Application Of Processes Research Articles

Plasma-Enhanced Atomic Layer Deposition of TiN Thin Films Using Ultralow Electron Temperature Plasma.

Ultralow electron temperature (ULET, Te < 0.5 eV) plasmas are applied to atomic layer deposition (ALD) for damage-free plasma processing. The effects of ULET plasma on the ALD of titanium nitride (TiN) thin films are investigated. The ULET plasma-enhanced ALD (ULET-PEALD) reduces TiN surface roughness by 60%, from 0.78 to 0.3 nm, compared to remote PEALD, and decreases resistivity from 430 μΩ·cm to 325 μΩ·cm, while improving crystallinity. Unlike remote plasma sources, ULET plasma causes less surface damage due to its lower ion energy, enabling low-energy ion surface treatment. The generation mechanism of ULET plasma and the ULET-PEALD process are explained in detail, along with an analysis of the deposited films. ULET plasmas offer great potential for applications in processes that were previously limited by concerns over plasma-induced damage.

An atomically economical and environmentally benign approach for the scalable synthesis of rare‐earth metal‐organic framework catalysts

AbstractMetal–organic frameworks (MOFs), as an emerging class of porous materials, demonstrated substantial potential for applications in chemical engineering processes. However, their production often generates significant waste including ionic residues and organic solvents, posing considerable environmental concerns. Herein, we report the environmentally benign MOF synthesis with metal oxides precursors, yielding water as the exclusive by‐product. This approach not only enhances atom economy but also promotes solvent recycling by preventing anion accumulation, significantly reducing waste generation. The synthesis can be easily scaled up in a 10‐L reactor with an impressive space–time yield of 608 kg·m−3·day−1. Furthermore, MOF‐76(Y) was implemented in a reactor for catalytic cycloaddition reaction at kg‐scale. Through optimization of the interfacial contact between CO2 and the reaction liquid phase, a yield of 96.7% was achieved under mild conditions. This work demonstrates a rare example of scalable and sustainable synthesis of MOF materials coupled with catalysis implementation at kilogram scale.

Tailoring CO2 Adsorption Configuration with Spatial Confinement Switches Electroreduction Product from Formate to Acetate.

Multi-proton-coupled electron transfer, multitudinous intermediates, and unavoidable competing hydrogen evolution reaction during CO2 electroreduction make it tricky to control high selectivity for specific products. Here, we present spatial confinement of Fe single atoms (FeN2S2) by adjacent FeS clusters (Fe4S4) to orientate the transition of CO2 adsorption configuration from C,O-side to O-end, which triggers a shift of activated CO2 from first-step protonation to C-C coupling, thus switching the target product from HCOOH in high Faraday efficiency (FE: 90.6%) on FeN2S2 to CH3COOH (FE: 82.3%) on Fe4S4/FeN2S2. The adsorption strength of *OCHO upon the solitary FeN2S2 site is linearly related to the coordination number of Fe-S, with HCOOH predominantly produced over single-atom FeN2S2 (ortho-substituted S atoms). Fe4S4 cluster functions as a switch for a specific reduction product, which can not only optimize the spatial and electronic structure of the neighboring FeN2S2 but also impel complete reduction of CO2 to the hydrocarbon intermediate *CH3, followed by coupling of CO2* and *CH3 via the single-atom cluster synergistic catalysis of Fe4S4/FeN2S2. This spatial confinement strategy provides a new avenue to modulate the reactant adsorption model for desirable reaction pathways, with potential applications in diverse multistep electrochemical processes of controlled selectivity.

Multiomic Analysis Provided Insights into the Responses of Carbon Sources by Wood-Rotting Fungi Daldinia carpinicola

Daldinia carpinicola is a newly identified species of wood-rotting fungi, with substantial aspects of its biology and ecological function yet to be clarified. A Nanopore third-generation sequencer was employed for de novo genome assembly to examine the genetic characteristics. The genome consisted of 35.93 Mb in 46 contigs with a scaffold N50 of 4.384 Mb. Glycoside hydrolases and activities enzymes accounted for a large proportion of the 522 identified carbohydrate-active enzymes (CAZymes), suggesting a strong wood degradation ability. Phylogenetic and comparative analysis revealed a close evolutionary relationship between D. carpinicola and D. bambusicola. D. carpinicola and Hypoxylon fragiforme exhibited significant collinear inter-species genome alignment. Based on transcriptome and metabolomic analyses, D. carpinicola showed a greater ability to utilize sucrose over sawdust as a carbon source, enhancing its growth by activating glycolysis/gluconeogenesis and the citrate cycle. However, compared with sucrose, sawdust as a carbon source activated D. carpinicola amino acid biosynthesis and the production of various secondary metabolites, including diterpenoid, indole alkaloid, folate, porphyrin, and biotin metabolism. The study establishes a theoretical basis for research and applications in biological processes, demonstrating a strategy to modulate the production of secondary metabolites by altering its carbon sources in D. carpinicola.

Reinforcement learning versus data-driven dynamic programming: a comparison for finite horizon dynamic pricing markets

Abstract Revenue management (RM) plays a vital role to optimize sales processes in real-life applications under incomplete information. The prediction of consumer demand and the anticipation of price reactions of competitors became key factors in RM to be able to apply classical dynamic programming (DP) methods for expected long-term reward maximization. Modern model-free deep Reinforcement Learning (RL) approaches are able to derive optimized policies without explicit estimations of underlying model dynamics. However, RL algorithms typically require either vast amounts of training data or a suitable synthetic model to be trained on. As existing studies focus on one group of algorithms only, the relation between established DP approaches and new RL techniques is opaque. To address this issue, in this paper, we use a dynamic pricing framework for an airline ticket market to compare state-of-the-art RL algorithms and data-driven versions of classic DP methods regarding (i) performance and (ii) required data to each other. For the DP techniques, we use estimations of market dynamics to be able to compare their performance and data consumption against RL methods. The numerical results of our experiments, which include monopoly as well as duopoly markets, allow to study how the different approaches’ performances relate to each other in exemplary settings. In both setups, we find that with few data (about 10 episodes) fitted DP methods were highly competitive; with medium amounts of data (about 100 episodes) DP methods got outperformed by RL, where PPO provided the best results. Given large amounts of training data (about 1000 episodes), the best RL algorithms, i.e., TD3, DDPG, PPO, and SAC, performed similarly achieving about 90% and more of the optimal solution.

Thermoanalytical Investigation of the Curing Kinetics of Sodium Silicate as an Inorganic Binder for 3D Sand Printing

This study investigates the development and application of climate friendly processes in the foundry industry, particularly with regard to the use of inorganic binders to reduce emissions and pollution. An inorganic binder system based on water glass, which is used in 3D printing technology for the production of sand molds and core, is being tested and the possibility of determining a kinetic model for the curing kinetics of sodium silicate as an inorganic binder is investigated. The aim is to use a kinetic model to better describe the microwave process currently required in binder jetting for drying the binder and catalyzing the chemical reaction of the binder during curing. For sodium silicate in particular, there is still no scientific knowledge available in this respect, which is why basic investigations based on thermogravimetry or heat flow difference calorimetry must first be carried out. In this way, it should be possible to simulate the drying process in the microwave, which has so far been based on empirical values, in order to maximize the efficiency of this process and also the quality of the components. The results indicate that the weight loss and weight changes depend on the heating rates and that a heating rate of 30 K/min is not sufficient to fully cure the sample at 500 °C. The thermogravimetric analysis (TGA) shows that the fastest weight loss occurs at the beginning of the measurement, indicating a partial pre-curing of the sample before the measurement. From the measurements, an average activation energy of 144.18 kJ/mol could be determined using the Friedman method and 123.36 kJ/mol and 123.31 kJ/mol using the Ozawa–Flynn–Wall and Kissinger–Akahira–Sunose methods, respectively. Measurements of the heat flow at a heating rate of 30 K/min indicate partially exothermic reactions during the curing process.

Influence of the Carboxylates in the 3D Pb(II) Based Catalysts on the Electrochemical Oxygen Evolution Reaction.

The Oxygen Evolution Reaction (OER) is considered to be one of the important processes for energy storage and conversion applications. However, due to its sluggish kinetics it becomes extremely crucial to design and develop cost-effective, stable and highly efficient electrocatalysts. Herein, we report two lead(II)based coordination polymers (CPs) {[Pb2(TPBN)(TDC)2].2H2O}n (CP1), and {[Pb2(TPBN)(FDC)2]}n (CP2) synthesized via self-assembly at ambient conditions in good yields (where TPBN= N,N',N‴,N‴'-tetrakis-(2-pyridylmethyl)-1,4-diaminobutane, H2TDC= 2,5-2,5-thiophenedicarboxylic acid, and H2FDC = 2,5-furandicarboxylic acid. Their 3D structures were determined by Single-crystal X-ray diffraction (SCXRD) studies. The bulk purity and thermal stability of CP1 and CP2 were studied by powder X-ray diffraction studies and thermogravimetric analysis, respectively. Both the CPs showed remarkable catalytic activity for the OER. With a current density of 15 mAcm-2, for both CP1 and CP2, overpotentials of 570 mV and 630 mV with tafel slope values of 112 mV dec-1, and 178 mV dec-1 respectively for CP1 and CP2 in 1.0 M alkaline (KOH) solution was obtained. It is notable that the difference in their catalytic performance - CP1 is better than CP2, can be attributed to the subtle single heteroatom in the dicarboxylate ring (S in thiophene and O in furan, respectively).

A machine learning driven computationally efficient horse shoe shaped antenna design for internet of medical things.

With bio-medical wearables becoming an essential part of Internet of Medical things (IoMT) for monitoring the health of workers, patients and others in different environments, antenna play a pivotal role in such wearables. In this communication, a novel Horse shoe shaped antenna (HSPA) meant for such wearables is presented. The vitals of the workers, patients etc. are collected and sent to the IoMT platform for ensuring their safety and monitoring their physical wellbeing. In this article, regression-based Machine learning (ML) techniques are used to facilitate the design of Horse shoe shaped patch antenna to predict the frequency of operation, radiation efficiency and Specific Absorption Rate (SAR) values to accelerate its design process for on-body applications. The HSPA designed resonates at 2.45 GHz in the frequency band of 1.75-2.98 GHz with SAR of 1.89 W/kg for an input power of 16.98 dBm, peak gain of 1.91 dBi and radiation efficiency of 62.07% when mounted on the human body. 1080 samples of data comprising of three EM parameters have been generated using a conventional EM tool by varying the physical and electrical parameters of the design. A detailed comparison of the five regression-based ML algorithms is presented, and it is observed that the ML models help in efficient use of resources while designing an antenna for bio-medical applications.

Compositional analysis of baru (Dipteryx alata Vogel) pulp highlighting its industrial potential.

Baru (Dipteryx alata Vogel), a fruit native to the Brazilian Cerrado, has gained scientific interest due to its nutritional potential and commercial value. Its edible seed, of high commercial value, represents around 5% of the fruit. On the other hand, its pulp, a byproduct of the baru processing industry, is normally discarded, generating a huge volume of waste with reported antioxidant properties. This study investigates the composition and bioactive properties of baru pulp, aiming to identify the antioxidant components in this byproduct. Our analysis revealed that baru pulp is rich in sugars (41.86%), fiber (29.12%), and essential minerals, along with commercially valuable bioactive compounds such as trigonelline (139.10mg 100g-1), tannins (429.16mg tannin equivalent 100g-1), vitamin C (109.57mg 100g-1), and phenolic compounds such as trans-cinnamic acid (5.91mg 100g-1), chlorogenic acid, and gallic acid (1.07mg 100g-1). Industrially relevant sesquiterpenes, such as α-copaene and bicyclogermacrene, account for 42.75% of the volatile profile, alongside germacrene D (11.69%), aromadendrene (9.05%), α-cubebene (6.84%), β-elemene (5.90%), and ledene (5.82%), which are commonly used in essential oil production from other food matrices. While further studies are required to optimize extraction methods, these findings highlight baru pulp as a promising and low-cost alternative to traditional sources of bioactive compounds, with potential applications in functional food formulations and sustainable industrial processes. Specifically, the use of in natura pulp or its flour for food enrichment is recommended, supporting sustainability through the valorization of agro-industrial waste.

Confinement and wettability-driven dispersed phase hydrodynamics in cross-flow jets at low velocity and density ratios

The evolution characteristics of a low-velocity dispersed phase into continuous shear flow have numerous applications across biomedical devices, chemical processes, water management in fuel cells, spray systems, film deposition, and atomizing devices. The flow characteristics arise from a complex interplay of wettability, hydrodynamics, and interfacial properties, which, when constrained by confined geometries such as those in fuel cells, present a fascinating multiphase-multiphysics problem. This study investigates the impact of the chemical signature of a confined geometry and the velocity ratio between the dispersed and continuous phases on the evolution of the dispersed phase. The footprint and shape of the generated droplet guide the pressure distribution, deformation, and subsequent cross-flow-induced stretching. By systematically analyzing the dynamic effects of capillarity, inertia, air-shear, gravity, viscosity, wettability, and confinement, we classify the fate of a liquid droplet within classical flow regimes: jetting, threading, and dripping. These distinct flow regimes are mapped using classical non-dimensional numbers, and a quasi-universal characteristic is obtained relative to velocity ratios. The findings of this research contribute to precise control and prediction of dispersed-phase hydrodynamics, which play a pivotal role in enhancing the efficiency of fuel cells, droplet generation devices, water harvesting systems, film deposition techniques, coatings, and point-of-care diagnostic devices. The work underscores the relevance of integrating experimental and computational insights for optimizing interface-driven processes in interdisciplinary applications.

ECLStat: A robust machine learning based visual imaging tool for electrochemiluminescence biosensing.

Visual electrochemiluminescence (ECL) has emerged as a prominent diagnostic method for accurately quantifying various disease markers even at point of care setting with high sensitivity and accuracy. It does not employ complicated instruments such as potentiostat and expensive imaging microscopy for quantifying trace amounts of molecules. The ECL system offers significant advantages over other detection processes, such as high sensitivity, selectivity, rapid response, multiplexing, and miniaturization capabilities, making it well-suited for future commercialization. However, the current ECL system lacks standardization and accuracy in the resulting output data due to the manual measurement of ECL signal response using open-source image processing software, which often limits the efficiency of the ECL process in real-time applications. To address the shortcomings of the existing approach and advance the ECL detection process, a fully automated machine learning-assisted standalone graphical user interface (GUI) application was developed for dedicated measurement and management of ECL-emitted light signals. The working performance of the developed program is evaluated for its real-time utility by detecting hydrogen peroxide, which is an important reactive oxygen species, and glucose, which is a significant biomarker of diabetes. The obtained results show the detection limit of 0.024mM and 0.035mM for H2O2 and glucose, with a quantification limit of 0.074mM and 0.10mM, respectively. The ultimate objective of the developed application is to improve accuracy by enabling users to apply machine learning algorithms to raw image data seamlessly without deeply comprehending the underlying computational processes and establish a standard protocol for ECL signal measurements. Moreover, the developed application can be used in other optical detection approaches such as chemiluminescence, colorimetric, and fluorescence.

Multipurpose β-Cyclodextrin-Involved nanospherical Aggregates: Self-Assembly Process, foliar Adhesion, and promising applications for kiwifruit and rice Protections

Multipurpose β-Cyclodextrin-Involved nanospherical Aggregates: Self-Assembly Process, foliar Adhesion, and promising applications for kiwifruit and rice Protections

Data security strategies to avoid data breaches in modern information systems

Introduction: Cyber threats are constantly increasing in severity and targeting organizations in all industries, and result in loss of valuable data and a decline in stakeholders’ confidence. Advanced information systems positively impact overall organizational operations and functionality but bring new opportunities that adversaries seek. This research article seeks to look at how organizations can enhance data protection and prevent breaches in modern technology landscapes. Materials and Methods: An initial search and screener was done on selected journal article from the year 2015-2024 that was peer reviewed, industry report, and case study. The conceptual framework combined the theoretical approach to data breaches, malware attacks, and cybercriminal motives with factors, such as users’ perceptions, legal requirements. Data from 22 original papers were subject to a qualitative content analysis approach, and thematic synthesis was used to determine common patterns in security practices, threats, and risk management mechanisms. Results: The analysis revealed several key themes: 1) The major importance of security awareness training as the key to strengthening the human factor which is the weakest link in the context of data protection. 2) The validity of the layer defense principle that entailed a combination of technical measures and sound policies and procedures. 3) The role of encryption, access controls, and data minimization to protect information and data that is more important today than ever before. 4) Threat Intelligence and incident response planning to detect the breaches in advance before the threat actors begin executing the attack. 5) The role of AI and machine learning as threats and as the possibility to identify and prevent threats while using their opportunities for data analysis in cybersecurity. Discussion: The results imply that the protection of information should be an organizational, risk management approach with current and future threats in mind. However, while technical measures must be adopted, so too must the emphasis be placed on promotion of a security-aware culture as well as the adherence to regulatory requirements. The research indicates areas for future development, with specific emphasis on the issues of insider threats and protection of information in cloud and IoT contexts. Drawbacks include the fact that technological advancements are rather fast, which makes it difficult for academic publications to keep up with them. Additionally, there could be situations when certain industries fail to report breaches. Conclusion: Preventing data breaches in today’s information systems, therefore, cannot be addressed through a single solution that involves merely the application of people, processes or technology, but rather through a holistic approach oriented towards the integration of the three aspects. Organizations need to be creative about the issue and adopt emerging technologies that address existing and fundamental security risks. Further research should focus on the effects of AI-based threats calculate the return of security investments, and establish guidelines for data protection specific to industries.

Development of Bio-Composites from Milkweed Fibers Using Air-Laid Spike Process for Automobile Dashboard Applications

This study focused on examining the reinforcement of milkweed fibers in polylactic acid (PLA) bio-composites used for dashboards in car interiors. Milkweed fiber is a natural fiber with a hollow structure that provides tremendous thermal insulation and noise resistance properties. Firstly, the milkweed fibers were blended with PLA fibers in a weight ratio of 75:25 using an air-laying process. Then, several layers of nonwoven material were compressed in a hydraulic press to obtain bio-composites. Finally, three bio-composites were obtained with different numbers of layers. The density, microstructure, thermal conductivity, sound transmission loss (STL), mechanical properties, dynamic mechanical analysis (DMA), and contact angles of the bio-composites were evaluated. The microstructure analysis revealed that some milkweed fibers collapsed due to the high-pressure molding process, which does not affect the bio-composite properties. The bio-composite with a higher number of nonwoven layers presented a poor interface between PLA and milkweed fibers, thus making it less homogeneous. This bio-composite showed a decrease of 5% in thermal conductivity values and a 19% increase in STL values. In addition, it exhibited a 160% increase in specific flexural strength and a 335% increase in specific flexural modulus compared to samples with a lower number of nonwoven layers. Therefore, it offers the best mechanical-property-to-density ratio, with values that conform to the specifications required for automotive dashboards.

Recent Progress in Advanced Catalytic Strategies for C─F Bond Cleavage in Waste Refrigerants: A Review

AbstractThe degradation of fluorinated refrigerants, known for their highly stable carbon‐fluorine (C─F) bonds, poses significant environmental and technical challenges. This review addresses these challenges by analyzing two core degradation mechanisms: molecular polarization (MP) and free radical attack (FRA), and exploring their applications in thermal catalytic and photocatalytic processes. MP redistributes electron density to weaken C─F bonds, facilitating adsorption and bond cleavage, while radical attack involves reactive species that directly break chemical bonds. However, both mechanisms have limitations: MP alone may lack the kinetic drive for dissociation, and radical‐based methods often suffer from low selectivity, short radical lifetimes, and the formation of toxic intermediates. The section on thermal catalytic degradation discusses how elevated temperatures enhance bond cleavage through MP, addressing adsorption challenges and accelerating dissociation. The part on photocatalytic degradation focuses on the role of light‐activated processes in generating reactive radicals and facilitating bond breaking, with an emphasis on visible and ultraviolet light‐driven reactions. The review concludes by exploring the potential of hybrid catalytic systems that combine thermal and photocatalytic processes, providing insights into the complementary use of these mechanisms for the degradation of persistent fluorinated compounds.

ZSM-5 Zeolite Synthesis from Coal Fly Ash Synthesised Silica: Sole Silica & Alumina Source.

This study explores the synthesis of ZSM-5 zeolite using high-purity mesoporous silica exclusively derived from coal fly ash (CFA), eliminating the need for additional silica or alumina sources. Traditional ZSM-5 synthesis relies on costly and environmentally harmful pure chemicals, whereas this approach utilizes CFA, an industrial byproduct, addressing both cost and sustainability concerns. The synthesized ZSM-5 zeolite demonstrates exceptional purity, with a surface area of 455.24 m2/g, and exhibits unique structural properties, confirmed through XRD, SEM, TEM, FTIR, TGA, and BET analyses. This method highlights the potential of CFA-derived silica as a sustainable feedstock for zeolite production, promoting both environmental sustainability and cost-effective industrial applications in catalysis, adsorption, and separation processes.

In-situ quality monitoring during embedded bioprinting using integrated microscopy and classical computer vision

Despite significant advances in bioprinting technology, current hardware platforms lack the capability for process monitoring and quality control. This limitation hampers the translation of the technology into industrial GMP-compliant manufacturing settings. As a key step towards a solution, we developed a novel bioprinting platform integrating a high-resolution camera forin-situmonitoring of extrusion outcomes during embedded bioprinting. Leveraging classical computer vision and image analysis techniques, we then created a custom software module for assessing print quality. This module enables quantitative comparison of printer outputs to input points of the computer-aided design model's 2D projections, measuring area and positional accuracy. To showcase the platform's capabilities, we then investigated compatibility with various bioinks, dyes, and support bath materials for both 2D and 3D print path trajectories. In addition, we performed a detailed study on how the rheological properties of granular support hydrogels impact print quality during embedded bioprinting, illustrating a practical application of the platform. Our results demonstrated that lower viscosity, faster thixotropy recovery, and smaller particle sizes significantly enhance print fidelity. This novel bioprinting platform, equipped with integrated process monitoring, holds great potential for establishing auditable and more reproducible biofabrication processes for industrial applications.

Localized Nanopore Fabrication in Silicon Nitride Membranes by Femtosecond Laser Exposure and Subsequent Controlled Breakdown.

Controlled breakdown has emerged as an effective method for fabricating solid-state nanopores in thin suspended dielectric membranes for various biomolecular sensing applications. On an unpatterned membrane, the site of nanopore formation by controlled breakdown is random. Nanopore formation on a specific site on the membrane has previously been realized using local thinning of the membrane by lithographic processes or laser-assisted photothermal etching under immersion in an aqueous salt solution. However, these approaches require elaborate and expensive cleanroom-based lithography processes or involve intricate procedures using custom-made equipment. Here, we present a rapid cleanroom-free approach using single pulse femtosecond laser exposures of 50 nm thick silicon nitride membranes in air to localize the site of nanopore formation by subsequent controlled breakdown to an area less than 500 nm in diameter on the membrane. The precise positioning of the nanopores on the membrane could be produced both using laser exposure powers which caused significant thinning of the silicon nitride membrane (up to 60% of the original thickness locally), as well as at laser powers which caused no visible modification of the membrane at all. We show that nanopores made using our approach can work as single-molecule sensors by performing dsDNA translocation experiments. Due to the applicability of femtosecond laser processing to a wide range of membrane materials, we expect our approach to simplify the fabrication of localized nanopores by controlled breakdown in a variety of thin film material stacks, thereby enabling more sophisticated nanopore sensors.

Effect of Minor Reinforcement with Ultrafine Industrial Microsilica Particles and T6 Heat Treatment on Mechanical Properties of Aluminum Matrix Composites

This study examines the use of ultrafine (~128 nm) microsilica (composed of a mixture of amorphous and microcrystalline silicon dioxide phases) particles, an industrial waste product, as a reinforcing material to create aluminum matrix composites (AMCs) via ultrasonic-assisted stir casting followed by T6 heat treatment. This study aimed to improve the mechanical properties of pure aluminum, which has insufficient strength for most engineering applications. The main objective of this study is to develop environmentally and economically efficient AMCs with improved properties, namely, the balance between strength and ductility, for further application in caliber rolling processes. Attention is also paid to minor reinforcements using a low concentration of microsilica (~0.36%wt), which minimizes the problems with the wettability of the reinforcing material particles. The composites reinforced with ultrafine microsilica exhibited enhanced mechanical performance, including a 59.7% increase in Vickers microhardness and a significant improvement in tensile strength, reaching 73 MPa. Additionally, T6 heat treatment synergistically improved ductility to 60.3% elongation while maintaining high strength, achieving a balanced performance suitable for forming processes. The study results confirm that using microsilica as a reinforcing material is an effective way to improve the performance of aluminum alloys, while minimizing costs and solving environmental problems.

Piezoelectric-Driven Fenton System Based on Bismuth Ferrite Nanosheets for Removal of N-acetyl-para-aminophenol in Aqueous Environments

Emerging pollutants, such as N-acetyl-para-aminophenol, pose significant challenges to environmental sustainability, and Bi2Fe2O2 (BFO) nanomaterials are an emerging class of piezoelectric materials. This study presents a novel piezoelectric-driven Fenton system based on Bi2Fe4O9 nanosheets for the efficient degradation of organic pollutants. BFO nanosheets with varying thicknesses were synthesized, and their piezoelectric properties were confirmed through piezoresponse force microscopy and heavy metal ion reduction experiments. The piezoelectric electrons generated within the BFO nanosheets facilitate the in situ production of hydrogen peroxide, which in turn drives the Fenton-like reaction. Furthermore, the piezoelectric electrons enhance the redox cycling of iron in the Fenton process, boosting the overall catalytic efficiency. The energy band structure of BFO nanosheets is well-suited for this process, enabling efficient hydrogen peroxide generation and promoting Fe³⁺ reduction. The findings demonstrate that thinner BFO nanosheets exhibit superior piezoelectric activity, leading to enhanced catalytic performance. Additionally, the incorporation of gold nanodots onto BFO nanosheets further boosts their piezocatalytic efficiency, particularly in the reduction of Cr (VI). The system exhibited robust oxidative capacity, stability, and recyclability, with reactive oxygen species (ROS) verified via electron paramagnetic resonance spectroscopy. Overall, BFO nanosheets, with their optimal energy band structure, self-supplied hydrogen peroxide, and enhanced Fe³⁺ reduction, represent a promising, sustainable solution for advanced oxidation processes in wastewater treatment and other applications.