While the CuBi₂O₄-based photocathode has emerged as an ideal candidate for photoelectrochemical water splitting, it is still far from its theoretical values due to poor charge carrier transport, poor electron–hole separation, and instability caused by self-photoelectric-corrosion with electrolytes. Establishing synthesis methods to produce a CuBi₂O₄ photocathode with sufficient cocatalyst sites would be highly beneficial for water splitting. Here, the platinum-enriched porous CuBi₂O₄ nanofiber (CuBi₂O₄/Pt) with uniform coverage and high surface area was prepared as a photocathode through an electrospinning and electrodeposition process for water splitting. The prepared photocathode material was composed of a CuBi₂O₄ nanofiber array, which has a freestanding porous structure, and the Pt nanoparticle is firmly embedded on the rough surface. The highly porous nanofiber structures allow the cocatalyst (Pt) better alignment on the surface of CuBi₂O₄, which can effectively suppress the electron–hole recombination at the electrolyte interface. The as-fabricated CuBi₂O₄ nanofiber has a tetragonal crystal structure, and its band gap was determined to be 1.8 eV. The self-supporting porous structure and electrocatalytic activity of Pt can effectively promote the separation of electron–hole pairs, thus obtaining high photocurrent density (0.21 mA/cm² at 0.6 V vs. RHE) and incident photon-to-current conversion efficiency (IPCE, 4% at 380 nm). This work shows a new view for integrating an amount of Pt nanoparticles with CuBi₂O₄ nanofibers and demonstrates the synergistic effect of cocatalysts for future solar water splitting.

Abrasive filament brushes have been widely used in surface processes for a wide range of applications, including blending, edge-radiusing, and polishing. However, the associated brush mechanics of material removal is still not clear. In order to analyze the brush grinding of aluminium alloy, this paper constructed a kinematic model of a single filament, simulated the scratch process of a single abrasive grain, and investigated the brush force and material removal based on the finite element approach. The simulated result shows that the brush grinding can be changed from elastic–plastic deformation to chip formation when increasing the brush speed to 1000 r/min. The normal and tangential forces increase linearly and quadratically with the increase in the rotation speed (500–5000 r/min), respectively, and increase linearly with the increase in the penetration depth (0.1–1 mm), which is consistent with the experiment results. In addition, the amount of material removal initially increases with the increase in penetration depth, and then decreases. This paper provides a new approach to understanding the process of material removal and is helpful for the selection of reasonable brush parameters in the intelligent grinding control application.

Diamond grinding wheels have been widely used to remove the residual features of cast parts, such as parting lines and pouring risers. However, diamond grains are prone to chemical wear as a result of their strong interaction with ferrous metals. To mitigate this wear, this study proposes the use of a novel water-based hexagonal boron nitride (hBN) as a minimum quantity lubrication (MQL) during the grinding of cast steel and conducted the grinding experiment and molecular dynamics simulation. The experiment demonstrated that compared to dry grinding, the water-based hBN nanofluid can effectively reduce the maximum temperature of a workpiece at contact zone from 408 K to 335 K and change the serious abrasion wear of diamond grain to slightly micro-broken. The molecular dynamics simulation indicates that the flake of hBN can weaken the catalytic effect of iron on the diamond, prevent the diffusion of carbon atom to cast steel, and suppress the graphitization of diamond grain. Additionally, the flake of hBN improves the contact state between the diamond grain and cast steel and reduces the cutting heat and friction coefficient from about 0.5 to 0.25. Thus, the water-based hBN nanofluid as a new MQL was proven to be suitable for the wear inhibition of diamond grain when grinding cast steel.

The removal of rust from large equipment such as trains and ship hulls poses a significant challenge. Traditional methods, such as chemical cleaning, flame rust removal, and laser rust removal, suffer from drawbacks such as high energy consumption, operational complexity, and poor mobility. Sandblasting and high-pressure water jet rust removal face issues such as high consumable costs and environmental pollution. Existing robotic grinding systems often rely on precise measurement of the workpiece surface geometry to perform deburring and polishing tasks; however, they lack the sufficient adaptability and robustness required for rust removal operations. To address these limitations, this study proposes a floating grinding actuator scheme based on compound force-position fuzzy control. By implementing simplified path-point planning, continuous grinding and rust removal can be achieved without requiring the pre-measurement of workpiece geometry data. This solution integrates force and laser displacement sensors to provide real-time compensation for path deviations and ensures adaptability to complex surfaces. A fuzzy derivative-leading PID algorithm was employed to control the grinding force, enabling adaptive force regulation and enhancing the control precision. Rust removal test results demonstrate that under varying advancing speeds, fuzzy derivative-leading PID control can significantly reduce fluctuations in both the grinding force and average error compared to traditional PID control. At a speed of 40 mm/s, excellent control performance was maintained, achieving a rust removal rate of 99.73%. This solution provides an efficient, environmentally friendly, and high-precision automated approach to rust removal using large-scale equipment.

Sensitive detection methods for T4 polynucleotide kinase/phosphatase (T4 PNKPP) are urgently required to obtain information on malignancy and thereby to provide better guidance in PNKP-related diagnostics and drug screening. Although the CRISPR/Cas12a system shows great promise in DNA-based signal amplification protocols, its guide RNAs with small molecular weight often suffer nuclease degradation during storage and utilization, resulting in reduced activation efficiency. Herein, we proposed a self-supplying guide RNA-mediated CRISPR/Cas12a system for the sensitive detection of T4 PNKP in cancer cells, in which multiple copies of guide RNA were generated by in situ transcription. In this assay, T4 PNKP was chosen as a model, and a dsDNA probe with T7 promoter region and the transcription region of guide RNA were involved. Under the action of T4 PNKP, the 5′-hydroxyl group of the dsDNA probe was converted to a phosphate group, which can be recognized and digested by Lambda Exo, resulting in dsDNA hydrolysis. The transcription template was destroyed, which resulted in the failure to generate guide RNA by the transcription pathway. Therefore, the CRISPR/Cas12a system could not be activated to effectively cleavage the F-Q-reporter, and the fluorescence signal was turned off. In the absence of T4 PNKP, the 5′-hydroxyl group of the substrate DNA cannot be digested by Lambda Exo. The intact dsDNA acts as the transcription template to generate a large amount of guide RNA. Finally, the formed Cas12a/gRNA complex triggered the reverse cleavage of Cas12a on the F-Q-reporter, resulting in a “turn-on” fluorescence signal. This strategy displayed sharp sensitivity of T4 PNKP with the limit of detection (LOD) down to 0.0017 mU/mL, which was mainly due to the multiple regulation effect of transcription amplification. In our system, the dsDNA simultaneously serves as the T4 PNKP substrate, transcription template, and Lambda Exo substrate, avoiding the need for multiple probe designs and saving costs. By integrating the target recognition, Lambda Exo activity, and trans-cleavage activity of Cas12a, CRISPR/Cas12a catalyzed the cleavage of fluorescent-labeled short-stranded DNA probes and enabled synergetic signal amplification for sensitive T4 PNKP detection. Furthermore, the T4 PNKP in cancer cells has been evaluated as a powerful tool for biomedical research and clinical diagnosis, proving a good practical application capacity.

Although ceramic fiber brushes have been widely used for deburring and surface finishing, the associated relationship between process parameters and lapping quality is still unclear. In order to optimize the lapping process of ceramic fiber brushes, this paper proposes a multi-layer neural network based on the Gaussian-restricted Boltzmann machine (GRBM), and verified its prediction effectiveness. Compared with a traditional back-propagation neural network, its prediction error was reduced from 7.6% to 4.5%, and the determination coefficient was increased from 0.96 to 0.98, respectively. The comparison results showed that the proposed model can better grasp the relationship between process parameters and machining quality, which can be used as a decision-making foundation for lapping-process optimization.