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The new low‐cost clean pre‐desulfation technology is very important in pyrometallurgy and hydrometallurgy. However, traditional reactors have low space‐time yield and desulfation rate, resulting in high energy consumption and SO2 emissions in the industrial desulfation processes. Herein, dual rotating liquid film reactors (RLFRs) and lime are proposed to construct a recyclable, ultra‐fast, and value‐added desulfation method. Parameter optimization and kinetic calculations prove that the above reactions are controlled by internal diffusion, revealing that RLFR promotes the mass transfer and reaction rate. The new process greatly shortens the desulfation time of lead paste from 40 min to 10 s with a high desulfation rate of 99.7%, and the sulfation time of lime from 30 min to 30 s with a sulfation rate of 98.6% with a net profit of 55.99 ¥/ton by cost accounting. Moreover, ten batches of continuous scale‐up experiments demonstrate the stability of processes, the desulfation and sulfation rates are kept at 99.7% and 98.2%, which greatly reduces the emissions of waste desulfate liquor. This work provides a new universal strategy for a sustainable, low‐cost, and clean desulfation method of waste resources to achieve technical and economic feasibility. This work proposes a new ultra‐fast recyclable intensification desulfation process of lead paste and a mother liquor regenerated by lime via dual rotating liquid film reactors to grind the surface of particles and enhance the mass transfer process, which achieves the desulfation rate and conversion rate of 99.7% and 98.6% in 10 and 30 s, respectively.

This paper presents a novel automated drone system designed for the efficient cleaning of solar panels. The drone, equipped with three rotors and advanced detection sensors, autonomously identifies the precise positioning of solar panels and activates an integrated cleaning mechanism comprising four rotating brushes (two vertical and two horizontal). The inclusion of a tilt mechanism in the two front propellers ensures automatic adjustment of the propeller axes, enhancing precise control of the drone’s movements during cleaning phases. Complemented by a robotic component featuring a water reservoir and a precision-spraying system, the entire system dynamically regulates water pressure, flow rates, and distribution based on the real-time conditions of the solar panels, thereby optimizing the cleaning process. Powered by solar energy, the system offers an autonomous and economically viable solution, mitigating the necessity for frequent recharging. This advanced technology not only enhances the energy efficiency of solar panels but also extends their operational lifespan. Consequently, the proposed system contributes significantly to the creation of a more dependable and sustainable solar-energy production infrastructure, simultaneously promoting environmentally responsible practices through efficient water utilization.

Up to date, different physical and chemical cleaning protocols are necessary to limit membrane fouling in membrane bioreactors. This paper deals with a mechanical cleaning process, which aims at the avoidance of hypochlorite and other critical chemicals in MBR with submerged flat sheet modules. The process basically consists of the addition of plastic particles into the loop circulation within submerged membrane modules. Investigations of two pilot plants are presented: Pilot plant 1 is equipped with a 10 m2 membrane module and operated with a translucent model suspension; pilot plant 2 is equipped with four 50 m2 membrane modules and operated with pretreated sewage. Results of pilot plant 1 show that the establishment of a fluidised bed with regular particle distribution is possible for a variety of particles. Particles with maximum densities of 1.05 g/cm3 and between 3 and 5 mm diameter form a stable fluidised bed almost regardless of activated sludge concentration, viscosity and reactor geometry. Particles with densities between 1.05 g/cm3 and 1.2 g/cm3 form a stable fluidised bed, if the velocity at the reactor bottom is sufficiently high. Activities within pilot plant 2 focused on plant optimisation and the development of an adequate particle retention system.

Solar-driven evaporation offers a sustainable solution for water purification, but efficiency losses due to heat dissipation and fouling limit its scalability. Herein, we present a bilayer-structured solar evaporator ( SDWE ) with dynamic fluidic flow mechanism, designed to ensure a thin water supply and self-cleaning capability. The porous polydopamine ( PDA ) layer on a nickel skeleton provides photothermal functionality and water microchannels, while the thermo-responsive sporopollenin layer on the bottom acts as a switchable water gate. Using confocal laser microscopy and micro-CT, we demonstrate that this unique structure ensures a steady supply of thin water layers, enhancing evaporation by minimizing latent heat at high temperatures. Additionally, the system initiates a self-cleaning process through bulk water convection when temperature drops due to salt accumulation, thus maintaining increased evaporation efficiency. Therefore, the optimized p-SDWE sample achieved a high evaporation rate of 3.58 kg m −2 h −1 using 93.9% solar energy from 1 sun irradiation, and produces 18–22 liters of purified water per square meter of SDWE per day from brine water. This dynamic water transport mechanism surpasses traditional day-night cycles, offering inherent thermal adaptability for continuous, high-efficiency evaporation. Solar-driven evaporation is a sustainable water purification method, but scalability is hindered by heat loss and salt fouling. The authors introduce a bilayer-structured solar evaporator with a dynamic fluid flow mechanism, which enables self-cleaning. This approach offers continuous, high-efficiency evaporation.

Photocatalytic air purification is a promising technology that mimics nature’s photochemical process, but its practical applications are still limited despite considerable research efforts in recent decades. Here, we briefly discuss the progress and challenges associated with this technology.

The gas-phase reaction networks are the backbone of astrochemical models. However, due to their complexity and nonlinear impact on the astrochemical modeling, they can be the first source of error in the simulations if incorrect reactions are present. Over time, following the increasing number of species detected, astrochemists have added new reactions, based on laboratory experiments and quantum mechanics (QM) computations, as well as reactions inferred by chemical intuition and the similarity principle. However, sometimes no verification of their feasibility in the interstellar conditions, namely their exothermicity, was performed. In this work, we present a new gas-phase reaction network, GRETOBAPE, based on the KIDA2014 network and updated with several reactions, cleaned from endothermic reactions not explicitly recognized as such. To this end, we characterized all the species in the GRETOBAPE network with accurate QM calculations. We found that ∼5% of the reactions in the original network are endothermic, although most of them are reported as barrierless. The reaction network of Si-bearing species is the most impacted by the endothermicity cleaning process. We also produced a cleaned reduced network, GRETOBAPE-red, to be used to simulate astrochemical situations where only C-, O-, N-, and S-bearing species with less than six atoms are needed. Finally, the new GRETOBAPE network, its reduced version, and the database with all the molecular properties are made publicly available. The species property database can be used in the future to test the feasibility of possibly new reactions.

ObjectiveExposure-oriented questionnaire modules provide crucial task information for occupational exposure assessment. We evaluated the effectiveness of an algorithm for automatically assigning modules during interviews in a hospital-based case-control study in Asia.MethodsWe used a rule-based expert system based on a keyword search of occupational history responses and screening questions pertaining to paints/stains, solvents/glues/degreasers, and engineered woods to assign jobs to one of 23 modules. If no module was identified, we assigned a Work Location module that redirected some participants to more detailed modules. Post interview, each job was coded to standardized occupation and industry classifications. For each job and industry code we determined the ‘ideal’ module. We reviewed each ‘ideal’ vs. ‘assigned’ module combination and characterized potential information loss: none, task loss, and industry loss. We evaluated the screening questions’ positive and negative predictive value (PPV, NPV) compared to task responses for jobs assigned the Solvent module. For those redirected from the Work Location to the Solvent module, we calculated the task prevalence.ResultsThe algorithm assignment for 26,608 jobs was based on keywords for 55.4% and screening questions for 8.6%; the remainder received the work location module. Potential information loss was identified for 8.8% of jobs (7.2% task loss; 1.6% industry loss). For the 5,847 jobs completing the Solvent module, the overall PPV and NPV of the screening questions was 82.4% and 70.0%, respectively, with higher NPVs for engineered woods and paints/stains than for solvents/glues/degreasers. The Work Location re-directed 663 jobs to the Solvent module; the most frequently-reported activities for these jobs were cleaning hands with solvents (14%), paint bystander (8.0%), cleaning/degreasing bystander (5.1%) and glues/adhesives bystander (5.0%).ConclusionsOverall, our automated approach resulted in excellent capture of tasks of interest. Jobs identified with potential information loss will be prioritized for additional review during exposure assessment efforts.

The authors propose a technological scheme for cleaning dried sultanas as well as technical means for purification. The basic parameters of working elements and movement of bunches and clumps of dried grapes inside a drum and regularities of their degree of purification are theoretically substantiated.

Objectives: Energy devices (EDs), such as Harmonic, Ligasure, Thunderbeat and Trocar are widely used in Minimally Invasive Surgeries. They are expensive and designed for single use. However, due to the limitation of resources, they have been reused in some cases. Therefore, we aimed to assess the efficacy of EDs reprocessing by Adenosine Triphosphate (ATP) method. Methods: This was a cross-sectional description study. After first clinical using, EDs were taken to cleaning. Every ED was cleaned three times. Efficacy cleaning was assessed after each cleaning procedure by ATP method. ATP <200 RLUs (Relative Light Units) was benchmark as efficient cleaning process. Results: A total of 611 EDs were studied, including 269 of Harmonic, Ligasure, Thunderbeat, 298 of Trocar, and 44 other types. Detachable devices accounted for about 32.7%. Overall, after three consecutive cleanings, the median ATP values were decreased dramatically (957 RLUs, 160 RLUs, and 62 RLUs, respectively). This was a significant reduction in ATP levels between three stages (p < 0.05). There were 63.5%, 84.3%, and 92.8% EDs that had ATP < 200 RLUs after first, second, third cleaning respectively. Approximately 90% of EDs were still functional after three cleaning times. Nondetachable items were to be more difficult to clean than detachable ones (p = 0.0003, OR 1.3 [1.1 – 1.5]). Conclusions: Our data suggest that monitoring efficacy cleaning of surgical instruments in general and single-use energy devices in particular with ATP can identify a number of different influence factors, like the instrument condition, reprocessing procedure, or especially their structure. ATP measurement seems to be a valid technique that allows an immediate repeat of the manual cleaning if the results exceed the established cutoff of 200 RLUs.

Background Predicting protein-ligand binding sites is a fundamental step in understanding the functional characteristics of proteins, which plays a vital role in elucidating different biological functions and is a crucial step in drug discovery. A protein exhibits its true nature after binding to its interacting molecule known as a ligand that binds only in the favorable binding site of the protein structure. Different computational methods exploiting the features of proteins have been developed to identify the binding sites in the protein structure, but none seems to provide promising results, and therefore, further investigation is required. Results In this study, we present a deep learning model PUResNet and a novel data cleaning process based on structural similarity for predicting protein-ligand binding sites. From the whole scPDB (an annotated database of druggable binding sites extracted from the Protein DataBank) database, 5020 protein structures were selected to address this problem, which were used to train PUResNet. With this, we achieved better and justifiable performance than the existing methods while evaluating two independent sets using distance, volume and proportion metrics.