Role of silica-based porous cellulose nanocrystals in improving water absorption and mechanical properties.

Epoxy resins are important thermosetting polymers. They are widely used in many applications i.e., adhesives, plastics, coatings and sealers. Epoxy molding compounds have attained dominance among common materials due to their excellent mechanical properties. The sol-gel simple method was applied to distinguish the impact on the colloidal time. The properties were obtained with silica-based fillers to enable their mechanical and thermal improvement. The work which we have done here on epoxy-based nanocomposites was successfully modified. The purpose of this research was to look into the effects of cellulose nanocrystals (CNCs) on various properties and applications. CNCs have recently attracted a lot of interest in a variety of industries due to their high aspect ratio, and low density which makes them perfect candidates. Adding different amounts of silica-based nanocomposites to the epoxy system. Analyzed with different techniques such as Fourier-transformed infrared spectroscope (FTIR), thermogravimetric analysis (TGA) and scanning electronic microscopic (SEM) to investigate the morphological properties of modified composites. The various %-age of silica composite was prepared in the epoxy system. The 20% of silica was shown greater enhancement and improvement. They show a better result than D-400 epoxy. Increasing the silica, the transparency of the films decreased, because clustering appears. This shows that the broad use of CNCs in environmental engineering applications is possible, particularly for surface modification, which was evaluated for qualities such as absorption and chemical resistant behavior.

Reductive soil disinfestation and Fe amendment improve soil microbial composition and Fritillaria production.

The continuous obstacles of cropping cause severe economic loss, which seriously threaten agricultural sustainable development. In addition, managing excess waste, such as potato peel and mineral waste residues, is a vital burden for industry and agriculture. Therefore, we explored the feasibility of reductive soil disinfestation (RSD) with potato peel and amendment with iron mineral waste residues for the production of Fritillaria thunbergii, which is vulnerable to continuous obstacles. In this study, the influences of iron mineral, RSD with different organic maters, as well as the combined effects of iron mineral and RSD on Fritillaria rhizosphere soil physicochemical properties, microbial communities, and Fritillaria production were investigated. The results revealed that the RSD treatments with potato peel significantly reduced the soil salinity and increased the soil pH, microbial activity, organic matter, and the contents of K and Ca. RSD with potato peel also significantly thrived of the beneficial microbes (Bacillus, Azotobacter, Microvirga, and Chaetomium), and down-regulated potential plant pathogens. RSD with potato peel significantly promoted F. thunbergii yield and quality. Moreover, the combined effects of RSD and iron mineral amendment further enhanced soil health, improved microbial community composition, and increased the yield and peimisine content of F. thunbergii by 24.2% and 49.3%, respectively. Overall, our results demonstrated that RSD with potato peel and amendment with iron mineral waste residues can efficiently improve soil fertility, modify the microbial community, and benefit for both the sustainable production of F. thunbergii and the management of waste. KEY POINTS: • RSD increases soil pH, organic matter, microbial activity, and mineral content • RSD with potato peel enriches beneficial microbes and decreases plant pathogens • PP + Fe treatment increases Fritillaria yield by 24.2% and peimisine content by 49.3.

Carbon-based catalyst for environmental bioremediation and sustainability: Updates and perspectives on techno-economics and life cycle assessment.

Global rise in the generation of waste has caused an enormous environmental concern and waste management problem. The untreated carbon rich waste serves as a breeding ground for pathogens and thus strategies for production of carbon rich biochar from waste by employing different thermochemical routes namely hydrothermal carbonization, hydrothermal liquefaction and pyrolysis has been of interest by researchers globally. Biochar has been globally produced due to its diverse applications from environmental bioremediation to energy storage. Also, several factors affect the production of biochar including feedstock/biomass type, moisture content, heating rate, and temperature. Recently the application of biochar has increased tremendously owing to the cost effectiveness and eco-friendly nature. Thus this communication summarized and highlights the preferred feedstock for optimized biochar yield along with the factor influencing the production. This review provides a close view on biochar activation approaches and synthesis techniques. The application of biochar in environmental remediation, composting, as a catalyst, and in energy storage has been reviewed. These informative findings were supported with an overview of lifecycle and techno-economical assessments in the production of these carbon based catalysts. Integrated closed loop approaches towards biochar generation with lesser/zero landfill waste for safeguarding the environment has also been discussed. Lastly the research gaps were identified and the future perspectives have been elucidated.

Sustainable management of plastic wastes in COVID-19 pandemic: The biochar solution.

To prevent the COVID-19 transmission, personal protective equipment (PPE) and packaging materials have been extensively used but often managed inappropriately, generating huge amount of plastic waste. In this review, we comprehensively discussed the plastic products utilized and the types and amounts of plastic waste generated since the outbreak of COVID-19, and reviewed the potential treatments for these plastic wastes. Upcycling of plastic waste into biochar was addressed from the perspectives of both environmental protection and practical applications, which can be verified as promising materials for environmental protections and energy storages. Moreover, novel upcycling of plastic waste into biochar is beneficial to mitigate the ubiquitous plastic pollution, avoiding harmful impacts on human and ecosystem through direct and indirect micro-/nano-plastic transmission routes, and achieving the sustainable plastic waste management for value-added products, simultaneously. This suggests that the plastic waste could be treated as a valuable resource in an advanced and green manner.

Are microplastics destabilizing the global network of terrestrial and aquatic ecosystem services?

Plastic has created a new man-made ecosystem called plastisphere. The plastic pieces including microplastics (MPs) and nanoplastics (NPs) have emerged as a global concern due to their omnipresence in ecosystems and their ability to interact with the biological systems. Nevertheless, the long-term impacts of MPs on biotic and abiotic resources are not completely understood, and existing evidence suggests that MPs are hazardous to various keystones species of the global biomes. MP-contaminated ecosystems show reduced floral and faunal biomass, productivity, nitrogen cycling, oxygen-generation and carbon sequestration, suggesting that MPs have already started affecting ecological biomes. However, not much is known about the influence of MPs towards the ecosystem services (ESs) cascade and its correlation with the biodiversity loss. MPs are perceived as a menace to the global ecosystems, but their possible impacts on the provisional, regulatory, and socio-economic ESs have not been extensively studied. This review investigates not only the potentiality of MPs to perturb the functioning of terrestrial and aquatic biomes, but also the associated social, ecological and economic repercussions. The possible long-term fluxes in the ES network of terrestrial and aquatic niches are also discussed.

Energy, environmental, economic and social equity (4E) pressures of COVID-19 vaccination mismanagement: A global perspective.

Vaccination now offers a way to resolve the COVID-19 pandemic. However, it is critical to recognise the full energy, environmental, economic and social equity (4E) impacts of the vaccination life cycle. The full 4E impacts include the design and trials, order management, material preparation, manufacturing, cold chain logistics, low-temperature storage, crowd management and end-of-life waste management. A life cycle perspective is necessary for sustainable vaccination management because a prolonged immunisation campaign for COVID-19 is likely. The impacts are geographically dispersed across sectors and regions, creating real and virtual 4E footprints that occur at different timescales. Decision-makers in industry and governments have to act, unify, resolve, and work together to implement more sustainable COVID-19 vaccination management globally and locally to minimise the 4E footprints. Potential practices include using renewable energy in production, storage, transportation and waste treatment, using better product design for packaging, using the Internet of Things (IoT) and big data analytics for better logistics, using real-time database management for better tracking of deliveries and public vaccination programmes, and using coordination platforms for more equitable vaccine access. These practices raise global challenges but suggest solutions with a 4E perspective, which could mitigate the impacts of global vaccination campaigns and prepare sustainably for future pandemics and global warming.

A Risk Assessment Scheme of Infection Transmission Indoors Incorporating the Impact of Resuspension.

A new risk assessment scheme was developed to quantify the impact of resuspension to infection transmission indoors. Airborne and surface pathogenic particle concentration models including the effect of two major resuspension scenarios (airflow-induced particle resuspension [AIPR] and walking-induced particle resuspension [WIPR]) were derived based on two-compartment mass balance models and validated against experimental data found in the literature. The inhalation exposure to pathogenic particles was estimated using the derived airborne concentration model, and subsequently incorporated into a dose-response model to assess the infection risk. Using the proposed risk assessment scheme, the influences of resuspension towards indoor infection transmission were examined by two hypothetical case studies. In the case of AIPR, the infection risk increased from 0 to 0.54 during 0-0.5 hours and from 0.54 to 0.57 during 0.5-4 hours. In the case of WIPR, the infection risk increased from 0 to 0.87 during 0-0.5 hours and from 0.87 to 1 during 0.5-4 hours. Sensitivity analysis was conducted based on the design-of-experiments method and showed that the factors that are related to the inspiratory rate of viable pathogens and pathogen virulence have the most significant effect on the infection probability under the occurrence of AIPR and WIPR. The risk assessment scheme could serve as an effective tool for the risk assessment of infection transmission indoors.

Modeling and experiments of the adhesion force distribution between particles and a surface.

Due to the existence of surface roughness in real surfaces, the adhesion force between particles and the surface where the particles are deposited exhibits certain statistical distributions. Despite the importance of adhesion force distribution in a variety of applications, the current understanding of modeling adhesion force distribution is still limited. In this work, an adhesion force distribution model based on integrating the root-mean-square (RMS) roughness distribution (i.e., the variation of RMS roughness on the surface in terms of location) into recently proposed mean adhesion force models was proposed. The integration was accomplished by statistical analysis and Monte Carlo simulation. A series of centrifuge experiments were conducted to measure the adhesion force distributions between polystyrene particles (146.1 ± 1.99 µm) and various substrates (stainless steel, aluminum and plastic, respectively). The proposed model was validated against the measured adhesion force distributions from this work and another previous study. Based on the proposed model, the effect of RMS roughness distribution on the adhesion force distribution of particles on a rough surface was explored, showing that both the median and standard deviation of adhesion force distribution could be affected by the RMS roughness distribution. The proposed model could predict both van der Waals force and capillary force distributions and consider the multiscale roughness feature, greatly extending the current capability of adhesion force distribution prediction.

Trigeneration based on the pyrolysis of rural waste in India: Environmental impact, economic feasibility and business model innovation.

Pyrolysis-based waste-to-bioenergy development has the potential to resolve some of the major challenges facing rural communities in India such as poor electrification, household air pollution, and farmland degradation and contamination. Existing understanding and analysis of the economic feasibility and environmental impact of bioenergy deployment in rural areas is limited by parameter uncertainties, and relevant business model innovation following economic evaluation is even scarcer. This paper uses findings from a new field survey of 1200 rural households to estimate the economic feasibility and environmental impact of a pyrolysis-based bioenergy trigeneration development that was designed to tackle these challenges. Based on the survey results, probability distributions were constructed and used to supply input parameters for cost-benefit analysis and life cycle assessment. Monte Carlo simulation was applied to characterise the uncertainties of economic feasibility and environmental impact accounting. It was shown that the global warming potential of the development was 350 kg of CO2-eq per capita per annum. Also, the survey identified a significant mismatch between feedstock prices considered in the literature and prices asked for by the surveyed villagers. The results of the cost-benefit analysis and life cycle assessment were then applied to propose two novel business models inspired by the Business Model Canvas, which had the potential to achieve up to 90 % economic profitability and result in a benefit-cost ratio of 1.35-1.75. This is the first study achieving combined environmental and economic analysis and business model innovation for rural bioenergy production in developing countries.

Mathematical models for the van der Waals force and capillary force between a rough particle and surface.

The capability of predicting the adhesion forces between a rough particle and surface including the van der Waals force and capillary force is important for modeling various processes involving particle surface retention and resuspension. On the basis of the fractal theory describing the behavior of multiple roughness scales and the Gaussian roughness distribution, a set of mathematical models for the van der Waals force and capillary force is proposed. The proposed models provide the adhesion force predictions in good agreement with the existing experimental data and converge to the previous classical solutions of the adhesion forces between a smooth particle and surface as the roughness goes to zero. The influences of roughness for the combination of particle and surface, relative humidity (RH), contact angle, and Hurst exponent toward the adhesion forces are examined using the proposed models. The decline mode of the adhesion force with surface roughness and contact angle, as well as the increase mode with RH and the Hurst exponent are reasonably predicted by the proposed models. The comparison between the proposed models and those from the existing studies is also performed, which shows the similarities and differences between the proposed models and the existing models.

Artificial intelligence and machine learning for smart bioprocesses.

In recent years, the digital transformation of bioprocesses, which focuses on interconnectivity, online monitoring, process automation, artificial intelligence (AI) and machine learning (ML), and real-time data acquisition, has gained considerable attention. AI can systematically analyze and forecast high-dimensional data obtained from the operating dynamics of bioprocess, allowing for precise control and synchronization of the process to improve performance and efficiency. Data-driven bioprocessing is a promising technology for tackling emerging challenges in bioprocesses, such as resource availability, parameter dimensionality, nonlinearity, risk mitigation, and complex metabolisms. This special issue entitled "Machine Learning for Smart Bioprocesses (MLSB-2022)" was conceptualized to incorporate some of the recent advances in applications of emerging tools such as ML and AI in bioprocesses. This VSI: MLSB-2022 contains 23 manuscripts, and summarizes the major findings that can serve as a valuable resource for researchers to learn major advances in applications of ML and AI in bioprocesses.

Review of biochar production via crop residue pyrolysis: Development and perspectives.

Worldwide surge in crop residue generation has necessitated developing strategies for their sustainable disposal. Pyrolysis has been widely adopted to convert crop residue into biochar with bio-oil and gas being two co-products. The review adopts a whole system philosophy and systematically summarises up-to-date knowledge of crop residue pyrolysis processes, influential factors, and biochar applications. Essential process design tools for biochar production e.g., cost-benefit analysis, life cycle assessment, and machine learning methods are also reviewed, which has often been overlooked in prior reviews. Important aspects include (a) correlating techno-economics of biochar production with crop residue compositions, (b) process operating conditions and management strategies, (c) biochar applications including soil amendment, fuel displacement, catalytic usage, etc., (d) data-driven modelling techniques, (e) properties of biochar, and (f) climate change mitigation. Overall, the review will support the development of application-oriented process pipelines for crop residue-based biochar.

Integration of anaerobic digestion with heat Pump: Machine learning-based technical and environmental assessment.

Anaerobic digestion (AD)-based biogas production mitigates the environmental footprint of organic wastes (e.g., food waste and sewage sludge) and facilitates a circular economy. The work proposed an integrated system where the thermal energy demand of an AD is supplied using an air source heat pump (ASHP). The proposed system is compared to a baseline system, where the thermal energy is supplied by a natural gas-based heating system. Several machine learning models are developed for predicting biogas production, among which the Gaussian Process Regression (GPR) showed a superior performance (R2 = 0.84 and RMSE = 0.0755 L gVS-1 day-1). The GPR model further informed a thermodynamic model of the ASHP, which revealed the maximum biogas yield to be approximately 0.585 L.gVS-1.day-1 at an optimal temperature of 55 °C (thermophilic). Subsequently, life cycle assessment showed that ASHP-based AD heating systems achieved 28.1 % (thermophilic) and 36.8 % (mesophilic) carbon abatement than the baseline system.

Metagenomic insights into the inhibitory mechanisms of Cu on fermentative hydrogen production.

Cu is widely present in the feedstocks of dark fermentation, which can inhibit H2 production efficiency of the process. However, current understanding on the inhibitory mechanisms of Cu, especially the microbiological mechanism, is still lacking. This study investigated the inhibitory mechanisms of Cu2+ on fermentative hydrogen production by metagenomics sequencing. Results showed that the exposure to Cu2+ reduced the abundances of high-yielding hydrogen-producing genera (e.g. Clostridium sensu stricto), and remarkably down-regulated the genes involved in substrate membrane transport (e.g., gtsA, gtsB and gtsC), glycolysis (e.g. PK, ppgK and pgi-pmi), and hydrogen formation (e.g. pflA, fdoG, por and E1.12.7.2), leading to significant inhibition on the process performances. The H2 yield was reduced from 1.49 mol H2/mol-glucose to 0.59 and 0.05 mol H2/mol-glucose upon exposure to 500 and 1000 mg/L of Cu2+, respectively. High concentrations of Cu2+ also reduced the rate of H2 production and prolonged the H2-producing lag phase.

Review of explainable machine learning for anaerobic digestion.

Anaerobic digestion (AD) is a promising technology for recovering value-added resources from organic waste, thus achieving sustainable waste management. The performance of AD is dictated by a variety of factors including system design and operating conditions. This necessitates developing suitable modelling and optimization tools to quantify its off-design performance, where the application of machine learning (ML) and soft computing approaches have received increasing attention. Here, we succinctly reviewed the latest progress in black-box ML approaches for AD modelling with a thrust on global and local model interpretability metrics (e.g., Shapley values, partial dependence analysis, permutation feature importance). Categorical applications of the ML and soft computing approaches such as what-if scenario analysis, fault detection in AD systems, long-term operation prediction, and integration of ML with life cycle assessment are discussed. Finally, the research gaps and scopes for future work are summarized.

Machine learning assisted prediction of biochar yield and composition via pyrolysis of biomass.

Biochar production via pyrolysis of various organic waste has potential to reduce dependence on conventional energy sources and mitigate global warming potential. Existing models for predicting biochar yield and compositions are computationally-demanding, complex, and have low accuracy for extrapolative scenarios. Here, two data-driven machine learning models based on Multi-Layer Perceptron Neural Network and Artificial Neuro-Fuzzy Inference System are developed. The data-driven models predict biochar yield and compositions for a variety of input feedstock compositions and pyrolysis process conditions. Feature importance assessment of the input dataset revealed their competitive significance for predicting biochar yield and compositions. Overall, the predictive accuracy of the models was up to 12.7% better than the Random Forest and eXtreme Gradient Boosting machine learning algorithms reported in the literature. The models developed are valuable for environmental footprint assessment of biochar production and rapid system optimization.

Economic and environmental assessment of organic waste to biomethane conversion.

Biomethane and biofertilizer production by anaerobic co-digestion of organic waste serves a promising method for reducing the environmental footprint of organic waste management. This study evaluated the techno-economic feasibility and environmental impacts of organic waste to biomethane development in Glasgow, UK using net present value (NPV) analysis and life cycle assessment. Four different biogas upgrading technologies (pressurized water scrubbing, chemical scrubbing, membrane separation, and pressure swing adsorption) were compared. The membrane separation technology-based biomethane production meets 0.8% of the gas demand for Glasgow households with a conversion efficiency of 83%. The organic waste to biomethane development saved up to 264 kg CO2-eq annually per tonne of waste treated, with an NPV ranged between £-9.0 million and £-12.0 million based on the upgrading technology. High costs for waste collection and transportation are primarily responsible for the negative NPV. Carbon taxes between £31.30 and £58.02 per tonne of CO2 are needed for economically viable biomethane production.

Life cycle assessment of waste-to-hydrogen systems for fuel cell electric buses in Glasgow, Scotland.

Waste-to-hydrogen (WtH) technologies are proposed as a dual-purpose method for simultaneous non-fossil-fuel based hydrogen production and sustainable waste management. This work applied the life cycle assessment approach to evaluate the carbon saving potential of two main WtH technologies (gasification and fermentation) in comparison to the conventional hydrogen production method of steam methane reforming (SMR) powering fuel cell electric buses in Glasgow. It was shown that WtH technologies could reduce CO2-eq emissions per kg H2 by 50-69% as compared to SMR. Gasification treating municipal solid waste and waste wood had global warming potentials of 4.99 and 4.11 kg CO2-eq/kg H2 respectively, which were lower than dark fermentation treating wet waste at 6.6 kg CO2-eq/kg H2 and combined dark and photo fermentation at 6.4 kg CO2-eq/kg H2. The distance emissions of WtH-based fuel cell electric bus scenarios were 0.33-0.44 kg CO2-eq/km as compared to 0.89 kg CO2-eq/km for the SMR-based scenario.

Interpretable machine learning to model biomass and waste gasification.

Machine learning has been regarded as a promising method to better model thermochemical processes such as gasification. However, their black box nature can limit how much one can trust and learn from the developed models. Here seven different machine learning methods have been adopted to model the gasification of biomass and waste across a wide range of operating conditions. Gradient boosting regression has been found to outperform the other model types with a coefficient of determination (R2) of 0.90 when averaged across ten key gasification outputs. Global and local model interpretability methods have been used to illuminate the developed black box models. The studied models were most strongly influenced by the feedstock's particle size and the type of gasifying agent employed. By combining global and local interpretability methods, the understanding of black box models has been improved. This allows policy makers and investors to make more educated decisions about gasification process design.