Phosphorus recovery and tetracycline Mitigation: The role of Bacillus cereus LB-9 in struvite biomineralization from wastewater.

Struvite biomineralization is an ecologically sound technology, adept at the efficient recovery and recycling of phosphorus from wastewater. However, the biomineralization process is often perturbed by the presence of antibiotics, notably tetracycline (TC), the impact of which on the biomineralization system has not been elucidated. This study examines the efficacy of Bacillus cereus LB-9 in struvite biomineralization, focusing on the precipitates' composition, morphology, and TC content. LB-9 facilitate an alkaline environment that effectively recovering nitrogen and phosphorus. These findings indicate that TC retards the initial formation of struvite and the concurrent recovery of nitrogen and phosphorus. However, at concentrations below 10 mg/L TC concentrations, TC enhanced struvite production (0.38g) by stimulating LB-9's growth and metabolic activity. Conversely, at a concentration of 10 mg/L TC, the strain's activity was markedly suppressed within the initial four days. This data suggests that TC promotes the strain's proliferation and metabolism, potentially through cellular secretions, thereby augmenting phosphorus recovery from wastewater. Notably, the recovered struvite doesn't contain TC, aligning with regulatory standards for agricultural application. In summary, LB-9-mediated struvite recovery is an effective strategy for producing phosphorus-enriched fertilizers and mitigating TC contamination, offering significant implications for wastewater treatment and industrial process development, particularly in the context of prevalent TC in wastewater.

Influences of defective interphase and contact region among nanosheets on the electrical conductivity of polymer graphene nanocomposites.

In the current article, a defective interface is characterized by "Dc," representing the smallest diameter of nanosheets crucial for effective conduction transfer from the conductive filler to the medium, and by "ψ" as interfacial conduction. These parameters define the effective aspect ratio and operational volume fraction of graphene in the samples. The resistances of the graphene and polymer layer in contact zones are also considered to determine the contact resistance between adjacent nanosheets. Subsequently, a model for the tunneling conductivity of composites is proposed based on these concepts. This innovative model is validated by experimental data. Additionally, the effects of various factors on the conductivity of the composites and contact resistance are analyzed. Certain parameters such as filler concentration, graphene conductivity, interfacial conduction, and "Dc" do not affect the contact resistance due to the superconductivity of the nanosheets. However, factors like thin and large nanosheets, short tunneling distance (d), high interfacial conduction (ψ), low "Dc," and low tunnel resistivity (ρ) contribute to increased conductivity in nanocomposites. The maximum conductivity of 0.09 is obtained at d = 2 nm and ψ = 900 S/m, but d > 6 nm and ψ < 200 S/m produce an insulated sample. Additionally, the highest conductivity of 0.11 S/m is achieved with Dc = 100 nm and ρ = 100 Ω m, whereas the conductivity approaches 0 at Dc = 500 nm and ρ = 600 Ω m.

From scraps to purification: innovative use of food waste-derived hydrochar in eradicating pharmaceutical pollutants.

Chemical products (CPs) such as carbamazepine and naproxen, present in aquatic environments, pose significant risks to both aquatic life and human health. This study investigated the use of hydrothermally carbonized food waste-derived hydrochar (AC-HTC) at three distinct temperatures (200, 250, and 300 °C) as an adsorbent to remove these CPs from water. Our research focused on the impact of hydrothermal carbonization temperature on hydrochar properties and the effects of chemical activation with phosphoric acid on adsorption capacity. Hydrothermal carbonization increased the hydrochar's surface area from 1.47 to 7.52 m2/g, which was further enhanced to 32.81 m2/g after activation with phosphoric acid. Batch adsorption experiments revealed that hydrochar produced at 250 °C (AC-HTC-250) demonstrated high adsorption capacities of 49.10 mg/g for carbamazepine and 14.35 mg/g for naproxen, outperforming several conventional adsorbents. Optimal adsorption occurred at pH 4, aligning well with the Langmuir and pseudo-first-order models. The hydrochar showed potential for regeneration and multiple uses, suggesting its applicability in sustainable wastewater treatment. Future research will explore scalability and effectiveness against a broader range of pollutants.

Assessment of electrical conductivity of polymer nanocomposites containing a deficient interphase around graphene nanosheet.

In this study, a poor/imperfect interphase is assumed to express the effective interphase thickness, operative filler concentration, percolation onset and volume share of network in graphene-polymer systems. Additionally, a conventional model is advanced by the mentioned terms for conductivity of samples by the extent of conduction transference between graphene and polymer medium. The model predictions are linked to the experimented data. Likewise, the mentioned terms as well as the conductivity of nanocomposites are expressed at dissimilar ranges of various factors. The novel equations successfully predict the percolation onset and conductivity in the samples containing a poor/imperfect interphase. Thin and long nanosheets with high conduction transportation desirably govern the percolation onset and nanocomposite conductivity, but a bigger tunneling distance causes a lower conductivity.

Green loops and clean skies: Optimizing municipal solid waste management using data science for a circular economy.

The interplay between Municipal Solid Waste (MSW) Management and data science unveils a panorama of opportunities and challenges, set against the backdrop of rising global waste and evolving technological landscapes. This article threads through the multifaceted aspects of incorporating data science into MSW management, unearthing key findings, novel knowledge, and instigating a call to action for stakeholders (e.g. policymakers, local authorities, waste management professionals, technology developers, and the general public) across the spectrum. Predominant challenges like the enigmatic nature of "black-box" models and tangible knowledge gaps in the sector are scrutinized, ushering in a narrative that emphasizes transparent, stakeholder-inclusive, and policy-adaptive approaches. Notably, a conscious shift towards "white-box" and "grey-box" data science models has been spotlighted as a pivotal response to transparency issues. Furthermore, the discourse highlights the necessity of crafting data science solutions that are specifically moulded to the nuanced challenges of MSW management, and it underscores the importance of recalibrating existing policies to be reflexive to technological advancements. A resolute call echoes for stakeholders to not just adapt but immerse themselves in a continuous learning trajectory, championing transparency, and fostering collaborations that hinge on innovative, data-driven methodologies. Thus, as the realms of data science and MSW management entwine, the article sheds light on the potential transformation awaiting waste management paradigms, contingent on the nurtured amalgamation of technological advances, policy alignment, and collaborative synergy.

Percolation onset and conductivity of nanocomposites assuming an incomplete dispersion of graphene nanosheets in a polymer matrix.

Herein, stacks of graphene nanosheets resulting from an incomplete dispersion of nanoparticles in polymer graphene nanocomposites are considered. The volume fraction, aspect ratio and conduction of stacks are expressed by the distance between nanosheets (s), thickness of an individual nanosheet (t), nanosheet diameter (D), thickness of the interphase zone (ti) and tunneling length (d). Moreover, the percolation onset, actual filler quantity and portion of networked nanosheets are stated by the stacks of nanosheets, interphase depth and tunneling length. Finally, an advanced model for the conductivity of a graphene-based system is presented using the mentioned terms. The influence of all properties of stacks, tunneling and interphase areas on the percolation onset, portion of percolated nanosheets and conductivity are examined. Furthermore, the tested values of conductivity are applied to confirm the predictability of the model. The larger quantity of thin sheets included in stacks produces a higher conductivity for samples. In addition, a thicker interphase and smaller tunnels can result in higher conductivity. The calculations of conductivity match the tested data at all filler amounts.

Estimating hydrogen absorption energy on different metal hydrides using Gaussian process regression approach.

Hydrogen is a promising alternative energy source due to its significantly high energy density. Also, hydrogen can be transformed into electricity in energy systems such as fuel cells. The transition toward hydrogen-consuming applications requires a hydrogen storage method that comes with pack hydrogen with high density. Among diverse methods, absorbing hydrogen on host metal is applicable at room temperature and pressure, which does not provide any safety concerns. In this regard, AB2 metal hydride with potentially high hydrogen density is selected as an appropriate host. Machine learning techniques have been applied to establish a relationship on the effect of the chemical composition of these hosts on hydrogen storage. For this purpose, a data bank of 314 data point pairs was used. In this assessment, the different A-site and B-site elements were used as the input variables, while the hydrogen absorption energy resulted in the output. A robust Gaussian process regression (GPR) approach with four kernel functions is proposed to predict the hydrogen absorption energy based on the inputs. All the GPR models' performance was quite excellent; notably, GPR with Exponential kernel function showed the highest preciseness with R2, MRE, MSE, RMSE, and STD of 0.969, 2.291%, 3.909, 2.501, and 1.878, respectively. Additionally, the sensitivity of analysis indicated that ZR, Ti, and Cr are the most demining elements in this system.

Biomedical engineering of polysaccharide-based tissue adhesives: Recent advances and future direction.

Tissue adhesives have been widely used for preventing wound leaks, sever bleeding, as well as for enhancing drug delivery and biosensing. However, only a few among suggested platforms cover the circumstances required for high-adhesion strength and biocompatibility, without toxicity. Antibacterial properties, controllable degradation, encapsulation capacity, detectability by image-guided procedures and affordable price are also centered to on-demand tissue adhesives. Herein we overview the history of tissue adhesives, different types of polysaccharide-based tissue adhesives, their mechanism of gluing, and different applications of polysaccharide-based tissue adhesives. We also highlight the latest progresses in engineering of tissue adhesives followed by existing challenges in fabrication processes. We argue that future studies have to place focus on a holistic understanding of biomaterials and tissue surface properties, proper fabrication procedures, and development of magnetic and conductive responsive adhesives in order to bridge the huge gap between the present studies for clinical implementation.

Feasibility of using different hydrothermal processes for sewage sludge management in China.

Due to its tremendous volume and severe environmental concern, sewage sludge (SS) management and treatment are significant in China. The recent prohibition (June 2021) of reusing SS as organic fertilizers makes it urgent to develop alternative processes. However, there is currently little research analyzing the applicability of using HP for sewage SS treatment in China. The significant difference in SS composition and the much less land supply in urban areas might invalidate most previous localized suggestions. In this paper, the development of emerging hydrothermal processes (HPs) for SS treatment will be reviewed, focusing on their decomposition mechanisms and the benefits of HPs compared with current SS treatment technologies. The SS volume, composition, and regulatory regime in China will also be evaluated. Those efforts could address the potential SS treatment capacity shortage and provide an opportunity to recover nutrients, organics and energy embedded in SS. The results show that HPs' high investment cost is mainly limited by the process scale, while their operating costs are comparable to incineration. Minimizing equipment erosion, ensuring process safety, and designing a more efficient heat recovery system are recommended for the future commercialization of HPs in China.

Green Polymer Nanocomposites for Skin Tissue Engineering.

Fabrication of an appropriate skin scaffold needs to meet several standards related to the mechanical and biological properties. Fully natural/green scaffolds with acceptable biodegradability, biocompatibility, and physiological properties quite often suffer from poor mechanical properties. Therefore, for appropriate skin tissue engineering and to mimic the real functions, we need to use synthetic polymers and/or additives as complements to green polymers. Green nanocomposites (either nanoscale natural macromolecules or biopolymers containing nanoparticles) are a class of scaffolds with acceptable biomedical properties window (drug delivery and cardiac, nerve, bone, cartilage as well as skin tissue engineering), enabling one to achieve the required level of skin regeneration and wound healing. In this review, we have collected, summarized, screened, analyzed, and interpreted the properties of green nanocomposites used in skin tissue engineering and wound dressing. We particularly emphasize the mechanical and biological properties that skin cells need to meet when seeded on the scaffold. In this regard, the latest state of the art studies directed at fabrication of skin tissue and bionanocomposites as well as their mechanistic features are discussed, whereas some unspoken complexities and challenges for future developments are highlighted.

Highly antifouling polymer-nanoparticle-nanoparticle/polymer hybrid membranes.

We introduce highly antifouling Polymer-Nanoparticle-Nanoparticle/Polymer (PNNP) hybrid membranes as multi-functional materials for versatile purification of wastewater. Nitrogen-rich polyethylenimine (PEI)-functionalized halloysite nanotube (HNT-SiO2-PEI) nanoparticles were developed and embedded in polyvinyl chloride (PVC) membranes for protein and dye filtration. Bulk and surface characteristics of the resulting HNT-SiO2-PEI nanocomposites were determined using Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM), and thermogravimetric analysis (TGA). Moreover, microstructure and physicochemical properties of HNT-SiO2-PEI/PVC membranes were investigated by scanning electron microscopy (SEM), atomic force microscopy (AFM), and attenuated total reflectance (ATR)-FTIR. Results of these analyses indicated that the overall porosity and mean pore size of nanocomposite membranes were enhanced, but the surface roughness was reduced. Additionally, surface hydrophilicity and flexibility of the original PVC membranes were significantly improved by incorporating HNT-SiO2-PEI nanoparticles. Based on pure water permeability and bovine serum albumin (BSA)/dye rejection tests, the highest nanoparticle-embedded membrane performance was observed at 2 weight percent (wt%) of HNT-SiO2-PEI. The nanocomposite incorporation in the PVC membranes further improved its antifouling performance and flux recovery ratio (96.8%). Notably, dye separation performance increased up to 99.97%. Overall, hydrophobic PVC membranes were successfully modified by incorporating HNT-SiO2-PEI nanomaterial and better-quality wastewater treatment performance was obtained.

Crystalline polysaccharides: A review.

The biodegradability and mechanical properties of polysaccharides are dependent on their architecture (linear or branched) as well as their crystallinity (size of crystals and crystallinity percent). The amount of crystalline zones in the polysaccharide significantly governs their ultimate properties and applications (from packaging to biomedicine). Although synthesis, characterization, and properties of polysaccharides have been the subject of several review papers, the effects of crystallization kinetics and crystalline domains on the properties and application have not been comprehensively addressed. This review places focus on different aspects of crystallization of polysaccharides as well as applications of crystalline polysaccharides. Crystallization of cellulose, chitin, chitosan, and starch, as the main members of this family, were discussed. Then, application of the aforementioned crystalline polysaccharides and nano-polysaccharides as well as their physical and chemical interactions were overviewed. This review attempts to provide a complete picture of crystallization-property relationship in polysaccharides.

Modeling hydrogen solubility in hydrocarbons using extreme gradient boosting and equations of state.

Due to industrial development, designing and optimal operation of processes in chemical and petroleum processing plants require accurate estimation of the hydrogen solubility in various hydrocarbons. Equations of state (EOSs) are limited in accurately predicting hydrogen solubility, especially at high-pressure or/and high-temperature conditions, which may lead to energy waste and a potential safety hazard in plants. In this paper, five robust machine learning models including extreme gradient boosting (XGBoost), adaptive boosting support vector regression (AdaBoost-SVR), gradient boosting with categorical features support (CatBoost), light gradient boosting machine (LightGBM), and multi-layer perceptron (MLP) optimized by Levenberg-Marquardt (LM) algorithm were implemented for estimating the hydrogen solubility in hydrocarbons. To this end, a databank including 919 experimental data points of hydrogen solubility in 26 various hydrocarbons was gathered from 48 different systems in a broad range of operating temperatures (213-623 K) and pressures (0.1-25.5 MPa). The hydrocarbons are from six different families including alkane, alkene, cycloalkane, aromatic, polycyclic aromatic, and terpene. The carbon number of hydrocarbons is ranging from 4 to 46 corresponding to a molecular weight range of 58.12-647.2 g/mol. Molecular weight, critical pressure, and critical temperature of solvents along with pressure and temperature operating conditions were selected as input parameters to the models. The XGBoost model best fits all the experimental solubility data with a root mean square error (RMSE) of 0.0007 and an average absolute percent relative error (AAPRE) of 1.81%. Also, the proposed models for estimating the solubility of hydrogen in hydrocarbons were compared with five EOSs including Soave-Redlich-Kwong (SRK), Peng-Robinson (PR), Redlich-Kwong (RK), Zudkevitch-Joffe (ZJ), and perturbed-chain statistical associating fluid theory (PC-SAFT). The XGBoost model introduced in this study is a promising model that can be applied as an efficient estimator for hydrogen solubility in various hydrocarbons and is capable of being utilized in the chemical and petroleum industries.

Towards estimation of CO2 adsorption on highly porous MOF-based adsorbents using gaussian process regression approach.

In recent years, new developments in controlling greenhouse gas emissions have been implemented to address the global climate conservation concern. Indeed, the earth's average temperature is being increased mainly due to burning fossil fuels, explicitly releasing high amounts of CO2 into the atmosphere. Therefore, effective capture techniques are needed to reduce the concentration of CO2. In this regard, metal organic frameworks (MOFs) have been known as the promising materials for CO2 adsorption. Hence, study on the impact of the adsorption conditions along with the MOFs structural properties on their ability in the CO2 adsorption will open new doors for their further application in CO2 separation technologies as well. However, the high cost of the corresponding experimental study together with the instrument's error, render the use of computational methods quite beneficial. Therefore, the present study proposes a Gaussian process regression model with four kernel functions to estimate the CO2 adsorption in terms of pressure, temperature, pore volume, and surface area of MOFs. In doing so, 506 CO2 uptake values in the literature have been collected and assessed. The proposed GPR models performed very well in which the exponential kernel function, was shown as the best predictive tool with R2 value of 1. Also, the sensitivity analysis was employed to investigate the effectiveness of input variables on the CO2 adsorption, through which it was determined that pressure is the most determining parameter. As the main result, the accurate estimate of CO2 adsorption by different MOFs is obtained by briefly employing the artificial intelligence concept tools.

Hydrogel membranes: A review.

Hydrogel membranes (HMs) are defined and applied as hydrated porous media constructed of hydrophilic polymers for a broad range of applications. Fascinating physiochemical properties, unique porous architecture, water-swollen features, biocompatibility, and special water content dependent transport phenomena in semi-permeable HMs make them appealing constructs for various applications from wastewater treatment to biomedical fields. Water absorption, mechanical properties, and viscoelastic features of three-dimensional (3D) HM networks evoke the extracellular matrix (ECM). On the other hand, the porous structure with controlled/uniform pore-size distribution, permeability/selectivity features, and structural/chemical tunability of HMs recall membrane separation processes such as desalination, wastewater treatment, and gas separation. Furthermore, supreme physiochemical stability and high ion conductivity make them promising to be utilised in the structure of accumulators such as batteries and supercapacitors. In this review, after summarising the general concepts and production processes for HMs, a comprehensive overview of their applications in medicine, environmental engineering, sensing usage, and energy storage/conservation is well-featured. The present review concludes with existing restrictions, possible potentials, and future directions of HMs.

Wavelength Selection FOR Rapid Identification of Different Particle Size Fractions of Milk Powder Using Hyperspectral Imaging.

Hyperspectral imaging (HSI) in the spectral range of 400-1000 nm was tested to differentiate three different particle size fractions of milk powder. Partial least squares discriminant analysis (PLS-DA) was performed to observe the relationship of spectral data and particle size information for various samples of instant milk powder. The PLS-DA model on full wavelengths successfully classified the three fractions of milk powder with a coefficient of prediction 0.943. Principal component analysis (PCA) identified each of the milk powder fractions as separate clusters across the first two principal components (PC1 and PC2) and five characteristic wavelengths were recognised by the loading plot of the first three principal components. Weighted regression coefficient (WRC) analysis of the partial least squares model identified 11 important wavelengths. Simplified PLS-DA models were developed from two sets of reduced wavelengths selected by PCA and WRC and showed better performance with predictive correlation coefficients (Rp2) of 0.962 and 0.979, respectively, while PLS-DA with complete spectrum had Rp2 of 0.943. Similarly, classification accuracy of PLS-DA was improved to 92.2% for WRC based predictive model. Calculation time was also reduced to 2.1 and 2.8 s for PCA and WRC based simplified PLS-DA models in comparison to the complete spectrum model that was taking 32.2 s on average to predict the classification of milk powder samples. These results demonstrated that HSI with appropriate data analysis methods could become a potential analyser for non-invasive testing of milk powder in the future.