Identification of Linear Epitopes in the C-Terminal Region of ASFV p72 Protein.

African swine fever, which is induced by the African swine fever virus (ASFV), poses a significant threat to the global pig industry due to its high lethality in domestic pigs and wild boars. Despite the severity of the disease, there is a lack of effective vaccines and drugs against the ASFV. The p72 protein, constituting 31 to 33% of the total virus particle mass, serves as the primary capsid protein of ASFV. It is a crucial antigen for the development of ASF subunit vaccines and serological diagnostic methods. In this investigation, 27 monoclonal antibodies (mAbs) were generated through mouse immunization with the truncated C-terminal p72 protein expressed by Escherichia coli. Among these, six mAbs exhibited binding to the p72 trimer, with their respective recognized epitopes identified as 542VTAHGINLIDKF553, 568GNAIKTP574, and 584FALKPREEY592. All three epitopes were situated within the interval sequences of functional units of the C-terminal jelly-roll barrel of p72. Notably, two epitopes, 568GNAIKTP574 and 584FALKPREEY592, were internal to the p72 trimer, while the epitope 542VTAHGINLIDKF553 was exposed on the surface of the trimer and consistently conserved across all ASFV genotypes. These findings enhance our comprehension of the antigenic function and structure of the p72 protein, facilitating the utilization of p72 in the development of diagnostic techniques for ASFV.

Simple Solution Preparation of Cs2SnI6 Films and Their Applications in Solid-State DSSCs.

Cs2SnI6 powder is, for the first time, solution-prepared via the formula CsI + SnI2 + I2 → Cs2SnI6. The product is highly pure and air/thermal stable. It is found that N,N-dimethylformamide (DMF) and methanol induce severe Cs2SnI6 deterioration with the appearance of a CsI phase in film preparation from Cs2SnI6 powder, while solvents of γ-butyrolactone (GBL) and ethylene glycol methyl ether (EGME) (Film-EGME) give better results. Then, by introducing EGME solvent, in situ preparation of Cs2SnI6 films (Film-1 to Film-4) is realized under solution reaction, which is found to be dominated by thermal dynamic process, i.e., highly pure/oriented Film-4 is obtained under the maximum reagent-concentration. Besides, for good reaction, the solubility of solvent should be balanced among all the reagents and products. Solid-state dye sensitized solar cells (ss-DSSCs) comprising a Cs2SnI6 electrolyte are investigated. The power conversion efficiencies (PCEs) of the ss-DSSCs based on solution-casted Film-EGME and the in situ-prepared Film-4 are 1.81% and 3.30%, respectively. Particularly, with the in situ prepared Cs2SnI6 films, it is found that the open circuit voltages of the ss-DSSCs are closely related to their gap states. When additive is added in Cs2SnI6 electrolyte, a PCE of 6.14% is obtained in an ss-DSSC. Our work highlights the importance of solvent in film preparation and the role of Cs2SnI6 gap states in device performance.

Health State Estimation of On-Board Lithium-Ion Batteries Based on GMM-BID Model.

As a single feature parameter cannot comprehensively evaluate the health status of a battery, a multi-source information fusion method based on the Gaussian mixture model and Bayesian inference distance is proposed for the health assessment of vehicle batteries. The missing and abnormal data from real-life vehicle operations are preprocessed to extract the sensitive characteristic parameters which determine the battery performance. The normal state Gaussian mixture model is established using the fault-free state data, whereas the Bayesian inference distance is constructed as an index to quantitatively evaluate the battery performance state. In order to solve the problem that abnormal data may exist in the measured data and introduce errors into evaluation results, the determination rules of abnormal data are formulated. The verification of real-life vehicle operation data reveals that the proposed method can accurately evaluate the onboard battery state and reduce safety hazards of electric vehicles during the normal operation process.

Room-temperature preparation of TiO2/graphene composite photoanodes for efficient dye-sensitized solar cells.

Semi-transparent TiO2/graphene photoanodes are prepared at room temperature via an electrophoretic deposition method followed by compression and applied in dye-sensitized solar cells (DSSCs). Compression enhances the power conversion efficiency (PCE) of a DSSC, which constitutes up 18.4 times improvement compared to the uncompressed device. Incorporating graphene into the compressed film further improves the PCE by 28.8%. Simultaneously, compressing and graphene incorporating can greatly increase the film's transmittance at long wavelengths, benefiting to the use of DSSCs as front unit in tandem solar cells. Scanning electron microscopy, porosity measurements, electrochemical impedance spectroscopy and open circuit voltage decay are performed to investigate the mechanisms. It is demonstrated that compressing a film can reduce the porosity and improve the inter-particle connections, which accounts for the increased light transmittance and enhanced PCE. The incorporated graphene can provide extra charge carrier pathway due to its excellent charge transport properties, as well as protect TiO2 nanostructure by preventing film cracking upon pressing due to its good flexibility, thus increases PCE to 6.75%, which, to our best knowledge, is the highest value among DSSCs with room-temperature prepared photoanodes.

Roller Bearing Performance Degradation Assessment Based on Fusion of Multiple Features of Electrostatic Sensors.

This paper presents a new method to assess the performance degradation of roller bearings based on the fusion of multiple features, with the aim of improving the early degradation detection ability of the electrostatic monitoring system. At first, a set of feature parameters of the electrostatic monitoring system indicating the normal state of the bearings are extracted from the perspective of the time domain, frequency domain and complexity. Then, the parameter set is processed to reduce the dimensions and eliminate the redundancy using spectral regression. With the processed features, a Gaussian mixed model is established to gauge the health of the bearing, providing the distance value obtained using Bayesian inference as a quantitative indicator for assessing the performance degradation. The method is applied to access the life of a bearing in which the mechanic fatigue is artificially accelerated. The test results show that the proposed method can better reflect the degradation process of the bearing compared to other evaluation methods. This enables the electrostatic monitoring technique to detect the degradation of the bearing earlier than the vibration monitoring, providing a powerful tool for the condition monitoring of roller bearings.