Method For Removal Research Articles

Construction of SnO2/Cu2O heterojunctions for enhanced photocatalytic degradation of moxifloxacin

Herein, SnO2/Cu2O heterojunction composites were prepared using a hydrothermal method for moxifloxacin removal from water. Cu2O and SnO2 were detected in the composite samples, which exhibited between the two semiconductors. The introduction of SnO2 into pure Cu2O, forming a heterojunction structure, can enhance device charge conversion, improve photocurrent intensity and photogenerated electron-hole separation efficiency, and greatly improve photocatalytic performance. Moxifloxacin degradation by SnO2/Cu2O composites reached 86.4 %. The proposed catalytic mechanism was based on radical scavenging, with • O2‐ identified as the main active species. In addition, the moxifloxacin degradation intermediates were analyzed, with possible degradation pathways suggested.

Microbial degradation of contaminants of emerging concern: metabolic, genetic and omics insights for enhanced bioremediation.

The perpetual release of natural/synthetic pollutants into the environment poses major risks to ecological balance and human health. Amongst these, contaminants of emerging concern (CECs) are characterized by their recent introduction/detection in various niches, thereby causing significant hazards and necessitating their removal. Pharmaceuticals, plasticizers, cyanotoxins and emerging pesticides are major groups of CECs that are highly toxic and found to occur in various compartments of the biosphere. The sources of these compounds can be multipartite including industrial discharge, improper disposal, excretion of unmetabolized residues, eutrophication etc., while their fate and persistence are determined by factors such as physico-chemical properties, environmental conditions, biodegradability and hydrological factors. The resultant exposure of these compounds to microbiota has imposed a selection pressure and resulted in evolution of metabolic pathways for their biotransformation and/or utilization as sole source of carbon and energy. Such microbial degradation phenotype can be exploited to clean-up CECs from the environment, offering a cost-effective and eco-friendly alternative to abiotic methods of removal, thereby mitigating their toxicity. However, efficient bioprocess development for bioremediation strategies requires extensive understanding of individual components such as pathway gene clusters, proteins/enzymes, metabolites and associated regulatory mechanisms. "Omics" and "Meta-omics" techniques aid in providing crucial insights into the complex interactions and functions of these components as well as microbial community, enabling more effective and targeted bioremediation. Aside from natural isolates, metabolic engineering approaches employ the application of genetic engineering to enhance metabolic diversity and degradation rates. The integration of omics data will further aid in developing systemic-level bioremediation and metabolic engineering strategies, thereby optimising the clean-up process. This review describes bacterial catabolic pathways, genetics, and application of omics and metabolic engineering for bioremediation of four major groups of CECs: pharmaceuticals, plasticizers, cyanotoxins, and emerging pesticides.

Central composite experimental design for Indigo Carmine dye removal from solutions by applying electro-coagulation and electro-oxidation processes

Textile and food industries produce toxic or harmfull dye-containing wastewaters that should be treated for safely discharge. The electro-coagulation (EC) and the electro-oxidation (EO) methods for removal of dyes from wastewaters are being investigated nowadays by researchers. In this study, the Indigo Carmine, a textile and food coloring agent, removal was investigated by applying the electro-coagulation and the electro-oxidation methods. For this purpose, aluminum electrodes for EC and graphite plates for EO were used. The central composite experimental design was applied as the optimization method for the EC and the EO processes. The optimization parameters were selected as time (10-30 minutes), current density (0.4-2 Ampere/500 mL) and concentration (50-250 mg/L), natural pH (5.78-6.90) and room temperature (20-25 °C). The EO process was determined to be effective than the EC process. Statistically important parameters were concentration and time-current density interaction for the EC, but all the parameters were statistically unimportant for the EO. Dye removal percentages by the EC were calculated between 82.75% and 98.38%, and dye removal percentages by the EO were calculated between 46.88% and 100% for the determined experimental matrix. Electrical consumptions were almost equal for the EO and the EO prosesses. A column ion exchange process (Selion SBA 2000 resin) was applied to the dye residue after the EO treatment. From the oxidation reduction measurements, the treated solutions were determined as dischargeable.

Algae Modified Alginate Beads for Improved Cd(II) Removal from Aqueous Solutions

The aim of this research was to synthesize alginate beads. The beads were modified with a mixture of three different species of algae. Both synthesized beads were evaluated for the efficiency of Cd(II) removal from aqueous solutions as one of the currently most sustainable metal removal methods. The focus was on the characterization of synthesized beads and their stability. The characterization was performed using Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). The specific surface area was determined. Cd(II) ion standard solutions were brought into contact with unmodified and modified beads. The experimental results showed that the most influential factors on biosorption are pH value and temperature. The maximum biosorption of Cd(II) ions was achieved at 181.0 mg/g. Kinetic and thermodynamic studies were carried out. The data obtained followed pseudo-second-order kinetics.

A three-stage framework for accurate detection of high-speed train body paint film defects

Detecting paint film defects on high-speed train (HST) body is a crucial step towards zero-defect manufacturing (ZDM). The effectiveness of defect detection in industrial environments, however, is significantly impacted by factors such as high reflection noise, weak defect features, small defect size, and various defect scales, resulting in unsatisfactory detection accuracy. To address these challenges, this paper proposes a technical framework for the accurate detection of paint film defects on HST body, encompassing three stages. A reflection detection removal method that takes into account the reflection transition region as well as the preservation of defect features is at first used to reduce reflection noise interference. Then a lightness-weighted adaptive image enhancement algorithm based on the fractional order differentiation is utilized to enhance the features of paint film defects while avoiding the introduction of reflection noise. Finally, an improved YOLOv5 algorithm, incorporating attention mechanism feature extraction and multi-scale feature fusion, is employed to detect and classify paint film defects. Experimental results demonstrate that the proposed framework effectively reduces the reflection in paint film images, and enhances the contrast between paint film defects and the background, with a mean average precision (mAP) for defect detection reaching up to 90.13%. This work serves as a valuable reference for the automated detection of defects arising in the painting process of HST body.

Tannin removal from Persian gum and investigation of physicochemical properties: Comparing different methods

Persian gum (PG) is a novel natural exudate gum and the main challenge regarding PG is tannin content of gum which limits its application in food and pharmaceutical industries. In fact, tannins are known as anti-nutrient compounds because they limit bioavailability of proteins and minerals. Hence, in this study, the effect of microwave radiation (180, 450, 600 W for 5 min), autoclaving (121 °C for 20 min), boiling (5, 10 and 15 min) and soaking either in water or saline solutions (mono- and divalent salts) was evaluated to remove tannins from Persian gum. Moreover, the effect of tannin removal on the physicochemical properties was investigated by evaluation of rheological properties (flow behavior and viscoelastic tests), zeta potential and Fourier transform infrared spectroscopy (FTIR) data. Our outcomes demonstrated that the highest tannin removal efficiency found to be 75.67 ± 0.25, 74.32 ± 0.54 and 67.08 ± 0.19 % which belonged to soaking in water (180 min), soaking in CaCl2 solution (1% w/v, 90 min) and boiling (15 min) treatments of brownish PG granules, respectively (p < 0.05). Interestingly, removal of tannin increased apparent viscosity of PG dispersion. Based on oscillatory tests, boiling treatment caused exhibition of solid-like behavior, in contrast to control sample. Tannin removal reduced zeta potential and the lowest reduction belonged to the gums soaked in water (from -32.3 to -28.7 mV); however, formation of ion-bridging in the presence of CaCl2 caused a remarkable decrease. FTIR analysis declared that in the boiled sample, a decrease was detected in vibration intensity within the range of 3200–3500 cm−1 (-OH groups) which can be due to heat degradation. Findings of the present research confirmed that soaking in water is a low-cost and practical method for tannin removal of PG, which almost had no negative effect on physiochemical properties of gum.

Artifact Management for Cerebral Near-Infrared Spectroscopy Signals: A Systematic Scoping Review.

Artifacts induced during patient monitoring are a main limitation for near-infrared spectroscopy (NIRS) as a non-invasive method of cerebral hemodynamic monitoring. There currently does not exist a robust "gold-standard" method for artifact management for these signals. The objective of this review is to comprehensively examine the literature on existing artifact management methods for cerebral NIRS signals recorded in animals and humans. A search of five databases was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. The search yielded 806 unique results. There were 19 articles from these results that were included in this review based on the inclusion/exclusion criteria. There were an additional 36 articles identified in the references of select articles that were also included. The methods outlined in these articles were grouped under two major categories: (1) motion and other disconnection artifact removal methods; (2) data quality improvement and physiological/other noise artifact filtering methods. These were sub-categorized by method type. It proved difficult to quantitatively compare the methods due to the heterogeneity of the effectiveness metrics and definitions of artifacts. The limitations evident in the existing literature justify the need for more comprehensive comparisons of artifact management. This review provides insights into the available methods for artifact management in cerebral NIRS and justification for a homogenous method to quantify the effectiveness of artifact management methods. This builds upon the work of two existing reviews that have been conducted on this topic; however, the scope is extended to all artifact types and all NIRS recording types. Future work by our lab in cerebral NIRS artifact management will lie in a layered artifact management method that will employ different techniques covered in this review (including dynamic thresholding, autoregressive-based methods, and wavelet-based methods) amongst others to remove varying artifact types.

Embedded U-shaped network with cross-hierarchical feature adaptation fusion for remote sensing image haze removal

ABSTRACT Recently, U-Net architecture has been extensively explored for remote sensing (RS) image haze removal, demonstrating remarkable performance. However, most existing RS image haze removal methods based on the U-Net fail to fully utilize feature information extracted in the encoding layers during decoding, resulting in unsatisfactory haze removal results. Moreover, most haze removal algorithms neglect the interaction of cross-level feature information, which is particularly important for scene recovery and context information constraints. To address these issues, this paper presents an Embedded U-shaped Network with Cross-Hierarchical Feature Adaptation Fusion Network (EUCHA). Specifically, an Embedded U-shaped Network Framework (EUNF) is proposed to fuse the features extracted from different scale encoding layers as supplementary information into the corresponding decoding layers by embedding multiple parallel U-shaped subnetworks, which alleviates feature information dilution during the decoding process and enhances the network receptive field. Furthermore, we propose a Cross-Hierarchical Feature Adaptive Fusion (CHAF) module, which effectively and adaptively fuses multiple adjacent hierarchical features extracted by channel attention and pixel attention through learnable factors to enhance the expressive ability of features. Experiments were conducted on both real and synthetic datasets and compared with the nine most advanced algorithms. The results showed that EUCHA algorithm performs better in removing artefacts in both RS images and natural images. The source code is available at https://github.com/GongHongY/EUCHA.

Alternative methods for caries removal: A narrative review

Dental caries, commonly known as tooth decay, remains a significant global oral health concern. Traditional methods of caries removal, such as mechanical drilling, have proven effective but often result in the removal of healthy tooth structure and may cause patient discomfort.As,, 9a result, alternative methods for caries removal have emerged, aiming to preserve tooth structure, minimize pain, and provide more conservative treatment options. This review article explores various alternative methods for caries removal, including mechanical methods, chemomechanical methods, ultrasonic methods, laser-based techniques, cryotherapy, `silver diamine fluoride, sealants and ozone based approaches.The article summarizes the scientific literature surrounding these methods, including their efficacy, advantages, limitations, and potential applications. Additionally, the article discusses the potential challenges and future directions in the field of alternative caries removal methods. By providing a comprehensive overview of these innovative approaches, this review article aims to contribute to the growing body of knowledge on minimally invasive dentistry and enhance the understanding of alternative techniques for caries removal.

An assessment of the autotrophic/heterotrophic synergism in microalgae under mixotrophic mode and its contribution in high-rate phosphate recovery from wastewater

Dual carbon metabolisms and the synergism contribute to improving nutrient recovery under mixotrophy. However, how synergism influences nutrient recovery has yet to be understood, which is revealed in the current study. Due to dual carbon metabolisms and synergism,the PO4−-P recovery rate under mixotrophy reached 0.34 mg L−1 h−1. Due to the internal cycling of respiratory CO2, the mutualistic index (MI) in terms of synergism helped Scenedesmus accumulate 27.49 % more biomass under mixotrophy than sum of the two controls. In contrast, MI contributed 0.26 g L−1 d−1 to the total modeled mixotrophic productivity of 1.15 g L−1 d−1. To total modeled PO4−-P recovery, mixotrophic-auto, and mixotrophic-hetero shares were 42 % and 58 %. The synergism under mixotrophy contributed 20 % in total PO4−-P recovery. The PO4−-P recovery rate under mixotrophywas comparable to other biological P removal methods. These findings emphasize the potential of synergism in improving productivityand promoting resource recovery for sustainable wastewater treatment.

Enhancing streamflow simulations with gated recurrent units deep learning models in the flood prone region with low-convergence streamflow data

Highly accurate streamflow simulations are essential for water resources and river management. However, there are longstanding challenges for streamflow modeling in the hydrology field. As a result, deep learning methods have been introduced as novel tools and are being used in simulation. This paper proposes a novel approach that utilizes unconventional data preprocessing techniques to improve the accuracy of daily streamflow predictions in flood-prone regions. We applied this approach to a flood-prone area in western Iran and simulated daily streamflow using a deep learning Gated Recurrent Unit (GRU) method under various data selection scenarios. These scenarios focused on identifying the optimal combination of input variables, time steps, and outlier removal techniques. The outlier removal methods investigated in this study include Mahalanobis distance, critical section removal, Z-score, and no removal. The average rainfall of the area, data driven precipitation, Normalized Difference Vegetation Index (NDVI), surface soil moisture, groundwater baseflows, and streamflow in the hydrometric station were evaluated using correlation control method, and inputs with the lowest correlation were removed. Based on the results obtained from the deep learning models produced in the research, it was found that the GRU model, with various modified inputs using Z-score removal, had the best performance. The model had an average of Root Mean Squared Error (RMSE) at 5.24 mm and R2 (Coefficient of Determination) at 0.91 during training, while during validation, it had an RMSE of 7.74 mm and R2 of 0.83. Considering using relatively low convergence data in streamflow simulation in this study, it can be said that the listed scenarios showed an appropriate result in dealing with the data and recognizing the complex pattern of the daily streamflow, and the future studies will show the improvement of the GRU models if higher convergence data will be used.

Z-scheme heterojunction of TiO2/NH2-MIL-125(Ti) growing on carbon fibers cloth: Photocatalytic degradation of tetracycline and toxicity analysis

Photocatalysis stands out as a promising and eco-friendly method for antibiotics removal, yet traditional photocatalysts, mainly in powder form, are often prone to detachment and loss, making them difficult to recycle and reuse. In this work, we address this challenge by using carbon fiber (CF) cloth with a large area as a carrier for catalysts. To improve the photocatalytic efficiency, we synthesized TiO2 nanorods on the surfaces of CF cloth and incorporate NH2-MIL-125 (Ti) on them, thus the Z-scheme heterojunctions CF/TiO2/NH2-MIL-125(Ti) was obtained. In order to fully understand the structural properties and performance advantages of CF/TiO2/NH₂-MIL-125 (Ti) composites, scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), X-ray photoelectron spectroscopy (XPS), UV–visible diffuse reflectance spectroscopy (DRS), photoluminescence spectroscopy (PL), Brunauer-Emmett-Teller (BET), Mott-Schottky (M-S) and valence band X-ray photoelectron spectroscopy (VB-XPS). Due to the special electronic transmission path of the Z-scheme heterojunctions, the CF/TiO2/NH2-MIL-125(Ti) composite achieve 95.30 % degradation of tetracycline (TC) within 210 min. The composite also shows exceptional reusability, with a degradation rate of 93.55 % maintained even after four cycles. Finally, we proposed a pathway and catalytic process for the decomposition of TC by CF/TiO2/NH2-MIL-125(Ti) and performed toxicity assessments of TC and its precursors using T.E.S.T. software. This novel photocatalyst composite offers a green and efficient approach for treating antibiotic wastewater, presenting a significant advancement in environmental remediation strategies.

Averaging fractional Fourier domains for background noise removal applied to digital lensless holographic microscopy

A background noise removal method based on averaging fractional Fourier domains is presented. The method is applied to Digital Lensless Holographic Microscopy (DLHM) intensity reconstructions, where the background is perturbed by the weak yet detrimental presence of information of the twin image. A set of fractional Fourier domains of a DLHM intensity reconstruction is computed and thereafter averaged leading to a sensible reduction of the background noise and, therefore, an increase in the overall contrast of the resulting image. The maximum reach of the fractional rotations used in the method is determined by measuring the spatial resolution in a regular star test target such that the spatial resolution is kept within the span of interest for a given application. The set of images to be averaged is composed of fractional rotations of the original intensity reconstruction that are smaller than the previously determined maximum reach. The number of fractional rotations that are finally averaged is determined by the sought increase in the contrast of the resulting image. Experimental samples of micrometer-sized objects and an intricate biological specimen have been used to validate the proposal.

Preparation of NH2-MIL-101(Fe) Metal Organic Framework and Its Performance in Adsorbing and Removing Tetracycline.

Tetracycline's accumulation in the environment poses threats to human health and the ecological balance, necessitating efficient and rapid removal methods. Novel porous metal-organic framework (MOF) materials have garnered significant attention in academia due to their distinctive characteristics. This paper focuses on studying the adsorption and removal performance of amino-modified MIL-101(Fe) materials towards tetracycline, along with their adsorption mechanisms. The main research objectives and conclusions are as follows: (1) NH2-MIL-101(Fe) MOF materials were successfully synthesized via the solvothermal method, confirmed through various characterization techniques including XRD, FT-IR, SEM, EDS, XPS, BET, and TGA. (2) NH2-MIL-101(Fe) exhibited a 40% enhancement in tetracycline adsorption performance compared to MIL-101(Fe), primarily through chemical adsorption following pseudo-second-order kinetics. The adsorption process conformed well to Freundlich isotherm models, indicating multilayer and heterogeneous adsorption characteristics. Thermodynamic analysis revealed the adsorption process as a spontaneous endothermic reaction. (3) An increased adsorbent dosage and temperature correspondingly improved NH2-MIL-101(Fe)'s adsorption efficiency, with optimal performance observed under neutral pH conditions. These findings provide new strategies for the effective removal of tetracycline from the environment, thus holding significant implications for environmental protection.

Innovative Adsorbents for Pollutant Removal: Exploring the Latest Research and Applications.

The growing presence of diverse pollutants, including heavy metals, organic compounds, pharmaceuticals, and emerging contaminants, poses significant environmental and health risks. Traditional methods for pollutant removal often face limitations in efficiency, selectivity, and sustainability. This review provides a comprehensive analysis of recent advancements in innovative adsorbents designed to address these challenges. It explores a wide array of non-conventional adsorbent materials, such as nanocellulose, metal-organic frameworks (MOFs), graphene-based composites, and biochar, emphasizing their sources, structural characteristics, and unique adsorption mechanisms. The review discusses adsorption processes, including the basic principles, kinetics, isotherms, and the factors influencing adsorption efficiency. It highlights the superior performance of these materials in removing specific pollutants across various environmental settings. The practical applications of these adsorbents are further explored through case studies in industrial settings, pilot studies, and field trials, showcasing their real-world effectiveness. Additionally, the review critically examines the economic considerations, technical challenges, and environmental impacts associated with these adsorbents, offering a balanced perspective on their viability and sustainability. The conclusion emphasizes future research directions, focusing on the development of scalable production methods, enhanced material stability, and sustainable regeneration techniques. This comprehensive assessment underscores the transformative potential of innovative adsorbents in pollutant remediation and their critical role in advancing environmental protection.

Efficient and fast arsenate removal from water by in-situ formed magnesium hydroxide

MgO nanoparticles have good As-adsorption capacity in treating As-contaminated wastewater but suffer from high production cost. In this study, instead of using pre-formed MgO nanoparticles, we found that in-situ formed Mg(OH)2 from MgCl2 and NaOH reaction exhibited super high arsenate (As(V)) removal efficiency. Only 1.5 mmol/L of in-situ formed Mg(OH)2 could remove more than 95% As(V) within 10 min to make the As contaminated water (10 mg-As(V)/L) meet the municipal wastewater treatment standard, whereas MgO nanoparticles failed. The Mg-As sludge has an amorphous crystal structure while no Mg(OH)2 phase could be observed. As(V) existed uniformly within the sludge which was confirmed by elemental mapping. A precipitation-adsorption-coagulation mechanism might exist, which could relieve the restriction of limited surface area of solid MgO adsorbents. This study not only reveals an applicable method for efficient removal of trace level As(V) from water but also implies the huge potential of in-situ formed adsorbents in water treatment.

The Use of Some Species of Bacteria and Algae in the Bioremediation of Pollution Caused by Hydrocarbons and Some Heavy Metals in Al Asfar Lake Water

Pollution is the biggest environmental and health threat in the world. Conventional treatments of polluted habitats require the removal of pollutants contaminating the environment, but removal methods are costly and involve high power consumption. This research aims to investigate the potential for bioremediation and proposes an alternative source for implementing it that is cheaper and more environmentally friendly. The phycobioremediation experiment used hydrocarbon- and heavy-metal-polluted water from Al Asfar Lake, AlAhsa, KSA. The isolation and characterization of the lake’s predominant microalgae and associated bacteria were carried out. Monoalgal cultures of the dominant genera of algae were employed for the treatment of contaminated water and soil samples. The concentrations of the heavy metals and hydrocarbons in these samples were determined before and after the treatments by using atomic absorption spectroscopy (for heavy metals) and gas chromatography (for hydrocarbons). From the initial assessments, the levels of manganese, copper, and chromium were high, with chromium being the highest. Three microalgal isolates were identified: two coccoid, with one being blue-green and the other green, and one filamentous cyanobacterium. These species were the most efficient in removing heavy metals and dangerous hydrocarbons. Molecular characterization revealed Chlorella sp. and Geitlarianema sp. to be the most promising for bioremediation. The present work sheds light on the prospect of using algal and bacterial consortia for optimized, safe, and eco-friendly pollution amelioration.

Sequential use of coagulation and adsorption methods for COD removal from soft drink industry wastewater

Sequential use of coagulation and adsorption methods for COD removal from soft drink industry wastewater

Magnetic porous functional material from coal mine waste for simultaneous removal of cadmium and arsenic in water and soil: Synergistic effect of iron-containing minerals and porous structure

Heavy metals such as cadmium and arsenic (Cd/As) can cause harmful effects on human health. Coal mine waste (coal gangue) is a solid waste residue without commercial values produced abundantly in the coalfield. Hence, treating Cd and As concurrently with reutilizing coal gangue (CG) is critical to ecologically sustainable growth. This work successfully synthesized the magnetic porous functional materials (MPCG) based on CG using a two-step method for the simultaneous removal of Cd and As in water and soil. First, MPCG exhibited an irregular network porous structure. Then, it consisted of iron (Fe), mainly in magnetite, hematite, and pyrite forms. Such properties also exhibited excellent pH adaptability and coadsorption capacity by removing up to 103.79 mg g−1 and 579.43 mg g−1 for Cd(II) and As(V), respectively. There is a synergistic effect between the iron-containing minerals and porous structure to enhance the adsorption capacity of MPCG for Cd(Ⅱ) and As(Ⅴ). Several primary potential adsorption mechanisms by MPCG for Cd(Ⅱ) and As(Ⅴ) were identified. The potential mechanisms of Cd(II) adsorption mainly include ion exchange, electrostatic attraction, surface complexation and precipitation/co-precipitation, and the potential mechanisms of As(V) adsorption mainly include electrostatic attraction, surface complexation and precipitation. In addition, the removal rates of MPCG for Cd and As in soil were as high as 44.78 and 38.44 %, respectively. In conclusion, MPCG has excellent potential as an efficient and environmentally compatible functional material, which provides a potential way to realize efficient heavy metal removal from our ecosystems.

Production of hydrochar from the hydrothermal carbonisation of food waste feedstock for use as an adsorbent in removal of heavy metals from water

AbstractIn this research, discarded butternut peels were converted into hydrochar products through hydrothermal carbonisation (HTC), with adjustments made to the temperature (ranging from 180 to 260℃) and residence time (spanning 45–180 min). The findings indicated that both the temperature and time of carbonisation significantly influenced the yield of hydrochar (HC), as well as its physiochemical and structural properties. Higher temperatures and prolonged residence time led to decreased yield, elevated fixed carbon content and an increased fuel ratio. Furthermore, raising the process conditions increased HHV and reduced the oxygen-containing functional groups. The HC yield dropped from 28.75 to 17.58% with increased carbonisation temperature and time. The findings of this study also suggest that modified hydrochar is a promising material for removing heavy metals from wastewater. It is a relatively low-cost and abundant material that can be produced from various biomass feedstocks, including food waste. In addition, it is a sustainable and environmentally friendly option for wastewater treatment. Hydrochar-based systems offer several advantages over traditional methods of heavy metal removal, such as chemical precipitation and ion exchange. The unique physicochemical characteristics of hydrochar, including its porous structure and oxygen-rich functional groups, offer a high surface area and more binding sites for heavy metal ions. By changing the physicochemical properties of hydrochar with chemicals like phosphoric acid, it is possible to increase its adsorption capacity. The Freundlich isotherm was the best fit for the adsorption data for all three metal ions (Pb2+, Cu2+ and Cd2+), indicating that the adsorption process is multilayer and heterogeneous.