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InN holds great promise for a wide range of applications, including broadband optical devices, high-frequency electronic devices, and serving as a substrate for highly sensitive gas detectors and efficient catalysts. Nevertheless, production of high-quality InN thin films through epitaxy has remained a significant challenge. This is primarily due to complexities arising from the low dissociation temperature and the high N2 equilibrium pressure of InN, which lead to formation of numerous nitrogen vacancies and a propensity to generate indium metal nanoparticles. Efficacy of the indium bilayer pre-deposition method for InN film growth has been established in prior studies. In this work, we introduce a specialized hydrogen insertion method to further enhance nitridation of the indium bilayer. The corresponding kinetic mechanism has been demonstrated through theoretical simulations and practical epitaxy experiments, leading to the development of an optimized hydrogen insertion process. This research represents a substantial improvement over existing InN epitaxial methods that involve control of the indium bilayer and introduces a novel mechanism for enhancing InN heteroepitaxy. [ABSTRACT FROM AUTHOR]

Aluminum hydride (AlH3), as a potential new metal fuel, has attracted extensive attention in the field of hydrogen storage materials, propellants and energetic materials due to its excellent properties. In this paper, a new organic liquid phase reduction method was used to prepare AlH3, and the microstructure and composition of the prepared samples were characterized by SEM–EDS, XRD, FT-IR, XPS, etc. The main crystal phase of the prepared AlH3 was α-AlH3, with high quality and no other impurities. The thermal decomposition behavior and non-isothermal reaction kinetics of AlH3 were investigated by TG-DSC. The results show that there are three exothermic stages in the heating process of AlH3: dehydrogenation of AlH3, first oxidation of Al, and second oxidation of Al. The activation energy of the dehydrogenation of AlH3 is 77.8675 kJ/mol (Kissinger method) and 81.4862 kJ/mol (Ozawa method), respectively. The morphology evolution of AlH3 particles during the heating process (300–3000 K) was simulated using a reaction kinetics method based on the ReaxFF force field. The response behavior of AlH3 in the instantaneous high-temperature detonation environment was investigated using the high-energy laser-induced shock wave technique. Under the impact of a high-energy laser (1006 mJ), the AlH3 sample undergoes an ultra-fast reaction and generates a large amount of plasma. The expansion of the plasma pushes the surrounding air to form a supersonic shock wave and propagates forward. The shock wave propagation velocity of AlH3 is 690.99 m/s in the range of 2.1–12.35 μs, and the higher the laser energy, the faster the shock wave propagation velocity. This study provides the basis for the application of AlH3. [ABSTRACT FROM AUTHOR]

In this study, the crystal appearance of industrial grade 2,6-diamino-3,5-dinitropyridine (PYX) was mostly needle-shaped or rod-shaped with an average aspect ratio of 3.47 and roundness of 0.47. According to national military standards, the explosion percentage of impact sensitivity s about 40% and friction sensitivity is about 60%. To improve loading density and pressing safety, the solvent–antisolvent method was used to optimize the crystal morphology, i.e., to reduce the aspect ratio and increase the roundness value. Firstly, the solubility of PYX in DMSO, DMF, and NMP was measured by the static differential weight method, and the solubility model was established. The results showed that the Apelblat equation and Van't Hoff equation could be used to clarify the temperature dependence of PYX solubility in a single solvent. Scanning electron microscopy (SEM) was used to characterize the morphology of the recrystallized samples. After recrystallization, the aspect ratio of the samples decreased from 3.47 to 1.19, and roundness increased from 0.47 to 0.86. The morphology was greatly improved, and the particle size decreased. The structures before and after recrystallization were characterized by infrared spectroscopy (IR). The results showed that no chemical structure changes occurred during recrystallization, and the chemical purity was improved by 0.7%. According to the GJB-772A-97 explosion probability method, the mechanical sensitivity of explosives was characterized. After recrystallization, the impact sensitivity of explosives was significantly reduced from 40% to 12%. A differential scanning calorimeter (DSC) was used to study the thermal decomposition. The thermal decomposition temperature peak of the sample after recrystallization was 5 °C higher than that of the raw PYX. The thermal decomposition kinetic parameters of the samples were calculated by AKTS software, and the thermal decomposition process under isothermal conditions was predicted. The results showed that the activation energy (E) of the samples after recrystallization was higher by 37.9~527.6 kJ/mol than raw PYX, so the thermal stability and safety of the recrystallized samples were improved. [ABSTRACT FROM AUTHOR]

The label-free quantification (LFQ) has emerged as an exceptional technique in proteomics owing to its broad proteome coverage, great dynamic ranges and enhanced analytical reproducibility. Due to the extreme difficulty lying in an in-depth quantification, the LFQ chains incorporating a variety of transformation, pretreatment and imputation methods are required and constructed. However, it remains challenging to determine the well-performing chain, owing to its strong dependence on the studied data and the diverse possibility of integrated chains. In this study, an R package EVALFQ was therefore constructed to enable a performance evaluation on >3000 LFQ chains. This package is unique in (a) automatically evaluating the performance using multiple criteria, (b) exploring the quantification accuracy based on spiking proteins and (c) discovering the well-performing chains by comprehensive assessment. All in all, because of its superiority in assessing from multiple perspectives and scanning among over 3000 chains, this package is expected to attract broad interests from the fields of proteomic quantification. The package is available at https://github.com/idrblab/EVALFQ. [ABSTRACT FROM AUTHOR]

The type IV bacterial secretion system (SS) is reported to be one of the most ubiquitous SSs in nature and can induce serious conditions by secreting type IV SS effectors (T4SEs) into the host cells. Recent studies mainly focus on annotating new T4SE from the huge amount of sequencing data, and various computational tools are therefore developed to accelerate T4SE annotation. However, these tools are reported as heavily dependent on the selected methods and their annotation performance need to be further enhanced. Herein, a convolution neural network (CNN) technique was used to annotate T4SEs by integrating multiple protein encoding strategies. First, the annotation accuracies of nine encoding strategies integrated with CNN were assessed and compared with that of the popular T4SE annotation tools based on independent benchmark. Second, false discovery rates of various models were systematically evaluated by (1) scanning the genome of Legionella pneumophila subsp. ATCC 33152 and (2) predicting the real-world non-T4SEs validated using published experiments. Based on the above analyses, the encoding strategies, (a) position-specific scoring matrix (PSSM), (b) protein secondary structure & solvent accessibility (PSSSA) and (c) one-hot encoding scheme (Onehot), were identified as well-performing when integrated with CNN. Finally, a novel strategy that collectively considers the three well-performing models (CNN-PSSM, CNN-PSSSA and CNN-Onehot) was proposed, and a new tool (CNN-T4SE, https://idrblab.org/cnnt4se/) was constructed to facilitate T4SE annotation. All in all, this study conducted a comprehensive analysis on the performance of a collection of encoding strategies when integrated with CNN, which could facilitate the suppression of T4SS in infection and limit the spread of antimicrobial resistance. [ABSTRACT FROM AUTHOR]

Microbial community (MC) has great impact on mediating complex disease indications, biogeochemical cycling and agricultural productivities, which makes metaproteomics powerful technique for quantifying diverse and dynamic composition of proteins or peptides. The key role of biostatistical strategies in MC study is reported to be underestimated, especially the appropriate application of feature selection method (FSM) is largely ignored. Although extensive efforts have been devoted to assessing the performance of FSMs, previous studies focused only on their classification accuracy without considering their ability to correctly and comprehensively identify the spiked proteins. In this study, the performances of 14 FSMs were comprehensively assessed based on two key criteria (both sample classification and spiked protein discovery) using a variety of metaproteomics benchmarks. First, the classification accuracies of those 14 FSMs were evaluated. Then, their abilities in identifying the proteins of different spiked concentrations were assessed. Finally, seven FSMs (FC, LMEB, OPLS-DA, PLS-DA, SAM, SVM-RFE and T-Test) were identified as performing consistently superior or good under both criteria with the PLS-DA performing consistently superior. In summary, this study served as comprehensive analysis on the performances of current FSMs and could provide a valuable guideline for researchers in metaproteomics. [ABSTRACT FROM AUTHOR]

Label-free quantification (LFQ) with a specific and sequentially integrated workflow of acquisition technique, quantification tool and processing method has emerged as the popular technique employed in metaproteomic research to provide a comprehensive landscape of the adaptive response of microbes to external stimuli and their interactions with other organisms or host cells. The performance of a specific LFQ workflow is highly dependent on the studied data. Hence, it is essential to discover the most appropriate one for a specific data set. However, it is challenging to perform such discovery due to the large number of possible workflows and the multifaceted nature of the evaluation criteria. Herein, a web server ANPELA (https://idrblab.org/anpela/) was developed and validated as the first tool enabling performance assessment of whole LFQ workflow (collective assessment by five well-established criteria with distinct underlying theories), and it enabled the identification of the optimal LFQ workflow(s) by a comprehensive performance ranking. ANPELA not only automatically detects the diverse formats of data generated by all quantification tools but also provides the most complete set of processing methods among the available web servers and stand-alone tools. Systematic validation using metaproteomic benchmarks revealed ANPELA's capabilities in 1 discovering well-performing workflow(s), (2) enabling assessment from multiple perspectives and (3) validating LFQ accuracy using spiked proteins. ANPELA has a unique ability to evaluate the performance of whole LFQ workflow and enables the discovery of the optimal LFQs by the comprehensive performance ranking of all 560 workflows. Therefore, it has great potential for applications in metaproteomic and other studies requiring LFQ techniques, as many features are shared among proteomic studies. [ABSTRACT FROM AUTHOR]