elimination of the COMD phenomenon.