RGS19 activates the MYH9/ß-catenin/c-Myc positive feedback loop in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common fatal cancers worldwide, and the identification of novel treatment targets and prognostic biomarkers is urgently needed because of its unsatisfactory prognosis. Regulator of G-protein signaling 19 (RGS19) is a multifunctional protein that regulates the progression of various cancers. However, the specific function of RGS19 in HCC remains unclear. The expression of RGS19 was determined in clinical HCC samples. Functional and molecular biology experiments involving RGS19 were performed to explore the potential mechanisms of RGS19 in HCC. The results showed that the expression of RGS19 is upregulated in HCC tissues and is significantly associated with poor prognosis in HCC patients. RGS19 promotes the proliferation and metastasis of HCC cells in vitro and in vivo. Mechanistically, RGS19, via its RGS domain, stabilizes the MYH9 protein by directly inhibiting the interaction of MYH9 with STUB1, which has been identified as an E3 ligase of MYH9. Moreover, RGS19 activates ß-catenin/c-Myc signaling via MYH9, and RGS19 is also a transcriptional target gene of c-Myc. A positive feedback loop formed by RGS19, MYH9, and the ß-catenin/c-Myc axis was found in HCC. In conclusion, our research revealed that competition between RGS19 and STUB1 is a critical mechanism of MYH9 regulation and that the RGS19/MYH9/ß-catenin/c-Myc feedback loop may represent a promising strategy for HCC therapy.

An at-leg pellet and associated Penicillium sp. provide multiple protections to mealybugs.

Beneficial fungi are well known for their contribution to insects' adaptation to diverse habitats. However, where insect-associated fungi reside and the underlying mechanisms of insect-fungi interaction are not well understood. Here, we show a pellet-like structure on the legs of mealybugs, a group of economically important insect pests. This at-leg pellet, formed by mealybugs feeding on tomato but not by those on cotton, potato, or eggplant, originates jointly from host secretions and mealybug waxy filaments. A fungal strain, Penicillium citrinum, is present in the pellets and it colonizes honeydew. P. citrinum can inhibit mealybug fungal pathogens and is highly competitive in honeydew. Compounds within the pellets also have inhibitory activity against mealybug pathogens. Further bioassays suggest that at-leg pellets can improve the survival rate of Phenacoccus solenopsis under pathogen pressure, increase their sucking frequency, and decrease the defense response of host plants. Our study presents evidences on how a fungi-associated at-leg pellet provides multiple protections for mealybugs through suppressing pathogens and host defense, providing new insights into complex insect × fungi × plant interactions and their coevolution.

Pulmonary redox imbalance drives early fibroproliferative response in moderate/severe coronavirus disease-19 acute respiratory distress syndrome and impacts long-term lung abnormalities.

BACKGROUND: COVID-19-associated pulmonary fibrosis remains frequent. This study aimed to investigate pulmonary redox balance in COVID-19 ARDS patients and possible relationship with pulmonary fibrosis and long-term lung abnormalities. METHODS: Baseline data, chest CT fibrosis scores, N-terminal peptide of alveolar collagen III (NT-PCP-III), transforming growth factor (TGF)-ß1, superoxide dismutase (SOD), reduced glutathione (GSH), oxidized glutathione (GSSG) and malondialdehyde (MDA) in bronchoalveolar lavage fluid (BALF) were first collected and compared between SARS-CoV-2 RNA positive patients with moderate to severe ARDS (n = 65, COVID-19 ARDS) and SARS-CoV-2 RNA negative non-ARDS patients requiring mechanical ventilation (n = 63, non-ARDS). Then, correlations between fibroproliferative (NT-PCP-III and TGF-ß1) and redox markers were analyzed within COVID-19 ARDS group, and comparisons between survivor and non-survivor subgroups were performed. Finally, follow-up of COVID-19 ARDS survivors was performed to analyze the relationship between pulmonary abnormalities, fibroproliferative and redox markers 3 months after discharge. RESULTS: Compared with non-ARDS group, COVID-19 ARDS group had significantly elevated chest CT fibrosis scores (p < 0.001) and NT-PCP-III (p < 0.001), TGF-ß1 (p < 0.001), GSSG (p < 0.001), and MDA (p < 0.001) concentrations on admission, while decreased SOD (p < 0.001) and GSH (p < 0.001) levels were observed in BALF. Both NT-PCP-III and TGF-ß1 in BALF from COVID-19 ARDS group were directly correlated with GSSG (p < 0.001) and MDA (p < 0.001) and were inversely correlated with SOD (p < 0.001) and GSH (p < 0.001). Within COVID-19 ARDS group, non-survivors (n = 28) showed significant pulmonary fibroproliferation (p < 0.001) with more severe redox imbalance (p < 0.001) than survivors (n = 37). Furthermore, according to data from COVID-19 ARDS survivor follow-up (n = 37), radiographic residual pulmonary fibrosis and lung function impairment improved 3 months after discharge compared with discharge (p < 0.001) and were associated with early pulmonary fibroproliferation and redox imbalance (p < 0.01). CONCLUSIONS: Pulmonary redox imbalance occurring early in COVID-19 ARDS patients drives fibroproliferative response and increases the risk of death. Long-term lung abnormalities post-COVID-19 are associated with early pulmonary fibroproliferation and redox imbalance.

Impact of PD-L1 gene polymorphisms and interactions with cooking with solid fuel exposure on tuberculosis.

Introduction Given that PD-L1 is a crucial immune checkpoint in regulating T cell responses, the aim of this study was to explore the impact of PD-L1 gene polymorphism and its interaction with cooking with solid fuel on susceptibility to tuberculosis (TB) in Chinese Han populations. Methods A total of 503 TB patients and 494 healthy controls were enrolled in this case-control study. Mass spectrometry (MS) technology was applied to genotype rs2297136 and rs4143815 of PD-L1 genes. The associations between SNPs and TB were assessed using unconditional logistic regression analysis. Marginal structural linear odds models were used to estimate the gene-environment interactions. Results Compared with genotype CC, genotypes GG and CG + GG at rs4143815 locus were significantly associated with susceptibility to TB (OR: 3.074 and 1.506, respectively, P<0.05). However, no statistical association was found between rs2297136 SNP and TB risk. Moreover, the relative excess risk of interaction (RERI) between rs4143815 of the PD-L1 gene and cooking with solid fuel was 2.365 (95% CI 1.922,2.809), suggesting positive interactions with TB susceptibility. Conclusion The rs4143815 polymorphism of the PD-L1 gene was associated with susceptibility to TB in Chinese Han populations. There were significantly positive interactions between rs4143815 and cooking with solid fuel.

The moderating role of work environment in the relationship between proactive personality and personal growth initiative among nurses: a cross-sectional study.

BACKGROUNDS: Personal growth initiative (PGI) is regarded as a meaningful concept with potential value at both the individual and organizational levels, but little is known about the factors that contribute to nurses' PGI. This study aimed to explore how proactive personality and hospital work environment affect PGI of clinical nurses. METHODS: A cross-sectional study was conducted between September and October 2022 among 4414 nurses from 10 tertiary general hospitals in 10 cities in Sichuan, China, using a two-stage sampling method. Self-reported anonymous online questionnaires, such as sociodemographic information survey, personal growth initiative scale II, the 10-item proactive personality scale, and practice environment scale-nursing work index were used to collect data. Multiple hierarchical regression analyses were performed to examine research hypotheses. RESULTS: Among the control variables in this study, nurses' self-perceptions of general health status and professional title positively predicted PGI (ß = 0.462, 95%CI = 0.272-0.653; ß = 1.078, 95%CI = 0.508-1.648). After adding control variables, both proactive personality (ß = 1.143, 95%CI = 1.096-1.190) and work environment (ß = 3.391, 95%CI = 2.904-3.879) positively predicted PGI. The work environment positively moderated the association between proactive personality and PGI (ß = 0.108, 95%CI = 0.025-0.191). These predictors jointly explained 50.3% of the variance in PGI. CONCLUSIONS: Nurses with a greater tendency to have a typical proactive personality have higher levels of personal growth initiative, and this positive effect will be better highlighted in a healthier work environment. Nursing managers should prioritize the employment of people with proactive personality traits, focus on the development and stimulation of proactive personality traits in nurses, and establish a supportive work environment to maximize the personal growth initiative of nurses.

Defect Engineering and Emission Tuning of Wide-Bandgap MAPbCl3 Perovskite.

Lead-chloride perovskites are promising candidates for optoelectronic applications, such as visible-blind UV photodetection. It remains unclear how the deep defects in this wide-bandgap material impact the carrier recombination dynamics. In this work, we study the defect properties of MAPbCl3 (MA = CH3NH3) based on photoluminescence (PL) measurements. Our investigations show that apart from the intrinsic emission, four sub-bandgap emissions emerge, which are very likely to originate from the radiative recombination of excitons bound to several intrinsic vacancy and interstitial defects. The intensity of various emission features can be tuned by adjusting the type and ratio of precursors used during synthesis. Our study not only provides important insights into the defect property and carrier recombination mechanism in this class of material but also demonstrates efficient strategies for defect passivation and engineering, paving the way for further development of lead-chloride perovskite-based optoelectronic devices.

Triterpenoids with α-glucosidase inhibitory and cytotoxic activities from the fruits of Schisandra chinensis.

An intensive phytochemical investigation into the fruits of Schisandra chinensis afforded 28 triterpenoids incorporating diverse backbones with methyl-migration, ring-expansion and ring-opening features. Among them, ten compounds (1-10) including three likely extracting artefacts (8-10) were described for the first time. Their structures were fully characterized by comprehensive spectroscopic analyses, with the absolute configurations established via electronic circular dichroism and Mosher's NMR techniques. Preliminary biological evaluations revealed that nine isolates showed inhibitory activity against the hyperglycemic target α-glycosidase and 12 compounds exerted cytotoxicity toward three female tumor cell lines (Hela (cervical), MDA-MB231 and MCF-7 (breast)). Compound 6 exhibited the most promising potency on all the three tested cancer cells, and further assessment demonstrated that it could induce significant cell apoptosis and cycle arrest, as well as suppress cell migration, by regulating relevant proteins in MDA-MB231 cells.

Hspb1 and Lgals3 in spinal neurons are closely associated with autophagy following excitotoxicity based on machine learning algorithms.

Excitotoxicity represents the primary cause of neuronal death following spinal cord injury (SCI). While autophagy plays a critical and intricate role in SCI, the specific mechanism underlying the relationship between excitotoxicity and autophagy in SCI has been largely overlooked. In this study, we isolated primary spinal cord neurons from neonatal rats and induced excitotoxic neuronal injury by high concentrations of glutamic acid, mimicking an excitotoxic injury model. Subsequently, we performed transcriptome sequencing. Leveraging machine learning algorithms, including weighted correlation network analysis (WGCNA), random forest analysis (RF), and least absolute shrinkage and selection operator analysis (LASSO), we conducted a comprehensive investigation into key genes associated with spinal cord neuron injury. We also utilized protein-protein interaction network (PPI) analysis to identify pivotal proteins regulating key gene expression and analyzed key genes from public datasets (GSE2599, GSE20907, GSE45006, and GSE174549). Our findings revealed that six genes-Anxa2, S100a10, Ccng1, Timp1, Hspb1, and Lgals3-were significantly upregulated not only in vitro in neurons subjected to excitotoxic injury but also in rats with subacute SCI. Furthermore, Hspb1 and Lgals3 were closely linked to neuronal autophagy induced by excitotoxicity. Our findings contribute to a better understanding of excitotoxicity and autophagy, offering potential targets and a theoretical foundation for SCI diagnosis and treatment.

Ficolin-A induces macrophage polarization to a novel pro-inflammatory phenotype distinct from classical M1.

BACKGROUND: Macrophages are key inflammatory immune cells that orchestrate the initiation and progression of autoimmune diseases. The characters of macrophage in diseases are determined by its phenotype in response to the local microenvironment. Ficolins have been confirmed as crucial contributors to autoimmune diseases, with Ficolin-2 being particularly elevated in patients with autoimmune diseases. However, whether Ficolin-A stimulates macrophage polarization is still poorly understood. METHODS: We investigated the transcriptomic expression profile of murine bone marrow-derived macrophages (BMDMs) stimulated with Ficolin-A using RNA-sequencing. To further confirm a distinct phenotype activated by Ficolin-A, quantitative RT-PCR and Luminex assay were performed in this study. Additionally, we assessed the activation of underlying cell signaling pathways triggered by Ficolin-A. Finally, the impact of Ficolin-A on macrophages were investigated in vivo through building Collagen-induced arthritis (CIA) and Dextran Sulfate Sodium Salt (DSS)-induced colitis mouse models with Fcna-/- mice. RESULTS: Ficolin-A activated macrophages into a pro-inflammatory phenotype distinct to LPS-, IFN-γ- and IFN-γ + LPS-induced phenotypes. The transcriptomic profile induced by Ficolin-A was primarily characterized by upregulation of interleukins, chemokines, iNOS, and Arginase 1, along with downregulation of CD86 and CD206, setting it apart from the M1 and M2 phenotypes. The activation effect of Ficolin-A on macrophages deteriorated the symptoms of CIA and DSS mouse models, and the deletion of Fcna significantly alleviated the severity of diseases in mice. CONCLUSION: Our work used transcriptomic analysis by RNA-Seq to investigate the impact of Ficolin-A on macrophage polarization. Our findings demonstrate that Ficolin-A induces a novel pro-inflammatory phenotype distinct to the phenotypes activated by LPS, IFN-γ and IFN-γ + LPS on macrophages.

Error detection for radiotherapy planning validation based on deep learning networks.

BACKGROUND: Quality assurance (QA) of patient-specific treatment plans for intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) necessitates prior validation. However, the standard methodology exhibits deficiencies and lacks sensitivity in the analysis of positional dose distribution data, leading to difficulties in accurately identifying reasons for plan verification failure. This issue complicates and impedes the efficiency of QA tasks. PURPOSE: The primary aim of this research is to utilize deep learning algorithms for the extraction of 3D dose distribution maps and the creation of a predictive model for error classification across multiple machine models, treatment methodologies, and tumor locations. METHOD: We devised five categories of validation plans (normal, gantry error, collimator error, couch error, and dose error), conforming to tolerance limits of different accuracy levels and employing 3D dose distribution data from a sample of 94 tumor patients. A CNN model was then constructed to predict the diverse error types, with predictions compared against the gamma pass rate (GPR) standard employing distinct thresholds (3%, 3 mm; 3%, 2 mm; 2%, 2 mm) to evaluate the model's performance. Furthermore, we appraised the model's robustness by assessing its functionality across diverse accelerators. RESULTS: The accuracy, precision, recall, and F1 scores of CNN model performance were 0.907, 0.925, 0.907, and 0.908, respectively. Meanwhile, the performance on another device is 0.900, 0.918, 0.900, and 0.898. In addition, compared to the GPR method, the CNN model achieved better results in predicting different types of errors. CONCLUSION: When juxtaposed with the GPR methodology, the CNN model exhibits superior predictive capability for classification in the validation of the radiation therapy plan on different devices. By using this model, the plan validation failures can be detected more rapidly and efficiently, minimizing the time required for QA tasks and serving as a valuable adjunct to overcome the constraints of the GPR method.

Valorization of Ginkgo biloba Leaf Powder as a Substrate in King Oyster Mushroom (Pleurotus eryngii) Cultivation.

Ginkgo biloba is widely planted as a colorful foliage tree, and its leaf can be used as a biomass energy source, but it has been underutilized for a long time. The aim of this study was to investigate the potential of garden waste as a substrate component in the cultivation process of the king oyster mushroom (Pleurotus eryngii), with the goal of enhancing both the yield of P. eryngii and the efficiency of energy use. The percentages of G. biloba leaf powder in the substrate were 10.5% and 21% to replace sawdust or sugarcane bagasse in a typical substrate. A substrate formulation that could completely replace sawdust and sugarcane bagasse was selected by analyzing mycelial growth rate, days of production, fruiting body length, biological efficiency, yield, stipe thickness, pileus diameter and laccase activity. The results showed that Y1 (treatment with 21% G. biloba leaf powder and sugarcane bagasse) had the highest yield (303.1 ± 31.9 g), which was higher than that of CK (control) (259.3 ± 37.4 g). The crude fiber content of the samples grown on substrate Y1 (as 7.43%) was higher than CK (7.37%). In addition, P. eryngii grown on substrate Y1 had the highest laccase activity for the complete colonization of the mycelium. Thus, these findings suggest that G. biloba leaf powder represents a viable and economical supplement for enhancing both the yield and quality of P. eryngii.

Targeting ferroptosis in neuroimmune and neurodegenerative disorders for the development of novel therapeutics.

Neuroimmune and neurodegenerative ailments impose a substantial societal burden. Neuroimmune disorders involve the intricate regulatory interactions between the immune system and the central nervous system. Prominent examples of neuroimmune disorders encompass multiple sclerosis and neuromyelitis optica. Neurodegenerative diseases result from neuronal degeneration or demyelination in the brain or spinal cord, such as Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis. The precise underlying pathogenesis of these conditions remains incompletely understood. Ferroptosis, a programmed form of cell death characterised by lipid peroxidation and iron overload, plays a pivotal role in neuroimmune and neurodegenerative diseases. In this review, we provide a detailed overview of ferroptosis, its mechanisms, pathways, and regulation during the progression of neuroimmune and neurodegenerative diseases. Furthermore, we summarise the impact of ferroptosis on neuroimmune-related cells (T cells, B cells, neutrophils, and macrophages) and neural cells (glial cells and neurons). Finally, we explore the potential therapeutic implications of ferroptosis inhibitors in diverse neuroimmune and neurodegenerative diseases.

Efficient Charge Transport in Inverted Perovskite Solar Cells via 2D/3D Ferroelectric Heterojunction.

While the 2D/3D heterojunction is an effective method to improve the power conversion efficiency (PCE) of perovskite solar cells (PSCs), carriers are often confined in the quantum wells (QWs) due to the unique structure of 2D perovskite, which makes the charge transport along the out-of-plane direction difficult. Here, a 2D/3D ferroelectric heterojunction formed by 4,4-difluoropiperidine hydrochloride (2FPD) in inverted PSCs is reported. The enriched 2D perovskite (2FPD)2PbI4 layer with n = 1 on the perovskite surface exhibits ferroelectric response and has oriented dipoles along the out-of-plane direction. The ferroelectricity of the oriented dipole layer facilitates the enhancement of the built-in electric field (1.06 V) and the delay of the cooling process of hot carriers, reflected in the high carrier temperature (above 1400 K) and the prolonged photobleach recovery time (139.85 fs, measured at bandgap), improving the out-of-plane conductivity. In addition, the alignment of energy levels is optimized and exciton binding energy (32.8 meV) is reduced by changing the dielectric environment of the surface. Finally, the 2FPD-treated PSCs achieve a PCE of 24.82% (certified: 24.38%) with the synergistic effect of ferroelectricity and defect passivation, while maintaining over 90% of their initial efficiency after 1000 h of maximum power point tracking.

Laser-Induced Graphene-Based Sensors in Health Monitoring: Progress, Sensing Mechanisms, and Applications.

The rising global population and improved living standards have led to an alarming increase in non-communicable diseases, notably cardiovascular and chronic respiratory diseases, posing a severe threat to human health. Wearable sensing devices, utilizing micro-sensing technology for real-time monitoring, have emerged as promising tools for disease prevention. Among various sensing platforms, graphene-based sensors have shown exceptional performance in the field of micro-sensing. Laser-induced graphene (LIG) technology, a cost-effective and facile method for graphene preparation, has gained particular attention. By converting polymer films directly into patterned graphene materials at ambient temperature and pressure, LIG offers a convenient and environmentally friendly alternative to traditional methods, opening up innovative possibilities for electronic device fabrication. Integrating LIG-based sensors into health monitoring systems holds the potential to revolutionize health management. To commemorate the tenth anniversary of the discovery of LIG, this work provides a comprehensive overview of LIG's evolution and the progress of LIG-based sensors. Delving into the diverse sensing mechanisms of LIG-based sensors, recent research advances in the domain of health monitoring are explored. Furthermore, the opportunities and challenges associated with LIG-based sensors in health monitoring are briefly discussed.

Machine learning based on the EEG and structural MRI can predict different stages of vascular cognitive impairment.

Background: Vascular cognitive impairment (VCI) is a major cause of cognitive impairment in the elderly and a co-factor in the development and progression of most neurodegenerative diseases. With the continuing development of neuroimaging, multiple markers can be combined to provide richer biological information, but little is known about their diagnostic value in VCI. Methods: A total of 83 subjects participated in our study, including 32 patients with vascular cognitive impairment with no dementia (VCIND), 21 patients with vascular dementia (VD), and 30 normal controls (NC). We utilized resting-state quantitative electroencephalography (qEEG) power spectra, structural magnetic resonance imaging (sMRI) for feature screening, and combined them with support vector machines to predict VCI patients at different disease stages. Results: The classification performance of sMRI outperformed qEEG when distinguishing VD from NC (AUC of 0.90 vs. 0,82), and sMRI also outperformed qEEG when distinguishing VD from VCIND (AUC of 0.8 vs. 0,0.64), but both underperformed when distinguishing VCIND from NC (AUC of 0.58 vs. 0.56). In contrast, the joint model based on qEEG and sMRI features showed relatively good classification accuracy (AUC of 0.72) to discriminate VCIND from NC, higher than that of either qEEG or sMRI alone. Conclusion: Patients at varying stages of VCI exhibit diverse levels of brain structure and neurophysiological abnormalities. EEG serves as an affordable and convenient diagnostic means to differentiate between different VCI stages. A machine learning model that utilizes EEG and sMRI as composite markers is highly valuable in distinguishing diverse VCI stages and in individually tailoring the diagnosis.

Architecting the metabolic reprogramming survival risk framework in LUAD through single-cell landscape analysis: three-stage ensemble learning with genetic algorithm optimization.

Recent studies have increasingly revealed the connection between metabolic reprogramming and tumor progression. However, the specific impact of metabolic reprogramming on inter-patient heterogeneity and prognosis in lung adenocarcinoma (LUAD) still requires further exploration. Here, we introduced a cellular hierarchy framework according to a malignant and metabolic gene set, named malignant & metabolism reprogramming (MMR), to reanalyze 178,739 single-cell reference profiles. Furthermore, we proposed a three-stage ensemble learning pipeline, aided by genetic algorithm (GA), for survival prediction across 9 LUAD cohorts (n = 2066). Throughout the pipeline of developing the three stage-MMR (3 S-MMR) score, double training sets were implemented to avoid over-fitting; the gene-pairing method was utilized to remove batch effect; GA was harnessed to pinpoint the optimal basic learner combination. The novel 3 S-MMR score reflects various aspects of LUAD biology, provides new insights into precision medicine for patients, and may serve as a generalizable predictor of prognosis and immunotherapy response. To facilitate the clinical adoption of the 3 S-MMR score, we developed an easy-to-use web tool for risk scoring as well as therapy stratification in LUAD patients. In summary, we have proposed and validated an ensemble learning model pipeline within the framework of metabolic reprogramming, offering potential insights for LUAD treatment and an effective approach for developing prognostic models for other diseases.

Genome-wide methylation, transcriptome and characteristic metabolites reveal the balance between diosgenin and brassinosteroids in Dioscorea zingiberensis.

Diosgenin (DG) is a bioactive metabolite isolated from Dioscorea species, renowned for its medicinal properties. Brassinosteroids (BRs) are a class of crucial plant steroidal hormones. Cholesterol and campesterol are important intermediates of DG and BR biosynthesis, respectively. DG and BRs are structurally similar components; however, the regulatory network and metabolic interplays have not been fully elucidated. In an effort to decode these complex networks, we conducted a comprehensive study integrating genome-wide methylation, transcriptome and characteristic metabolite data from Dioscorea zingiberensis. Leveraging these data, we were able to construct a comprehensive regulatory network linking DG and BRs. Mass spectrometry results enabled us to clarify the alterations in cholesterol, campesterol, diosgenin, and castasterone (one of the major active BRs). The DG content decreased by 27.72% at 6 h after brassinolide treatment, whereas the content increased by 85.34% at 6 h after brassinazole treatment. Moreover, we pinpointed DG/BR-related genes, such as CASs, CYP90s, and B3-ARFs, implicated in the metabolic pathways of DG and BRs. Moreover, CASs and CYP90s exhibit hypomethylation, which is closely related to their high transcription. These findings provide robust evidence for the homeostasis between DG and BRs. In conclusion, our research revealed the existence of a balance between DG and BRs in D. zingiberensis. Furthermore, our work not only provides new insights into the relationship between the two pathways but also offers a fresh perspective on the functions of secondary metabolites.

Effects of gamma-ray irradiation on material and electrical properties of AlN gate dielectric on 4H-SiC.

This article investigates the radiation effects on as-deposited and annealed AlN films on 4H-SiC substrates under gamma-rays. The AlN films are prepared using plasma-enhanced-atomic-layer-deposition on an n-type 4H-SiC substrate. The AlN/4H-SiC MIS structure is subjected to gamma-ray irradiation with total doses of 0, 300, and 600 krad(Si). Physical, chemical, and electrical methods were employed to study the variations in surface morphology, charge transport, and interfacial trapping characteristics induced by irradiation. After 300 krad(Si) irradiation, the as-deposited and annealed samples exhibit their highest root mean square values of 0.917 nm and 1.190 nm, respectively, which is attributed to N vacancy defects induced by irradiation. Under irradiation, the flatband voltage (Vfb) of the as-deposited sample shifts from 2.24 to 0.78 V, while the annealed sample shifts from 1.18 to 2.16 V. X-ray photoelectron spectrum analysis reveals the decomposition of O-related defects in the as-deposited AlN and the formation of Al(NOx)ycompounds in the annealed sample. Furthermore, the space-charge-limits-conduction (SCLC) in the as-deposited sample is enhanced after radiation, while the barrier height of the annealed sample decreases from 1.12 to 0.84 eV, accompanied by the occurrence of the SCLC. The physical mechanism of the degradation of electrical performance in irradiated devices is the introduction of defects like N vacancies and O-related defects like Al(NOx)y. These findings provide valuable insights for SiC power devices in space applications.