Highly Stretchable Polyurethane Porous Membranes with Adjustable Morphology for Advanced Lithium Metal Batteries.

A highly flexible, tunable morphology membrane with excellent thermal stability and ionic conductivity can endow lithium metal batteries with high power density and reduced dendrite growth. Herein, a porous Polyurethane (PU) membrane with an adjustable morphology was prepared by a simple nonsolvent-induced phase separation technique. The precise control of the final morphology of PU membranes can be achieved through appropriate selection of a nonsolvent, resulting a range of pore structures that vary from finger-like voids to sponge-like pores. The implementation of combinatorial DFT and experimental analysis has revealed that spongy PU porous membranes, especially PU-EtOH, show superior electrolyte wettability (472%), high porosity (75%), good mechanical flexibility, robust thermal dimensional stability (above 170 °C), and elevated ionic conductivity (1.38 mS cm-1) in comparison to the polypropylene (PP) separator. The use of PU-EtOH in Li//Li symmetric cell results in a prolonged lifespan of 800 h, surpasing the longevity of PU or PP cells. Moreover, when subjected to a high rate of 5C, the LiFePO4/Li half-cell with a PU-EtOH porous membrane displayed better cycling performance (115.4 mAh g-1) compared to the PP separator (104.4 mAh g-1). Finally, the prepared PU porous membrane exhibits significant potential for improving the efficiency and safety of LMBs.

Formation mechanisms and degradation methods of polycyclic aromatic hydrocarbons in biochar: A review.

Biochar has been widely used in soil amendment and environmental remediation. Polycyclic aromatic hydrocarbons (PAHs) could be produced in preparation of biochar, which may pose potential risks to the environment and human health. At present, most studies focus on the ecotoxicity potential of biochar, while there are few systematic reviews on the formation mechanisms and mitigation strategies of PAHs in biochar. Therefore, a systematical understanding of the distribution, formation mechanisms, risk assessment, and degradation approaches of PAHs in biochar is highly needed. In this paper, the distribution and content of the total and bioavailable PAHs in biochar are reviewed. Then the formation mechanisms, influencing factors, and potential risk assessment of PAHs in biochar are systematically explored. After that, the effective strategies to alleviate PAHs in biochar are summarized. Finally, suggestions and perspectives for future studies are proposed. This review provides a guide for reducing the formation of biochar-associated PAHs and their toxicity, which is beneficial for the development and large-scale safe use of environmentally friendly biochar.

Super capacity of ligand-engineered biochar for sorption of malachite green dye: key role of functional moieties and mesoporous structure.

This study synthesized a new thiomalic acid-modified rice husk biochar (TMA-BC) as a versatile and eco-friendly sorbent. After undergoing chemical treatments, the mercerized rice husk biochar (NaOH-BC) and TMA-BC samples showed higher BET surface area values of 277.1 m2/g and 305.8 m2/g, respectively, compared to the pristine biochar (BC) sample, which had a surface area of 234.2 m2/g. In batch adsorption experiments, it was found that the highest removal efficiency for malachite green (MG) was achieved with TMA-BC, reaching 96.4%, while NaOH-BC and BC exhibited removal efficiencies of 38.6% and 27.9%, respectively, at pH 8. The engineered TMA-BC exhibited a super adsorption capacity of 104.17 mg/g for MG dye at pH 8.0 and 25 °C with a dosage of 2 g/L. The SEM, TEM, XPS, and FTIR spectroscopy analyses were performed to examine mesoporous features and successful TMA-BC carboxylic and thiol functional groups grafting on biochar. Electrostatic forces, such as π - π interactions, hydrogen bonding, and pore intrusion, were identified as key factors in the sorption of MG dye. As compared to single-solution adsorption experiments, the binary solution experiments performed at optimized dosages of undesired ions, such as humic acid, sodium dodecyl sulfate surfactant, NaCl, and NaSCN, reflected an increase in MG dye removal of 2.8%, 8.7%, 5.4%, and 12.7%, respectively, which was attributed to unique mesoporous features and grafted functional groups of TMA-BC. Furthermore, the TMA-BC showed promising reusability up to three cycles. Our study indicates that mediocre biochar modified with TMA can provide an eco-friendly and cost-effective alternative to commercially accessible adsorbents.

Cyberattack patterns in blockchain-based communication networks for distributed renewable energy systems: A study on big datasets.

Blockchain-based reliable, resilient, and secure communication for Distributed Energy Resources (DERs) is essential in Smart Grid (SG). The Solana blockchain, due to its high stability, scalability, and throughput, along with low latency, is envisioned to enhance the reliability, resilience, and security of DERs in SGs. This paper presents big datasets focusing on SQL Injection, Spoofing, and Man-in-the-Middle (MitM) cyberattacks, which have been collected from Solana blockchain-based Industrial Wireless Sensor Networks (IWSNs) for events monitoring and control in DERs. The datasets provided include both raw (unprocessed) and refined (processed) data, which highlight distinct trends in cyberattacks in DERs. These distinctive patterns demonstrate problems like superfluous mass data generation, transmitting invalid packets, sending deceptive data packets, heavily using network bandwidth, rerouting, causing memory overflow, overheads, and creating high latency. These issues result in ineffective real-time events monitoring and control of DERs in SGs. The thorough nature of these datasets is expected to play a crucial role in identifying and mitigating a wide range of cyberattacks across different smart grid applications.

Dual-3DM3AD: Mixed Transformer Based Semantic Segmentation and Triplet Pre-Processing for Early Multi-Class Alzheimer's Diagnosis.

Alzheimer's Disease (AD) is a widespread, chronic, irreversible, and degenerative condition, and its early detection during the prodromal stage is of utmost importance. Typically, AD studies rely on single data modalities, such as MRI or PET, for making predictions. Nevertheless, combining metabolic and structural data can offer a comprehensive perspective on AD staging analysis. To address this goal, this paper introduces an innovative multi-modal fusion-based approach named as Dual-3DM3-AD. This model is proposed for an accurate and early Alzheimer's diagnosis by considering both MRI and PET image scans. Initially, we pre-process both images in terms of noise reduction, skull stripping and 3D image conversion using Quaternion Non-local Means Denoising Algorithm (QNLM), Morphology function and Block Divider Model (BDM), respectively, which enhances the image quality. Furthermore, we have adapted Mixed-transformer with Furthered U-Net for performing semantic segmentation and minimizing complexity. Dual-3DM3-AD model is consisted of multi-scale feature extraction module for extracting appropriate features from both segmented images. The extracted features are then aggregated using Densely Connected Feature Aggregator Module (DCFAM) to utilize both features. Finally, a multi-head attention mechanism is adapted for feature dimensionality reduction, and then the softmax layer is applied for multi-class Alzheimer's diagnosis. The proposed Dual-3DM3-AD model is compared with several baseline approaches with the help of several performance metrics. The final results unveil that the proposed work achieves 98% of accuracy, 97.8% of sensitivity, 97.5% of specificity, 98.2% of f-measure, and better ROC curves, which outperforms other existing models in multi-class Alzheimer's diagnosis.

Recent Theoretical and Experimental Advancements of Aluminum-Sulfur Batteries.

Aluminum-sulfur batteries (AlSBs) exhibit significant potential as energy storage systems due to their notable attributes, including a high energy density, cost-effectiveness, and abundant availability of aluminum and sulfur. In order to commercialize AlSBs, an understanding of their working principles is necessary. In this review, we examine the current advancements in cathodes, both in theory and practice, as well as the progress made in aqueous and nonaqueous electrolytes. We also explore the modifications made to separators and the theoretical understanding of problems associated with AlSBs. Furthermore, we discuss future research directions aimed at resolving these issues. Our aim is to summarize the current progress in AlSBs and, based on recent progress and understanding of the mechanism, help design a battery to overcome the challenges that such batteries have been facing.

Development of a tetravalent subunit vaccine against dengue virus through a vaccinomics approach.

Dengue virus infection (DVI) is a mosquito-borne disease that can lead to serious morbidity and mortality. Dengue fever (DF) is a major public health concern that affects approximately 3.9 billion people each year globally. However, there is no vaccine or drug available to deal with DVI. Dengue virus consists of four distinct serotypes (DENV1-4), each raising a different immunological response. In the present study, we designed a tetravalent subunit multi-epitope vaccine, targeting proteins including the structural protein envelope domain III (EDIII), precursor membrane proteins (prM), and a non-structural protein (NS1) from each serotype by employing an immunoinformatic approach. Only conserved sequences obtained through a multiple sequence alignment were used for epitope mapping to ensure efficacy against all serotypes. The epitopes were shortlisted based on an IC50 value <50, antigenicity, allergenicity, and a toxicity analysis. In the final vaccine construct, overall, 11 B-cell epitopes, 10 HTL epitopes, and 10 CTL epitopes from EDIII, prM, and NS1 proteins targeting all serotypes were selected and joined via KK, AAY, and GGGS linkers, respectively. We incorporated a 45-amino-acid-long B-defensins adjuvant in the final vaccine construct for a better immunogenic response. The vaccine construct has an antigenic score of 0.79 via VaxiJen and is non-toxic and non-allergenic. Our refined vaccine structure has a Ramachandran score of 96.4%. The vaccine has shown stable interaction with TLR3, which has been validated by 50 ns of molecular dynamics (MD) simulation. Our findings propose that a designed multi-epitope vaccine has substantial potential to elicit a strong immune response against all dengue serotypes without causing any adverse effects. Furthermore, the proposed vaccine can be experimentally validated as a probable vaccine, suggesting it may serve as an effective preventative measure against dengue virus infection.

Atorvastatin as an Antihypertensive Agent: A Pilot Study.

Objective Hypertension (HTN) is among the most common causes of chronic disease burden, along with dyslipidemia. It is a prominent risk factor for cardiovascular and cerebrovascular morbidity and mortality. More often than not, HTN coexists with dyslipidemia. This study aimed to see the antihypertensive effect of statins (atorvastatin), as certain animal models have shown that statins have a voltage-gated calcium channel-blocking effect. Material and methods This was a randomized controlled trial done at the Ayub Hospital Complex in Abbottabad, Pakistan. After ethical approval, 120 patients with newly diagnosed hypertension belonging to either gender and aged 35 and above were enrolled in the trial. They were randomly divided into two groups, with each group comprising 60 patients. One group was administered amlodipine 5 mg per oral (PO) once a day, while the other group was given 5 mg of amlodipine PO plus 10 mg of atorvastatin PO. The patients were examined on a follow-up visit 14 days later, and blood pressure was recorded as per protocols. Results A total of 120 newly diagnosed patients were studied in this trial. The mean age was 51.07 years, with a standard deviation of ±6.15 years and a range of 41-60 years. There were 64 (53.3%) males and 56 (46.7%) females in the study. The mean systolic blood pressures (SBPs) and diastolic blood pressures (DBPs) in Group 2 (amlodipine 5 mg + atorvastatin 10 mg) were significantly lower than the patients in Group 1 (only amlodipine 5 mg) in the follow-up visit, which was 14 days after starting the medication (p≤0.05). Conclusion The addition of a lipid-lowering drug to an antihypertensive regimen results in a better lowering of blood pressure in hypertensive individuals.

Enhancement effect of AgO nanoparticles on fermentative cellulase activity from thermophilic Bacillus subtilis Ag-PQ.

BACKGROUND: Cellulase is an important bioprocessing enzyme used in various industries. This study was conducted with the aim of improving the biodegradation activity of cellulase obtained from the Bacillus subtilis AG-PQ strain. For this purpose, AgO and FeO NPs were fabricated using AgNO3 and FeSO4·7H2O salt respectively through a hydro-thermal method based on five major steps; selection of research-grade materials, optimization of temperature, pH, centrifuge, sample washed with distilled water, dry completely in the oven at the optimized temperature and finally ground for characterization. The synthesized NPs were characterized by scanning electron microscope (SEM), energy dispersive X-ray (EDX), and X-ray diffraction (XRD) to confirm the morphology, elemental composition, and structure of the sample respectively. The diameter of the NPs was recorded through SEM which lay in the range of 70-95 nm. RESULTS: Cultural parameters were optimized to achieve better cellulase production, where incubation time of 56 h, inoculum size of 5%, 1% coconut cake, 0.43% ammonium nitrate, pH 8, and 37 °C temperature were found optimal. The enhancing effect of AgO NPs was observed on cellulase activity (57.804 U/ml/min) at 50 ppm concentration while FeO NPs exhibited an inhibitory effect on cellulase activity at all concentrations. Molecular docking analysis was also performed to understand the underlying mechanism of improved enzymatic activity by nanocatalysts. CONCLUSION: This study authenticates AgO NPs as better nanocatalysts for improved thermostable cellulase biodegradation activity with the extraordinary capability to be potentially utilized in bioethanol production.

Segmentation and classification of skin lesions using hybrid deep learning method in the Internet of Medical Things.

INTRODUCTION: Particularly within the Internet of Medical Things (IoMT) context, skin lesion analysis is critical for precise diagnosis. To improve the accuracy and efficiency of skin lesion analysis, CAD systems play a crucial role. To segment and classify skin lesions from dermoscopy images, this study focuses on using hybrid deep learning techniques. METHOD: This research uses a hybrid deep learning model that combines two cutting-edge approaches: Mask Region-based Convolutional Neural Network (MRCNN) for semantic segmentation and ResNet50 for lesion detection. To pinpoint the precise location of a skin lesion, the MRCNN is used for border delineation. We amass a huge, annotated collection of dermoscopy images for thorough model training. The hybrid deep learning model to capture subtle representations of the images is trained from start to finish using this dataset. RESULTS: The experimental results using dermoscopy images show that the suggested hybrid method outperforms the current state-of-the-art methods. The model's capacity to segment lesions into distinct groups is demonstrated by a segmentation accuracy measurement of 95.49 percent. In addition, the classification of skin lesions shows great accuracy and dependability, which is a notable advancement over traditional methods. The model is put through its paces on the ISIC 2020 Challenge dataset, scoring a perfect 96.75% accuracy. Compared to current best practices in IoMT, segmentation and classification models perform exceptionally well. CONCLUSION: In conclusion, this paper's hybrid deep learning strategy is highly effective in skin lesion segmentation and classification. The results show that the model has the potential to improve diagnostic accuracy in the setting of IoMT, and it outperforms the current gold standards. The excellent results obtained on the ISIC 2020 Challenge dataset further confirm the viability and superiority of the suggested methodology for skin lesion analysis.

Lyme rashes disease classification using deep feature fusion technique.

Automatic classification of Lyme disease rashes on the skin helps clinicians and dermatologists' probe and investigate Lyme skin rashes effectively. This paper proposes a new in-depth features fusion system to classify Lyme disease rashes. The proposed method consists of two main steps. First, three different deep learning models, Densenet201, InceptionV3, and Exception, were trained independently to extract the deep features from the erythema migrans (EM) images. Second, a deep feature fusion mechanism (meta classifier) is developed to integrate the deep features before the final classification output layer. The meta classifier is a basic deep convolutional neural network trained on original images and features extracted from base level three deep learning models. In the feature fusion mechanism, the last three layers of base models are dropped out and connected to the meta classifier. The proposed deep feature fusion method significantly improved the classification process, where the classification accuracy was 98.97%, which is particularly impressive than the other state-of-the-art models.

Arthroscopic Stabilisation in Anterior Shoulder Instability: An Analysis of Mid-term Outcome.

OBJECTIVE: To assess the clinical outcome of arthroscopic stabilisation for recurrent anterior glenohumeral joint dislocations. STUDY DESIGN: Case series. Place and Duration of the Study: Ghurki Trust Teaching Hospital, Lahore, from May 2018 to December 2022. METHODOLOGY: Fifty-two patients who underwent arthroscopic repair of Bankart lesion were studied. Those who had concomitant fractures, neurological injury or underwent any surgery on the same shoulder were excluded. The outcome was assessed in terms of improvement in pain, constant and Rowe scores, postoperatively. RESULTS: The mean delay in the procedure after the first dislocation was 65.15+38.23 months, and the mean follow-up period was 26.61+7.55 months. On final follow-up, improvement in pain was statistically significant (p<0.05): (VAS: 2.17+1.77 to 0.69+0.64 at rest and from 4.5+2.24 to 1.58+1.85 at motion). On subjective assessment, 50% of the sample was very satisfied, and an objective assessment showed statistically significant (p<0.05) improvement in Constant score from 65.4 to 78.6. Rowe score not only showed a significant improvement (from 31.6 to 80.3 with p<0.05), but 69.2% of the follow-up scores lied in the good and excellent results range as well. Recurrence was noted in 8 cases (15.3%). CONCLUSION: Significant clinical improvement was seen after arthroscopic stabilisation in anterior shoulder instability. However, further research is required regarding the recurrence rate and restriction in the range of motion. KEY WORDS: Glenohumeral joint, Dislocation, Stabilisation, Shoulder arthroscopy.

Evaluating the influence of green growth, institutional quality and financial inclusion on financial stability: evidence by sustainable finance theory.

Financial stability is essential for economic growth because it fosters confidence and trust and promotes investment in green development. However, it is a dilemma for the world economies to create an equilibrium between financial stability and environmental sustainability. In the extent of these challenges, the present study aims at grabbing the link of financial inclusion to attain financial stability. Further, the present study investigates the association of institutional quality, renewable energy, green growth, environmental sustainability, and financial inclusion with financial stability. Two basic econometric models are applied that focused on the basic and interaction term outcomes. In addition, principal component analysis (PCA) is analyzed to design an index for five proxies of financial inclusion. Additionally, the research inspected the interaction term of institutional quality and financial inclusion (FIN*INSQ) and determined the multiplied impact on financial stability in a separate model. This research employed the linear autoregressive distributed lag approach from 1990 to 2020 for long- and short-term dynamics. Theoretically, the research supports the sustainable finance and financial development theory. Hence, results showed that financial inclusion and institutional quality are positively associated with financial stability, while green growth, environmental sustainability, and renewable energy mechanisms are achieved through financial stability. Following our findings, the government should establish consistency between financial development and economic policies to maintain financial instability and ensure financial soundness. Furthermore, countries require viable financial institutions prioritizing green growth and institutional quality to achieve financial stability and long-term development.

Machine learning-based prediction of osteoporosis in postmenopausal women with clinical examined features: A quantitative clinical study.

Osteoporosis is a skeletal disease that is commonly seen in older people but often neglected due to its silent nature. To overcome the issue of osteoporosis in men and women, we proposed an advanced prediction model with the help of machine learning techniques which can help to identify the potential occurrence of this bone disease by its advanced screening tools. To achieve more reliable and accurate results, various machine-learning techniques were applied to the presented data sets. Moreover, we also compared the performance of our results with other existing algorithms to solely focus on the advanced features of the proposed methodology. The two data sets, the clinical tests of patients in Taiwan and medical reports of postmenopausal women in Korea through Korean Health and Nutrition Examination Surveys (2010-2011) were considered in this study. To predict bone disorders, we utilized the data about females and developed a system using artificial neural networks, support vector machines, and K-nearest neighbor. To compare the performance of the model Area under the Receiver Operating Characteristic Curve and other evaluation metrics were compared. The achieved results from all the algorithms and compared them with Osteoporosis Self-Assessment Tool for Asians and the results were noticeably better and more reliable than existing systems due to the involvement of ML. Using machine learning techniques to predict these types of diseases is better because physicians and patients can take early action to prevent the consequences in advance.

Assessment of meteorological and air quality drivers of elevated ambient ozone in Beijing via machine learning approach.

Over the past few years, surface ozone (O3) pollution has dominated China's air pollution as particulate matter has decreased. In Beijing, the annual average concentrations of ground-level O3 from 2015 to 2020 regularly increased from 57.32 to 62.72 µg/m3, showing a change of almost 9.4%, with a 1.6% per year increase. The meteorological factors are the primary influencer of elevated O3 levels; however, their importance and heterogeneity of variables remain rarely understood. In this study, we used 13 meteorological factors and 6 air quality (AQ) parameters to estimate their influencing score using the random forest (RF) algorithm to explain and predict ambient O3. Among the meteorological variables and overall, both land surface temperature and temperature at 2 m from the surface emerged as the most influential factors, while NO2 stood out as the highest influencing factor from the AQ parameters. Indeed, it is crucial and imperative to reduce the temperature caused by climate change in order to effectively control ambient O3 levels in Beijing. Overall, meteorological factors alone exhibited a higher coefficient of determination (R2) value of 0.80, compared with AQ variables of 0.58, for the post-lockdown period. In addition, we calculated the number of days O3 concentration levels exceeded the WHO standard and newly proposed peak-season maximum daily 8-h average (MDA8) O3 guideline for Beijing. The exceedance number of days from the WHO standard of MDA8 ambient O3 was observed to be the highest in June, and each studied year crossed peak season guidelines by almost 2 times margin. This study demonstrates the contributions of meteorological variables and AQ parameters in surging ambient O3 and highlights the importance of future research toward devising an optimum strategy to combat growing O3 pollution in urban areas.

Comparison of Four Purification Methods on Serum Extracellular Vesicle Recovery, Size Distribution, and Proteomics.

In recent decades, the role played by extracellular vesicles in physiological and pathological processes has attracted attention. Extracellular vesicles are released by different types of cells and carry molecules that could become biomarkers for the diagnosis of diseases. Extracellular vesicles are also moldable tools for the controlled release of bioactive substances in clinical and therapeutic applications. However, one of the significant challenges when studying these exciting and versatile vesicles is the purification process, which presents significant difficulties in terms of lack of purity, yield, and reproducibility, reflected in unreliable data. Therefore, our objective in the present study was to compare the proteomic profile of serum-derived EVs purified using ExoQuick™ (Systems Biosciences), Total Isolation Kit (Life Technologies), Ultracentrifugation, and Ultrafiltration. Each technique utilized for purification has shown different concentrations and populations of purified particles. The results showed marked differences in distribution, size, and protein content, demonstrating the need to develop reproducible and reliable protocols to isolate extracellular vesicles for their clinical application.

Secure medical image transmission using deep neural network in e-health applications.

Recently, medical technologies have developed, and the diagnosis of diseases through medical images has become very important. Medical images often pass through the branches of the network from one end to the other. Hence, high-level security is required. Problems arise due to unauthorized use of data in the image. One of the methods used to secure data in the image is encryption, which is one of the most effective techniques in this field. Confusion and diffusion are the two main steps addressed here. The contribution here is the adaptation of the deep neural network by the weight that has the highest impact on the output, whether it is an intermediate output or a semi-final output in additional to a chaotic system that is not detectable using deep neural network algorithm. The colour and grayscale images were used in the proposed method by dividing the images according to the Region of Interest by the deep neural network algorithm. The algorithm was then used to generate random numbers to randomly create a chaotic system based on the replacement of columns and rows, and randomly distribute the pixels on the designated area. The proposed algorithm evaluated in several ways, and compared with the existing methods to prove the worth of the proposed method.

Significance of biopolymer-based hydrogels and their applications in agriculture: a review in perspective of synthesis and their degree of swelling for water holding.

Hydrogels are three-dimensional polymer networks that are hydrophilic and capable of retaining a large amount of water. Hydrogels also can act as vehicles for the controlled delivery of active compounds. Bio-polymers are polymers that are derived from natural sources. Hydrogels prepared from biopolymers are considered non-toxic, biocompatible, biodegradable, and cost-effective. Therefore, bio-polymeric hydrogels are being extensively synthesized and used all over the world. Hydrogels based on biopolymers finds important applications in the agricultural field where they are used as soil conditioning agents as they can increase the water retention ability of soil and can act as a carrier of nutrients and other agrochemicals. Hydrogels are also used for the controlled delivery of fertilizer to plants. In this review, bio-polymeric hydrogels based on starch, chitosan, guar gum, gelatin, lignin, and alginate polymer have been discussed in terms of their synthesis method, swelling behavior, and possible agricultural application. The urgency to address water scarcity and the need for sustainable water management in agriculture necessitate the exploration and implementation of innovative solutions. By understanding the synthesis techniques and factors influencing the swelling behavior of these hydrogels, we can unlock their full potential in fostering sustainable agriculture and mitigating the challenges posed by an ever-changing environment.

Optimization of pig manure-derived biochar for ammonium and phosphate simultaneous recovery from livestock wastewater.

Livestock wastewater has led to serious eco-environmental issues. To effectively treat livestock wastewater and realize the resource utilization of livestock solid waste, manure waste has been widely used to prepare biochar for the recovery of nitrogen and phosphorus. However, fresh biochar has a poor ability to adsorb phosphate due to its negative charge. To overcome the defect, the proportion of biochar samples prepared at 400 °C and 700 °C was optimized under a mass ratio of 2:3 to obtain mixed biochar PM 4-7, achieving the purpose of enhanced ammonium and phosphate recovery in livestock wastewater simultaneously without any modification. The effects of pyrolysis temperature, dosage, and pH were studied, different adsorption models were used to explore the adsorption mechanism, and the effect of biochar loaded with nutrient elements on seed was verified through a seed germination experiment. It was revealed that the maximum removal rates of phosphate and ammonium were 33.88 % and 41.50 %, respectively, endorsing that mixed biochar PM 4-7 can recover nutrients from livestock wastewater, and could be used as a slow-release fertilizer to promote seed germination and growth. This method provides a new potential way for the efficient resource utilization of pig manure and the recovery of nutrients from breeding wastewater.