The pursuit of carbon neutrality confronts the twofold challenge of meeting energy demands and reducing pollution. This review article examines the potential of gasifying plastic waste and biomass as innovative, sustainable sources for hydrogen production, a critical element in achieving environmental reform. Addressing the problem of greenhouse gas emissions, the work highlights how the co-gasification of these feedstocks could contribute to environmental preservation by reducing waste and generating clean energy. Through an analysis of current technologies, the potential for machine learning to refine gasification for optimal hydrogen production is revealed. Additionally, hydrogen storage solutions are evaluated for their importance in creating a viable, sustainable energy infrastructure. The economic viability of these production methods is critically assessed, providing insights into both their cost-effectiveness and ecological benefits. Findings indicate that machine learning can significantly improve process efficiencies, thereby influencing the economic and environmental aspects of hydrogen production. Furthermore, the study presents the advancements in these technologies and their role in promoting a transition to a green economy and circular energy practices. Ultimately, the review delineates how integrating hydrogen production from unconventional feedstocks, bolstered by machine learning and advanced storage, can contribute to a sustainable and pollution-free future.
In recent years, food waste has been a global concern that contributes to climate change. To deal with the rising impacts of climate change, in Hong Kong, food waste is converted into electricity in the framework of low-carbon approach. This work provides an overview of the conversion of food waste into electricity to achieve carbon neutrality. The production of methane and electricity from waste-to-energy (WTE) conversion are determined. Potential income from its sale and environmental benefits are also assessed quantitatively and qualitatively. It was found that the electricity generation from the food waste could reach 4.33 × 109 kWh annually, avoiding equivalent electricity charge worth USD 3.46 × 109 annually (based on US' 8/kWh). An equivalent CO2 mitigation of 9.9 × 108 kg annually was attained. The revenue from its electricity sale in market was USD 1.44×109 in the 1st year and USD 4.24 ×109 in the 15th year, respectively, according to the projected CH4 and electricity generation. The modelling study indicated that the electricity production is 0.8 kWh/kg of landfilled waste. The food waste could produce electricity as low as US' 8 per kW ∙ h. In spite of its promising results, there are techno-economic bottlenecks in commercial scale production and its application at comparable costs to conventional fossil fuels. Issues such as high GHG emissions and high production costs have been determined to be resolved later. Overall, this work not only leads to GHG avoidance, but also diversifies energy supply in providing power for homes in the future.
Microalgae is a sustainable alternative source to traditional proteins. Existing pretreatment methods for protein extraction from microalgae still lack scalability, are uneconomical and inefficient. Herein, high shear mixing (HSM) was applied to disrupt the rigid cell walls and was found to assist in protein release from microalgae. This study integrates HSM in liquid biphasic system with seven parameters being investigated on extraction efficiency (EE) and protein yield (Y). The highest EE and Y obtained are 96.83 ± 0.47 % and 40.98 ± 1.27 %, respectively, using 30% w/v K3PO4 salt, 60 % v/v alcohol, volume ratio of 1:1 and 0.5 % w/v biomass loading under shearing rate of 16,000 rpm for 1 min.
The insect larvae Protaetia brevitarsis seulensis have recently been researched as a nutritious food source and concentrated on their environmental impacts. Therefore, their gut microbiota has been studied to elucidate their effects and roles on the environment. Of the abundance of bacterial genus identified based on the 16S rRNA genes from isolates of the gut of insect larva Protaetia brevitarsis seulensis, six of the prominent genus were identified as Bacillus (40.2%), Cellulosimicrobium (33.5%), Microbacterium (2.8%), Streptomyces (3%), Krasilnikoviella (17.5%), and Isoptericola (3%) and their similarity of 16S rRNA blast changed from 99 to 100%. Cellulosimicrobium protaetiae BI34T showed strong denitrification and cellulose degradation activity. The newly complete genome sequence of BI34T and the genomes of five species was published in the genus Cellulosimicrobium with emphasis on the denitrification and secondary metabolite genes. In order to elucidate the relationship between the strain BI34T and the host insect larva, the whole-genome sequence was analyzed and compared with the genomes of five strains in the same genus, Cellulosimicrobium, loaded from GenBank. Our results revealed the composition of the gut microbiota of the insect larvae and analyzed the genomic data for the new strain to predict its characteristics and to understand the nitrogen metabolism pathway.
Multidrug resistance to pathogens has posed a severe threat to public health. The threat could be addressed by antimicrobial peptides (AMPs) with broad-spectrum suppression. In this study, Brevibacillus halotolerans 7WMA2, isolated from marine sediment, produced AMPs against Gram-positive and Gram-negative bacteria. The AMPs were precipitated by ammonium sulfate 30% (w/v) from culture broth and dialyzed by a 1 kDa membrane. Tryptone Soy Agar (TSA) was used for the cultivation and resulted in the largest bacteria-inhibiting zones under aerobic conditions at 25 °C, 48 h. An SDS-PAGE gel overlay test revealed that strain 7WMA2 could produce AMPs of 5-10 kDa and showed no degradation when held at 121 °C for 30 min at a wide pH 2-12 range. The AMPs did not cause toxicity to HeLa cells with concentrations up to 500 µg/mL while increasing the arbitrary unit up to eight times. The study showed that the AMPs produced were unique, with broad-spectrum antimicrobial ability.
Fucoxanthin is a carotenoid that possesses various beneficial medicinal properties for human well-being. However, the current extraction technologies and quantification techniques are still lacking in terms of cost validation, high energy consumption, long extraction time, and low yield production. To date, artificial intelligence (AI) models can assist and improvise the bottleneck of fucoxanthin extraction and quantification process by establishing new technologies and processes which involve big data, digitalization, and automation for efficiency fucoxanthin production. This review highlights the application of AI models such as artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS), capable of learning patterns and relationships from large datasets, capturing non-linearity, and predicting optimal conditions that significantly impact the fucoxanthin extraction yield. On top of that, combining metaheuristic algorithm such as genetic algorithm (GA) can further improve the parameter space and discovery of optimal conditions of ANN and ANFIS models, which results in high R2 accuracy ranging from 98.28% to 99.60% after optimization. Besides, AI models such as support vector machine (SVM), convolutional neural networks (CNNs), and ANN have been leveraged for the quantification of fucoxanthin, either computer vision based on color space of images or regression analysis based on statistical data. The findings are reliable when modeling for the concentration of pigments with high R2 accuracy ranging from 66.0% - 99.2%. This review paper has reviewed the feasibility and potential of AI for the extraction and quantification purposes, which can reduce the cost, accelerate the fucoxanthin yields, and development of fucoxanthin-based products.
The rapid expansion of industrialization and continuous population growth have caused a steady increase in energy consumption. Despite using renewable energy, such as bioethanol, to replace fossil fuels had been strongly promoted, however the outcomes were underwhelming, resulting in excessive greenhouse gases (GHG) emissions. Microalgal biochar, as a carbon-rich material produced from the pyrolysis of biomass, provides a promising solution for achieving net zero emission. By utilizing microalgal biochar, these GHG emissions can be captured and stored efficiently. It also enhances soil fertility, improves water retention, and conduct bioremediation in agriculture and environmental remediation field. Moreover, incorporating microalgal biochar into a zero-waste biorefinery could boost the employ of biomass feedstocks effectively to produce valuable bioproducts while minimizing waste. This contributes to sustainability and aligns with the concepts of a circular bioeconomy. In addition, some challenges like commercialization and standardization will be addressed in the future.
Surfactants have always been a prominent chemical that is useful in various sectors (e.g., cleaning agent production industry, textile industry and painting industry). This is due to the special ability of surfactants to reduce surface tension between two fluid surfaces (e.g., water and oil). However, the current society has long omitted the harmful effects of petroleum-based surfactants (e.g., health issues towards humans and reducing cleaning ability of water bodies) due to their usefulness in reducing surface tension. These harmful effects will significantly damage the environment and negatively affect human health. As such, there is an urgency to secure environmentally friendly alternatives such as glycolipids to reduce the effects of these synthetic surfactants. Glycolipids is a biomolecule that shares similar properties with surfactants that are naturally synthesized in the cell of living organisms, glycolipids are amphiphilic in nature and can form micelles when glycolipid molecules clump together, reducing surface tension between two surfaces as how a surfactant molecule is able to achieve. This review paper aims to provide a comprehensive study on the recent advances in bacteria cultivation for glycolipids production and current lab scale applications of glycolipids (e.g., medical and waste bioremediation). Studies have proven that glycolipids are effective anti-microbial agents, subsequently leading to an excellent anti-biofilm forming agent. Heavy metal and hydrocarbon contaminated soil can also be bioremediated via the use of glycolipids. The major hurdle in the commercialization of glycolipid production is that the cultivation stage and downstream extraction stage of the glycolipid production process induces a very high operating cost. This review provides several solutions to overcome this issue for glycolipid production for the commercialization of glycolipids (e.g., developing new cultivating and extraction techniques, using waste as cultivation medium for microbes and identifying new strains for glycolipid production). The contribution of this review aims to serve as a future guideline for researchers that are dealing with glycolipid biosurfactants by providing an in-depth review on the recent advances of glycolipid biosurfactants. By summarizing the points discussed as above, it is recommended that glycolipids can substitute synthetic surfactants as an environmentally friendly alternative.
In the effort of isolating novel microbial species, the strain PL0132T was isolated from a fallen leaf under fresh water at a stream, which glided when grown on a tap water medium (without nutrients). The strain was determined to be Gram-negative, strictly aerobic, and rod-shaped, which grew optimally at 25 °C, pH 6-7, and the strain tolerates 1% (w/v) NaCl concentration. The complete genome of strain PL0132T comprises one contig with a sequencing depth of 76×, consisting of 8,853,064 base pairs and the genomic DNA G + C content was 46.7% (genome). 16S rRNA gene sequence analysis revealed that strain PL0132T represents a member of the phylum Bacteroidetes and is affiliated with the genus Spirosoma. Based on genomic, phenotypic, and chemotaxonomic characteristics, the strain PL0132T represents a novel species of the genus Spirosoma, for which the name Spirosoma foliorum sp. nov. is proposed (= KCTC 72228 T = InaCC B1447T).
Digitalization and sustainability have been considered as critical elements in tackling a growing problem of solid waste in the framework of circular economy (CE). Although digitalization can enhance time-efficiency and/or cost-efficiency, their end-results do not always lead to sustainability. So far, the literatures still lack of a holistic view in understanding the development trends and key roles of digitalization in waste recycling industry to benefit stakeholders and to protect the environment. To bridge this knowledge gap, this work systematically investigates how leveraging digitalization in waste recycling industry could address these research questions: (1) What are the key problems of solid waste recycling? (2) How the trends of digitalization in waste management could benefit a CE? (3) How digitalization could strengthen waste recycling industry in a post-pandemic era? While digitalization boosts material flows in a CE, it is evident that utilizing digital solutions to strengthen waste recycling business could reinforce a resource-efficient, low-carbon, and a CE. In the Industry 4.0 era, digitalization can add 15% (about USD 15.7 trillion) to global economy by 2030. As digitalization grows, making the waste sector shift to a CE could save between 30% and 35% of municipalities' waste management budget. With digitalization, a cost reduction of 3.6% and a revenue increase of 4.1% are projected annually. This would contribute to USD 493 billion in an increasing revenue yearly in the next decade. As digitalization enables tasks to be completed shortly with less manpower, this could save USD 421 billion annually for the next decade. With respect to environmental impacts, digitalization in the waste sector could reduce global CO2 emissions by 15% by 2030 through technological solutions. Overall, this work suggests that digitalization in the waste sector contributes net-zero emission to a digital economy, while transitioning to a sustainable world as its social impacts.
The generation of microplastics (MPs) has increased recently and become an emerging issue globally. Due to their long-term durability and capability of traveling between different habitats in air, water, and soil, MPs presence in freshwater ecosystem threatens the environment with respect to its quality, biotic life, and sustainability. Although many previous works have been undertaken on the MPs pollution in the marine system recently, none of the study has covered the scope of MPs pollution in the freshwater. To consolidate scattered knowledge in the literature body into one place, this work identifies the sources, fate, occurrence, transport pathways, and distribution of MPs pollution in the aquatic system with respect to their impacts on biotic life, degradation, and detection techniques. This article also discusses the environmental implications of MPs pollution in the freshwater ecosystems. Certain techniques for identifying MPs and their limitations in applications are presented. Through a literature survey of over 276 published articles (2000-2023), this study presents an overview of solutions to the MP pollution, while identifying research gaps in the body of knowledge for further work. It is conclusive from this review that the MPs exist in the freshwater due to an improper littering of plastic waste and its degradation into smaller particles. Approximately 15-51 trillion MP particles have accumulated in the oceans with their weight ranging between 93,000 and 236,000 metric ton (Mt), while about 19-23 Mt of plastic waste was released into rivers in 2016, which was projected to increase up to 53 Mt by 2030. A subsequent degradation of MPs in the aquatic environment results in the generation of NPs with size ranging from 1 to 1000 nm. It is expected that this work facilitates stakeholders to understand the multi-aspects of MPs pollution in the freshwater and recommends policy actions to implement sustainable solutions to this environmental problem.
One of potential inhibitors which is widely used for the clinical treatment of COVID-19 in comorbid patients is Angiostensin Converting Enzyme-1 (ACE1) inhibitor. A safer peptide-based ACE1 inhibitor derived from salmon skin collagen, that is considered as the by-product of the fish processing industry have been investigated in this study. The inhibitory activity against ACE1 was examined using in vitro and in silico methods. In vitro analysis includes the extraction of acid-soluble collagen, characterization using FTIR, Raman, UV-Vis, XRD, cytotoxicity assay, and determination of inhibition against ACE1. In silico method visualizes binding affinity, molecular interaction, and inhibition type of intact collagen and active peptides derived from collagen against ACE1 using molecular docking. The results of FTIR spectra detected amide functional groups (A, B, I, II, III) and imine proline/hydroxyproline, while the results of Raman displayed peak absorption of amide I, amide III, proline/hydroxyproline ring, phenylalanine, and protein backbone. Furthermore, UV-Vis spectra showed typical collagen absorption at 230 nm and based on XRD data, the chain types in the samples were α-helix. ACE1 inhibition activity was obtained in a concentration-dependent manner where the highest was 82.83% and 85.84% at concentrations of 1000, and 2000 µg/mL, respectively, and showed very low cytotoxicity at the concentration less than 1000 µg/mL. In silico study showed an interaction between ACE1 and collagen outside the active site with the affinity of - 213.89 kcal/mol. Furthermore, the active peptides of collagen displayed greater affinity compared to lisinopril, namely HF (His-Phe), WYT (Trp-Tyr-Thr), and WF (Trp-Phe) of - 11.52; - 10.22; - 9.58 kcal/mol, respectively. The salmon skin-derived collagen demonstrated ACE1 inhibition activity with a non-competitive inhibition mechanism. In contrast, the active peptides were predicted as potent competitive inhibitors against ACE1. This study indicated that valorization of fish by-product can lead to the production of a promising bioactive compound to treat COVID-19 patient with diabetic comorbid.
Biochar, derived from unused biomass, is widely considered for its potential to deal with climate change problems. Global interest in biochar is attributed to its ability to sequester carbon in soil and to remediate aquatic environment from water pollution. As soil conditioner and/or adsorbent, biochar offers opportunity through a circular economy (CE) paradigm. While energy transition continues, progress toward low-emissions materials accelerates their advance towards net-zero emissions. However, none of existing works addresses CE-based biochar management to achieve carbon neutrality. To reflect its novelty, this work provides a critical overview of challenges and opportunities for biochar to promote CE and carbon neutrality. This article also offers seminal perspectives about strengthening biomass management through CE and resource recovery paradigms, while exploring how the unused biomass can promote net zero emissions in its applications. By consolidating scattered knowledge in the body of literature into one place, this work uncovers new research directions to close the loops by implementing the circularity of biomass resources in various fields. It is conclusive from a literature survey of 113 articles (2003-2023) that biomass conversion into biochar can promote net zero emissions and CE in the framework of the UN Sustainable Development Goals (SDGs). Depending on their physico-chemical properties, biochar can become a suitable feedstock for CE. Biochar application as soil enrichment offsets 12% of CO2 emissions by land use annually. Adding biochar to soil can improve its health and agricultural productivity, while minimizing about 1/8 of CO2 emissions. Biochar can also sequester CO2 in the long-term and prevent the release of carbon back into the atmosphere after its decomposition. This practice could sequester 2.5 gigatons (Gt) of CO2 annually. With the global biochar market reaching USD 368.85 million by 2028, this work facilitates biochar with its versatile characteristics to promote carbon neutrality and CE applications.
Gelatin hydrogel is widely employed in various fields, however, commercially available gelatin hydrogels are mostly derived from mammalian which has many disadvantages due to the supply and ethical issues. In this study, the properties of hydrogels from fish-derived collagen fabricated with varying Glutaraldehyde (GA) determined. The antidiabetic properties of salmon gelatin (SG) and tilapia gelatin (TG) was also evaluated against α-glucosidase. Glutaraldehyde-crosslinked salmon gelatin and tilapia gelatin were used, and compared with different concentrations of GA by 0.05 %, 0.1 %, and 0.15 %. Water absorbency, swelling, porosity, pore size and water retention of the hydrogels were dependent on the degree of crosslinking. The synthesis of hydrogels was confirmed by FTIR study. Scanning electron microscope (SEM) observation showed that all hydrogels have a porous structure with irregular shapes and heterogeneous morphology. Performance tests showed that gelatin-GA 0.05 % mixture had the best performance. Antidiabetic bioactivity in vitro and in silico tests showed that the active peptides of SG and TG showed a high binding affinity to α-glucosidase enzyme. In conclusion, SG and TG cross-linked GA 0.05 % have the potential as an antidiabetic agent and as a useful option over mammalian-derived gelatin.
This study investigated physico-chemical interactions among Cu(II), biogenic materials, and Fe2O3 in a continuous-flow biofilm reactor system under a well-controlled environment. The effects of Fe2O3 and bacterial biofilms on the distribution of Cu(II) in a simulated aquatic environment were studied. To control biological and abiotic elements in the marine environment, a biofilm reactor was designed to understand the metal speciation of Cu(II) and its distribution. The reactor consisted of a biofilm chamber equipped with glass slides for biofilms attachment. Due to its ability to grow as biofilm in the medium, Pseudomonas atlantica was cultivated to adsorb trace Cu(II) to attached and suspended cells. It was found that biofilms with 170-285 mequiv chemical oxygen demand (COD) concentration/m2 of total oxidizable materials accelerated the Cu(II) adsorption to the surface of the reactor significantly by a factor of five. A significant inhibition to the bacterial growth took place (p ≤ 0.05; t-test) when Cu(II) concentration was higher than 0.5 mg/L. In the absence of Cu(II), bacterial cells grew normally to 0.075 of optical density (OD). However, at the Cu(II) concentration of 0.2 mg/L, the cells grew to a lower OD of 0.58. The presence of glycine and EDTA substantially reduced the toxicity of Cu(II) on bacterial growth (p ≤ 0.05; paired t-test). Their complexation with Cu(II) rendered the metal ions less available to bacterial cells. This implies that the Fe2O3 and bacterial biofilm affected Cu(II) distribution and speciation in the aquatic environment.
Production and extraction systems of algal protein and handling process of functional food ingredients need to control several parameters such as temperature, pH, intensity, and turbidity. Many researchers have investigated the Internet of Things (IoT) approach for enhancing the yield of microalgae biomass and machine learning for identifying and classifying microalgae. However, there have been few specific studies on using IoT and artificial intelligence (AI) for production and extraction of algal protein as well as functional food ingredients processing. In order to improve the production of algal protein and functional food ingredients, the implementation of smart system is a must to have real-time monitoring, remote control system, quick response to sudden events, prediction and characterisation. Techniques of IoT and AI are expected to help functional food industries to have a big breakthrough in the future. Manufacturing and implementation of beneficial smart systems are important to provide convenience and to increase the efficiency of work by using the interconnectivity of IoT devices to have good capturing, processing, archiving, analyzing, and automation. This review investigates the possibilities of implementation of IoT and AI in production and extraction of algal protein and processing of functional food ingredients.
Soy isoflavone extracts are widely researched for their distinctive potential in contributing to various functional foods. The research work focuses on testing the toxicity of purified soy isoflavone extracts in mice models. With an agreement of the animal ethics, acute toxicity is firstly used to screen the effects of test compounds in mice for therapeutic purposes. Moreover, tests were conducted on BALB/c for estrogen in vivo and MCF7 for in vitro, screening active protection of liver cells, lipid peroxidation and scavenging free radicals 2,2-diphenyl-1-picrylhydrazyl (DPPH). Genistin and daidzin were found to be the two major compounds accounting for 47% and 35% of total purified soy isoflavones. The acute toxicity test results exhibited no effect against physiological accretion of BALB/c after 7-day administration with the given dose of 10 g/kgBW. Moreover, modified E-screen assay on MCF7 cells proved that the estrogen of isoflavone extracts induces cell proliferation by 15% compared with other non-steroid culture techniques. Therefore, this research contributes to helping researchers apply soy isoflavones in functional food, to alleviate the difficulties in menopausal symptoms for women in the future.
SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13197-022-05491-4.
The identification of microalgae species is an important tool in scientific research and commercial application to prevent harmful algae blooms (HABs) and recognizing potential microalgae strains for the bioaccumulation of valuable bioactive ingredients. The aim of this study is to incorporate rapid, high-accuracy, reliable, low-cost, simple, and state-of-the-art identification methods. Thus, increasing the possibility for the development of potential recognition applications, that could identify toxic-producing and valuable microalgae strains. Recently, deep learning (DL) has brought the study of microalgae species identification to a much higher depth of efficiency and accuracy. In doing so, this review paper emphasizes the significance of microalgae identification, and various forms of machine learning algorithms for image classification, followed by image pre-processing techniques, feature extraction, and selection for further classification accuracy. Future prospects over the challenges and improvements of potential DL classification model development, application in microalgae recognition, and image capturing technologies are discussed accordingly.
Identification of microalgae species is of importance due to the uprising of harmful algae blooms affecting both the aquatic habitat and human health. Despite this occurence, microalgae have been identified as a green biomass and alternative source due to its promising bioactive compounds accumulation that play a significant role in many industrial applications. Recently, microalgae species identification has been conducted through DNA analysis and various microscopy techniques such as light, scanning electron, transmission electron, and atomic force -microscopy. The aforementioned procedures have encouraged researchers to consider alternate ways due to limitations such as costly validation, requiring skilled taxonomists, prolonged analysis, and low accuracy. This review highlights the potential innovations in digital microscopy with the incorporation of both hardware and software that can produce a reliable recognition, detection, enumeration, and real-time acquisition of microalgae species. Several steps such as image acquisition, processing, feature extraction, and selection are discussed, for the purpose of generating high image quality by removing unwanted artifacts and noise from the background. These steps of identification of microalgae species is performed by reliable image classification through machine learning as well as deep learning algorithms such as artificial neural networks, support vector machines, and convolutional neural networks. Overall, this review provides comprehensive insights into numerous possibilities of microalgae image identification, image pre-processing, and machine learning techniques to address the challenges in developing a robust digital classification tool for the future.