Incorporating microbial community data with machine learning techniques to predict feed substrates in microbial fuel cells

Tree-structured parzen estimator optimized-automated machine learning assisted by meta-analysis for predicting biochar-driven N2O mitigation effect in constructed wetlands

Biochar is a carbon-neutral tool for combating climate change. Artificial intelligence applications to estimate the biochar mitigation effect on greenhouse gases (GHGs) can assist scientists in making more informed solutions. However, there is also evidence indicating that biochar promotes, rather than reduces, N2O emissions. Thus, the effect of biochar on N2O remains uncertain in constructed wetlands (CWs), and there is not a characterization metric for this effect, which increases the difficulty and inaccuracy of biochar-driven alleviation effect projections. Here, we provide new insight by utilizing machine learning-based, tree-structured Parzen Estimator (TPE) optimization assisted by a meta-analysis to estimate the potency of biochar-driven N2O mitigation. We first synthesized datasets that contained 80 studies on global biochar-amended CWs. The mitigation effect size was then calculated and further introduced as a new metric. TPE optimization was then applied to automatically tune the hyperparameters of the built extreme gradient boosting (XGBoost) and random forest (RF), and the optimum TPE-XGBoost obtained adequately achieved a satisfactory prediction accuracy for N2O flux (R2 = 91.90%, RPD = 3.57) and the effect size (R2 = 92.61%, RPD = 3.59). Results indicated that a high influent chemical oxygen demand/total nitrogen (COD/TN) ratio and the COD removal efficiency interpreted by the Shapley value significantly enhanced the effect size contribution. COD/TN ratio made the most and the second greatest positive contributions among 22 input variables to N2O flux and to the effect size that were up to 18% and 14%, respectively. By combining with a structural equation model analysis, NH4+-N removal rate had significant negative direct effects on the N2O flux. This study implied that the application of granulated biochar derived from C-rich feedstocks would maximize the net climate benefit of N2O mitigation driven by biochar for future biochar-based CWs.

Machine Learning in Bioelectrocatalysis

At present, the global energy crisis and environmental pollution coexist, and the demand for sustainable clean energy has been highly concerned. Bioelectrocatalysis that combines the benefits of biocatalysis and electrocatalysis produces high-value chemicals, clean biofuel, and biodegradable new materials. It has been applied in biosensors, biofuel cells, and bioelectrosynthesis. However, there are certain flaws in the application process of bioelectrocatalysis, such as low accuracy/efficiency, poor stability, and limited experimental conditions. These issues can possibly be solved using machine learning (ML) in recent reports although the combination of them is still not mature. To summarize the progress of ML in bioelectrocatalysis, this paper first introduces the modeling process of ML, then focuses on the reports of ML in bioelectrocatalysis, and ultimately makes a summary and outlook about current issues and future directions. It is believed that there is plenty of scope for this interdisciplinary research direction.

Sustainable circular biorefinery approach for novel building blocks and bioenergy production from algae using microbial fuel cell

The imminent need for transition to a circular biorefinery using microbial fuel cells (MFC), based on the valorization of renewable resources, will ameliorate the carbon footprint induced by industrialization. MFC catalyzed by bioelectrochemical process drew significant attention initially for its exceptional potential for integrated production of biochemicals and bioenergy. Nonetheless, the associated costly bioproduct production and slow microbial kinetics have constrained its commercialization. This review encompasses the potential and development of macroalgal biomass as a substrate in the MFC system for L-lactic acid (L-LA) and bioelectricity generation. Besides, an insight into the state-of-the-art technological advancement in the MFC system is also deliberated in detail. Investigations in recent years have shown that MFC developed with different anolyte enhances power density from several µW/m² up to 8160 mW/m². Further, this review provides a plausible picture of macroalgal-based L-LA and bioelectricity circular biorefinery in the MFC system for future research directions.

Application of artificial intelligence-based methods in bioelectrochemical systems: Recent progress and future perspectives

Bioelectrochemical Systems (BESs) leverage microbial metabolic processes to either produce electricity by degrading organic matter or consume electricity to assist metabolism, and can be used for various applications such as energy production, wastewater treatment, and bioremediation. Given the intricate mechanisms of BESs, the application of artificial intelligence (AI)-based methods have been proposed to enhance the performance of BESs due to their capability to identify patterns and gain insights through data analysis. This review focuses on the analysis and comparison of AI algorithms commonly used in BESs, including artificial neural network (ANN), genetic programming (GP), fuzzy logic (FL), support vector regression (SVR), and adaptive neural fuzzy inference system (ANFIS). These algorithms have different features, such as ANN's simple network structure, GP's use in the training process, FL's human-like thought process, SVR's high prediction accuracy and robustness, and ANFIS's combination of ANN and FL features. The AI-based methods have been applied in BESs to predict microbial communities, products or substrates, and reactor performance, which can provide valuable information and improve system efficiency. Limitations of AI-based methods for predicting and optimizing BESs and recommendations for future development are also discussed. This review demonstrates the potential of AI-based methods in optimizing BESs and provides valuable information for the future development of this field.

Machine learning-guided the fabrication of nanozyme based on highly-stable violet phosphorene decorated with phosphorus-doped hierarchically porous carbon microsphere for portable intelligent sensing of mycophenolic acid in silage

Violet phosphorene (VP) have been proved to be more stable than black phosphorene, but few reports for its application in electrochemical sensors. In this study, a highly-stable VP decorated with phosphorus-doped hierarchically porous carbon microsphere (PCM) with multiple enzyme-like activities as a nanozyme sensing platform for portable intelligent analysis of mycophenolic acid (MPA) in silage with machine learning (ML) assistance is successfully fabricated. The pore size distribution on the PCM surface is discussed using N2 adsorption tests, and morphological characterization indicates that the PCM is embedded in the layers of lamellar VP. The affinity of the VP-PCM nanozyme obtained under the guidance of the ML model reaches Km = 12.4 μmol/L for MPA. The VP-PCM/SPCE for the efficient detection of MPA exhibits high sensitivity, a wide detection range of 2.49 μmol/L - 71.14 μmol/L with a low limit of detection of 18.7 nmol/L. The proposed ML model with high prediction accuracy (R2 = 0.9999, MAPEP = 0.0081) assists the nanozyme sensor for intelligent and rapid quantification of MPA residues in corn silage and wheat silage with satisfactory recoveries of 93.33%-102.33%. The excellent biomimetic sensing properties of the VP-PCM nanozyme are driving the development of a novel MPA analysis strategy assisted by ML in the context of production requirements of livestock safety.

Machine Learning for Improved Detection of Pathogenic E. coli in Hydroponic Irrigation Water Using Impedimetric Aptasensors: A Comparative Study

Reuse of alternative water sources for irrigation (e.g., untreated surface water) is a sustainable approach that has the potential to reduce water gaps, while increasing food production. However, when growing fresh produce, this practice increases the risk of bacterial contamination. Thus, rapid and accurate identification of pathogenic organisms such as Shiga-toxin producing Escherichia coli (STEC) is crucial for resource management when using alternative water(s). Although many biosensors exist for monitoring pathogens in food systems, there is an urgent need for data analysis methodologies that can be applied to accurately predict bacteria concentrations in complex matrices such as untreated surface water. In this work, we applied an impedimetric electrochemical aptasensor based on gold interdigitated electrodes for measuring E. coliO157:H7 in surface water for hydroponic lettuce irrigation. We developed a statistical machine-learning (SML) framework for assessing different existing SML methods to predict the E. coliO157:H7 concentration. In this study, three classes of statistical models were evaluated for optimizing prediction accuracy. The SML framework developed here facilitates selection of the most appropriate analytical approach for a given application. In the case of E. coliO157:H7 prediction in untreated surface water, selection of the optimum SML technique led to a reduction of test set RMSE by at least 20% when compared with the classic analytical technique. The statistical framework and code (open source) include a portfolio of SML models, an approach which can be used by other researchers using electrochemical biosensors to measure pathogens in hydroponic irrigation water for rapid decision support.

Large-scale comparative analysis reveals different bacterial community structures in full- and lab-scale wastewater treatment bioreactors

The activated sludge process is widely used for biological wastewater treatment due to its low cost and high efficiency. Although numerous lab-scale bioreactor experiments have been conducted to investigate the microorganism performance and mechanisms in activated sludge, understanding the bacterial community differences between full- and lab-scale bioreactors has remained elusive. In this study, we investigated the bacterial communities in 966 activated sludge samples obtained from various bioreactors, including both full- and lab-scale ones, from 95 previous studies. Our findings reveal significant differences in the bacterial communities between full- and lab-scale bioreactors, with thousands of bacterial genera exclusive to each scale. We also identified 12 genera that are frequently abundant in full-scale bioreactors but rarely observed in lab-scale reactors. By using a machine-learning method, organic matter and temperature were determined as the primary factors affecting microbial communities in full- and lab-scale bioreactors. Additionally, transient bacterial species from other environments may also contribute to the observed bacterial community differences. Furthermore, the bacterial community differences between full- and lab-scale bioreactors were verified by comparing the results of lab-scale bioreactor experiments to full-scale bioreactor sampling. Overall, this study sheds light on the bacteria overlooked in lab-scale studies and deepens our understanding of the differences in bacterial communities between full- and lab-scale bioreactors.

Scale-up and techno-economic analysis of microbial electrolysis cells for hydrogen production from wastewater

Microbial electrolysis cells (MECs) have demonstrated high-rate H₂ production while concurrently treating wastewater, but the transition in scale from laboratory research to systems that can be practically applied has encountered challenges. It has been more than a decade since the first pilot-scale MEC was reported, and in recent years, many attempts have been made to overcome the barriers and move the technology to the market. This study provided a detailed analysis of MEC scale-up efforts and summarized the key factors that should be considered to further develop the technology. We compared the major scale-up configurations and systematically evaluated their performance from both technical and economic perspectives. We characterized how system scale-up impacts the key performance metrics such as volumetric current density and H₂ production rate, and we proposed methods to evaluate and optimize system design and fabrication. In addition, preliminary techno-economic analysis indicates that MECs can be profitable in many different market scenarios with or without subsidies. We also provide perspectives on future development needed to transition MEC technology to the marketplace.

Prediction of biogas production of industrial scale anaerobic digestion plant by machine learning algorithms

In the anaerobic digestion (AD) process there are some difficulties in maintaining process stability due to the complexity of the system. The variability of the raw material coming to the facility, temperature fluctuations and pH changes as a result of microbial processes cause process instability and require continuous monitoring and control. Increasing continuous monitoring, and internet of things applications within the scope of Industry 4.0 in AD facilities can provide process stability control and early intervention. In this study, five different machine learning (ML) algorithms (RF, ANN, KNN, SVR, and XGBoost) were used to describe and predict the correlation between operational parameters and biogas production quantities collected from a real-scale anaerobic digestion plant. The KNN algorithm had the lowest accuracy in predicting total biogas production over time, while the RF model had the highest prediction accuracy of all prediction models. The RF method produced the best prediction, with an R² of 0.9242, and it was followed by XGBoost, ANN, SVR, and KNN (with R² values of 0.8960, 0.8703, 0.8655, 0.8326, respectively). Real-time process control will be provided and process stability will be maintained by preventing low-efficiency biogas production with the integration of ML applications into AD facilities.

Ensemble machine learning approach for examining critical process parameters and scale-up opportunities of microbial electrochemical systems for hydrogen peroxide production

Hydrogen peroxide (H₂O₂) production in microbial electrochemical systems (MESs) is an attractive option for enabling a circular economy in the water/wastewater sector. Here, a machine learning algorithm was developed, using a meta-learning approach, to predict the H₂O₂ production rates in MES based on the seven input variables, including various design and operating parameters. The developed models were trained and cross-validated using the experimental data collected from 25 published reports. The final ensemble meta-learner model (combining 60 models) demonstrated a high prediction accuracy with very high R² (0.983) and low root-mean-square error (RMSE) (0.647 kg H₂O₂ m^-3 d^-1) values. The model identified the carbon felt anode, GDE cathode, and cathode-to-anode volume ratio as the top three most important input features. Further scale-up analysis for small-scale wastewater treatment plants indicated that proper design and operating conditions could increase the H₂O₂ production rate to as high as 9 kg m^-3 d^-1.

Model development of bioelectrochemical systems: A critical review from the perspective of physiochemical principles and mathematical methods

Bioelectrochemical systems (BESs) are promising devices for wastewater treatment and bio-energy production. Since various processes are interacted and affect the overall performance of the device, the development of theoretical modeling is an efficient approach to understand the fundamental mechanisms that govern the performance of the BES. This review aims to summarize the physiochemical principle and mathematical method in BES models, which is of great importance for the establishment of an accurate model while has received little attention in previous reviews. In this review, we begin with a classification of existing models including bioelectrochemical models, electronic models, and machine learning models. Subsequently, physiochemical principles and mathematical methods in models are discussed from two aspects: one is the description of methodology how to build a framework for models, and the other is to further review additional methods that can enrich model functions. Finally, the advantages/disadvantages, extended applications, and perspectives of models are discussed. It is expected that this review can provide a viewpoint from methodologies to understand BES models.

Improving kitchen waste composting maturity by optimizing the processing parameters based on machine learning model

As a novel analytical method based on big data, machine learning model can explore the relationship between different parameters and draw universal conclusions, which was used to predict composting maturity and identify key parameters in this study. The results showed that the Stacking model exhibited excellent prediction accuracy. SHapley Additive exPlanations (SHAP) and Partial Dependence Analysis (PDA) were performed to evaluate the importance of different parameters as well as their optimal interval. Optimal starting conditions should be maintained in the mesophilic state (temperature: 30-45℃, moisture content: 55-65%, pH: 6.3-8.0), and nutrients (total nitrogen > 2.3%, total organic carbon > 35%) should be adjusted in the thermophilic state. Experiments revealed that model-based optimization strategies could improve composting maturity because they could optimize compost microbial flora and perform complex carbon cycle functions. In conclusion, this study provides new insights into the enhancement of the composting process.

Exploring necessity to pre-treat organic fraction of waste prior to use in an earthen MFC modified with bentonite

In this study, the addition of bentonite at different proportions as clay minerals and various thicknesses (4, 5, and 6 mm) of ceramic membranes were evaluated for proton and oxygen mass transfer coefficients. Bentonite (20% and 4 mm) was found to be optimum and was then employed to assess earthen microbial fuel cell (EMFC) performance for different substrates (kitchen waste (KW) slurry and leachate) under batch mode. Both substrates were added in different concentrations of chemical oxygen demand (COD), i.e., 18, 15.2, 12.5, 9.7, and 6.9 g/L to EMFCs. The EMFC achieved superior organic removals for leachate (>96%). Intriguingly, the volatile fatty acids (VFAs) generation and consumption were different for each substrate. Each system expressed affinity towards acetic acid, but limited VFAs (acetic and propionic) were generated by KW while leachate generated acetic, propionic, and butyric. The leachate concentration having COD of 15.2 g/L produced the highest power density of 586.5 ± 38.8 mW/m³, while for KW, only 41.5 mW/m³ (6.9 g/L of COD for KW) was obtained. The study consolidates the need for an intermediate step to pre-treat the organic fraction of waste before its use for resource recovery. Bentonite was found as an effective clay mineral for manufacturing ceramic membranes.

Machine learning to predict dynamic changes of pathogenic Vibrio spp. abundance on microplastics in marine environment

Microplastics are widely found in the marine environment. Recent studies have shown that pathogenic microorganisms can hitchhike on microplastics, which might act as a vector for the spread of pathogens. Vibrio spp. are known to be pathogenic to humans and can cause serious foodborne diseases. In this study, using datasets from an estuary and a mariculture zone in China, five machine learning models were established to predict the relative abundance of Vibrio spp. on microplastics. The results showed that deep neural network (DNN) model and RandomForest algorithm achieved the best predictive performance. Different data sources, data sampling, and processing methods had a little impact on the prediction performance of DNN and RandomForest models. SHapley Additive exPlanations (SHAP) indicated that salinity and temperature are the primary factors affecting the relative abundance of Vibrio spp. The prediction performances of the five machine learning models were further improved by feature selection, providing information to support future experimental research. The results of this study could help establish a long-term and dynamic monitoring system for the relative abundance of Vibrio spp. on microplastics in response to environmental factors as well as provide useful information for assessing the potential health impacts of microplastics on marine ecology and humans.

A critical review on early-warning electrochemical system on microbial fuel cell-based biosensor for on-site water quality monitoring

The microbial fuel cell (MFC) sensor is a very promising self-powered self-sustainable system for early warning water quality detection. These sensors are cost-effective, biodegradable, compact in design, and portable in nature are favorable for real-time in situ water quality monitoring. This review represents the mechanism action behind the toxicity detection, optimization strategies, process parameters, role of biofilm, the role of external resistance, hydrodynamic study, and mathematical modeling for improving the performance of the sensor. Additionally, the techno-economic prospect of this MFC-based sensor and its challenges, limitations are addressed to make it economically more favorable for commercial use. The future direction is also explored based on the sensor's disadvantages and limitations. Comprehensively, this review covered all the possible directions of MFC sensor fabrication, their application, recent advancement, prospects challenges, and their possible solutions.

Microbial fuel cells for bioelectricity production from waste as sustainable prospect of future energy sector

Microbial fuel cell (MFC) is lauded for its potentials to solve both energy crisis and environmental pollution. Technologically, it offers the capability to harness electricity from the chemical energy stored in the organic substrate with no intermediate steps, thereby minimizes the entropic loss due to the inter-conversion of energy. The sciences underneath such MFCs include the electron and proton generation from the metabolic decomposition of the substrate by microbes at the anode, followed by the shuttling of these charges to cathode for electricity generation. While its promising prospects were mutually evinced in the past investigations, the upscaling of MFC in sustaining global energy demands and waste treatments is yet to be put into practice. In this context, the current review summarizes the important knowledge and applications of MFCs, concurrently identifies the technological bottlenecks that restricted its vast implementation. In addition, economic analysis was also performed to provide multiangle perspectives to readers. Succinctly, MFCs are mainly hindered by the slow metabolic kinetics, sluggish transfer of charged particles, and low economic competitiveness when compared to conventional technologies. From these hindering factors, insightful strategies for improved practicality of MFCs were formulated, with potential future research direction being identified too. With proper planning, we are delighted to see the industrialization of MFCs in the near future, which would benefit the entire human race with cleaner energy and the environment.

Leveraging artificial intelligence in bioelectrochemical systems

Bioelectrochemical systems (BESs) are highly evolved and sophisticated systems that produce bioenergy via exoelectrogenic microbes. Artificial intelligence (AI) helps to understand, relate, model, and predict both process parameters and microbial diversity, resulting in higher performance. This approach has revolutionized BESs through highly advanced computational algorithms that best suit the systems' architecture.

A multi-perspective review on microbial electrochemical technologies for food waste valorization

The worldwide generation of food waste (FW) has been increasing enormously due to the growing food industry and population. However, FW contains a large amount of biodegradable organics that can be converted to clean energy, which can potentially minimize the utilization of fossil fuels. Conventional biowaste valorization technologies, such as anaerobic digestion and composting, have been adopted for FW management for recovering useful biogas and compost. However, they are often limited by high capital and operation costs, low recovery efficiency, slow process kinetics, and system instability. On the other hand, microbial electrochemical technologies (METs) have been highly promising for efficiently harvesting bioenergy and high value-added products from FW. Hence, this article critically reviews up-to-date studies on applying various METs regarding their value-added products recovery efficiencies from FW. Moreover, this review lists existing challenges, ways to optimize the system performance and provides perspectives on future research needs.

Predicting antibiotic resistance gene abundance in activated sludge using shotgun metagenomics and machine learning

While the microbiome of activated sludge (AS) in wastewater treatment plants (WWTPs) plays a vital role in shaping the resistome, identifying the potential bacterial hosts of antibiotic resistance genes (ARGs) in WWTPs remains challenging. The objective of this study is to explore the feasibility of using a machine learning approach, random forests (RF's), to identify the strength of associations between ARGs and bacterial taxa in metagenomic datasets from the activated sludge of WWTPs. Our results show that the abundance of select ARGs can be predicted by RF's using abundant genera (Candidatus Accumulibacter, Dechloromonas, Pesudomonas, and Thauera, etc.), (opportunistic) pathogens and indicators (Bacteroides, Clostridium, and Streptococcus, etc.), and nitrifiers (Nitrosomonas and Nitrospira, etc.) as explanatory variables. The correlations between predicted and observed abundance of ARGs (erm(B), tet(O), tet(Q), etc.) ranged from medium (0.400 < R² < 0.600) to strong (R² > 0.600) when validated on testing datasets. Compared to those belonging to the other two groups, individual genera in the group of (opportunistic) pathogens and indicator bacteria had more positive functional relationships with select ARGs, suggesting genera in this group (e.g., Bacteroides, Clostridium, and Streptococcus) may be hosts of select ARGs. Furthermore, RF's with (opportunistic) pathogens and indicators as explanatory variables were used to predict the abundance of select ARGs in a full-scale WWTP successfully. Machine learning approaches such as RF's can potentially identify bacterial hosts of ARGs and reveal possible functional relationships between the ARGs and microbial community in the AS of WWTPs.

Experimental and validation with neural network time series model of microbial fuel cell bio-sensor for phenol detection

Phenol is one of the most commonly known chemical compound found as a pollutant in the chemical industrial wastewater. This pollutant has potential threat for human health and environment, as it can be easily absorbed by the skin and the mucous. Here, we prepared dual chambered microbial fuel cell (MFC) sensor for the detection of phenol. Varying concentration of phenol (100 mg/l, 250 mg/l, 500 mg/l, and 1000 mg/l) was applied as a substrate to the MFC and their change in output voltage was also measured. After adding 100 mg/l, 250 mg/l, 500 mg/l, and 1000 mg/l of phenol as sole substrate to the MFC, the maximum voltage output was obtained as 360 ± 10 mV, 395 ± 8 mV, 320 ± 7 mV, 350 ± 5 mV respectively. This biosensor was operated using industrial wastewater isolated microbes as a sensing element and phenol was used as a sole substrate. The topologies of ANN were analyzed to get the best model to predict the power output of MFCs and the training algorithms were compared with their convergence rates in training and test results. Time series model was used for regression analysis to predict the future values based on previously observed values. Two types of mathematical modeling i.e. Scaled Conjugate Gradient (SCG) algorithm and Time-series model was used with 44 experimental data with varying phenol concentration and varying synthetic wastewater concentration to optimize the biosensor performance. Both SCG and time series showing the best results with R² value 0.98802 and 0.99115.

Paper-based platforms for microbial electrochemical cell-based biosensors: A review

The development of low-cost analytical devices for on-site water quality monitoring is a critical need, especially for developing countries and remote communities in developed countries with limited resources. Microbial electrochemical cell-based (MXC) biosensors have been quite promising for quantitative and semi-quantitative (often qualitative) measurements of various water quality parameters due to their low cost and simplicity compared to traditional analytical methods. However, conventional MXC biosensors often encounter challenges, such as the slow establishment of biofilms, low sensitivity, and poor recoverability, making them unable to be applied for practical cases. In response, MXC biosensors assembled with paper-based materials demonstrated tremendous potentials to enhance sensitivity and field applicability. Furthermore, the paper-based platforms offer many prominent features, including autonomous liquid transport, rapid bacterial adhesion, lowered resistance, low fabrication cost (<$1 in USD), and eco-friendliness. Therefore, this review aims to summarize the current trend and applications of paper-based MXC biosensors, along with critical discussions on their field applicability. Moreover, future advancements of paper-based MXC biosensors, such as developing a novel paper-based biobatteries, increasing the system performance using an unique biocatalyst, such as yeast, and integrating the biosensor system with other advanced tools, such as machine learning and 3D printing, are highlighted.

Predicting the performance of anaerobic digestion using machine learning algorithms and genomic data

Modeling of anaerobic digestion (AD) is crucial to better understand the process dynamics and to improve the digester performance. This is an essential yet difficult task due to the complex and unknown interactions within the system. The application of well-developed data mining technologies, such as machine learning (ML) and microbial gene sequencing techniques are promising in overcoming these challenges. In this study, we investigated the feasibility of 6 ML algorithms using genomic data and their corresponding operational parameters from 8 research groups to predict methane yield. For classification models, random forest (RF) achieved accuracies of 0.77 using operational parameters alone and 0.78 using genomic data at the bacterial phylum level alone. The combination of operational parameters and genomic data improved the prediction accuracy to 0.82 (p<0.05). For regression models, a low root mean square error of 0.04 (relative root mean square error =8.6%) was acquired by neural network using genomic data at the bacterial phylum level alone. Feature importance analysis by RF suggested that Chloroflexi, Actinobacteria, Proteobacteria, Fibrobacteres, and Spirochaeta were the top 5 most important phyla although their relative abundances were ranging only from 0.1% to 3.1%. The important features identified could provide guidance for early warning and proactive management of microbial communities. This study demonstrated the promising application of ML techniques for predicting and controlling AD performance.

Research Trends and Future Perspectives in Marine Biomimicking Robotics

Mechatronic and soft robotics are taking inspiration from the animal kingdom to create new high-performance robots. Here, we focused on marine biomimetic research and used innovative bibliographic statistics tools, to highlight established and emerging knowledge domains. A total of 6980 scientific publications retrieved from the Scopus database (1950-2020), evidencing a sharp research increase in 2003-2004. Clustering analysis of countries collaborations showed two major Asian-North America and European clusters. Three significant areas appeared: (i) energy provision, whose advancement mainly relies on microbial fuel cells, (ii) biomaterials for not yet fully operational soft-robotic solutions; and finally (iii), design and control, chiefly oriented to locomotor designs. In this scenario, marine biomimicking robotics still lacks solutions for the long-lasting energy provision, which presently hinders operation autonomy. In the research environment, identifying natural processes by which living organisms obtain energy is thus urgent to sustain energy-demanding tasks while, at the same time, the natural designs must increasingly inform to optimize energy consumption.

Effects of Student Training in Social Skills and Emotional Intelligence on the Behaviour and Coexistence of Adolescents in the 21st Century

In recent decades, efforts have been made to achieve a positive coexistence among adolescents in secondary schools and create a healthy environment to prepare them to face the present-day challenges. Therefore, this study highlights the educational purpose of improving emotional management and social skills as well as decreasing antisocial and criminal behaviour among secondary education students through an educational training programme. Accordingly, to verify the effectiveness of the project, a quasi-experimental design with a pre-test/post-test structure and a control group was adopted. To achieve this, a total of 141 Spanish secondary school students participated in this study and were randomly assigned to one of two experimental conditions. The first (experimental) group (n = 55) participated in the training programme; correspondingly, the second group (control) (n = 57) followed the usual mentoring activities planned for the entire educational centre. Of the total number of participants, 52.7% of the sample were men and 47.3% were women. The mean age of the participants was 13.01 years old (SD = 0.935). The results showed improvements in the environment with adequate training and the correct application of a programme involving emotional intelligence (EI) among secondary education students. Furthermore, a decrease in conflicts and enhanced relations between the members of the educational community was evidenced. Finally, the practical implications for improving coexistence in secondary schools are discussed.

Micro-microbial electrochemical sensor equipped with combined bioanode and biocathode for water biotoxicity monitoring

The development of low-cost biosensors for water monitoring is expected to reduce potential risks from contamination accidents. This study reported a novel micro-microbial electrochemical sensor using combined bioanode and biocathode as the sensing element, characterized by a sequential flowing membrane-free channel and a bilateral passive oxygen supply. A decrease in the ratio of number of bioanode to biocathode resulted in a lower power generation, whereas, achieving a similar or even higher toxic response. The voltage was affected by both the flow rate and the acetate concentration. With the increased acetate concentration, a clear trade-off was observed between the electroactivity stimulation of bioanode vs. the electroactivity maintenance of biocathode. Biosensors made good response to the injection of formaldehyde (10 µL of 0.25%, and 100 µL of 0.025%) into the inlet. A high microbial diversity was observed. This work can lead to a revolutionizing way of water monitoring using self-powered micro-biosensors.

Performance prediction of ZVI-based anaerobic digestion reactor using machine learning algorithms

The use of zero-valent iron (ZVI) to enhance anaerobic digestion (AD) systems is widely advocated as it improves methane production and system stability. Accurate modeling of ZVI-based AD reactor is conducive to predicting methane production potential, optimizing operational strategy, and gathering reference information for industrial design in place of time-consuming and laborious tests. In this study, three machine learning (ML) algorithms, namely random forest (RF), extreme gradient boosting (XGBoost), and deep learning (DL), were evaluated for their feasibility of predicting the performance of ZVI-based AD reactors based on the operating parameters collected in 9 published articles. XGBoost demonstrated the highest accuracy in predicting total methane production, with a root mean squared error (RMSE) of 21.09, compared to 26.03 and 27.35 of RF and DL, respectively. The accuracy represented by mean absolute percentage error also showed the same trend, with 14.26%, 15.14% and 17.82% for XGBoost, RF and DL, respectively. Through the feature importance generated by XGBoost, the parameters of total solid of feedstock (TS_f), sCOD, ZVI dosage and particle size were identified as the dominant parameters that affect the methane production, with feature importance weights of 0.339, 0.238, 0.158, and 0.116, respectively. The digestion time was further introduced into the above-established model to predict the cumulative methane production. With the expansion of training dataset, DL outperformed XGBoost and RF to show the lowest RMSEs of 11.83 and 5.82 in the control and ZVI-added reactors, respectively. This study demonstrates the potential of using ML algorithms to model ZVI-based AD reactors.

Machine learning toward advanced energy storage devices and systems

Technology advancement demands energy storage devices (ESD) and systems (ESS) with better performance, longer life, higher reliability, and smarter management strategy. Designing such systems involve a trade-off among a large set of parameters, whereas advanced control strategies need to rely on the instantaneous status of many indicators. Machine learning can dramatically accelerate calculations, capture complex mechanisms to improve the prediction accuracy, and make optimized decisions based on comprehensive status information. The computational efficiency makes it applicable for real-time management. This paper reviews recent progresses in this emerging area, especially new concepts, approaches, and applications of machine learning technologies for commonly used energy storage devices (including batteries, capacitors/supercapacitors, fuel cells, other ESDs) and systems (including battery ESS, hybrid ESS, grid and microgrid-containing energy storage units, pumped-storage system, thermal ESS). The perspective on future directions is also discussed.

Enhancement of microbiome management by machine learning for biological wastewater treatment

Here, we propose to develop microbiome-based machine learning models to predict the response of biological wastewater treatment systems to environmental or operational disturbances or to design specific microbiomes to achieve a desired system function. These machine learning models can be used to enhance the stability of microbiome-based biological systems and warn against the failure of these systems.

Evaluation of artificial neural network algorithms for predicting the effect of the urine flow rate on the power performance of microbial fuel cells

Microbial fuel cell (MFC) power performance strongly depends on the biofilm growth, which in turn is affected by the feed flow rate. In this work, an artificial neural network (ANN) approach has been used to simulate the effect of the flow rate on the power output by ceramic MFCs fed with neat human urine. To this aim, three different second-order algorithms were used to train our network and then compared in terms of prediction accuracy and convergence time: Quasi-Newton, Levenberg-Marquardt, and Conjugate Gradient. The results showed that the three training algorithms were able to accurately simulate power production. Amongst all of them, the Levenberg-Marquardt was the one that presented the highest accuracy (R = 95%) and the fastest convergence (7.8 s). These results show that ANNs are useful and reliable tools for predicting energy harvesting from ceramic-MFCs under changeable flow rate conditions, which will facilitate the practical deployment of this technology.

Performance and community structure dynamics of microbial electrolysis cells operated on multiple complex feedstocks

BACKGROUND

Microbial electrolysis is a promising technology for converting aqueous wastes into hydrogen. However, substrate adaptability is an important feature, seldom documented in microbial electrolysis cells (MECs). In addition, the correlation between substrate composition and community structure has not been well established. This study used an MEC capable of producing over 10 L/L-day of hydrogen from a switchgrass-derived bio-oil aqueous phase and investigated four additional substrates, tested in sequence on a mature biofilm. The additional substrates included a red oak-derived bio-oil aqueous phase, a corn stover fermentation product, a mixture of phenol and acetate, and acetate alone.

RESULTS

The MECs fed with the corn stover fermentation product resulted in the highest performance among the complex feedstocks, producing an average current density of 7.3 ± 0.51 A/m2, although the acetate fed MECs outperformed complex substrates, producing 12.3 ± 0.01 A/m2. 16S rRNA gene sequencing showed that community structure and community diversity were not predictive of performance, and replicate community structures diverged despite identical inoculum and enrichment procedure. The trends in each replicate, however, were indicative of the influence of the substrates. Geobacter was the most dominant genus across most of the samples tested, but its abundance did not correlate strongly to current density. High-performance liquid chromatography (HPLC) showed that acetic acid accumulated during open circuit conditions when MECs were fed with complex feedstocks and was quickly degraded once closed circuit conditions were applied. The largest net acetic acid removal rate occurred when MECs were fed with red oak bio-oil aqueous phase, consuming 2.93 ± 0.00 g/L-day. Principal component analysis found that MEC performance metrics such as current density, hydrogen productivity, and chemical oxygen demand removal were closely correlated. Net acetic acid removal was also found to correlate with performance. However, no bacterial genus appeared to correlated to these performance metrics strongly, and the analysis suggested that less than 70% of the variance was accounted for by the two components.

CONCLUSIONS

This study demonstrates the robustness of microbial communities to adapt to a range of feedstocks and conditions without relying on specific species, delivering high hydrogen productivities despite differences in community structure. The results indicate that functional adaptation may play a larger role in performance than community composition. Further investigation of the roles each microbe plays in these communities will help MECs to become integral in the 21st-century bioeconomy to produce zero-emission fuels.

Advances in aptasensor technology

Aptasensors form a class of biosensors that function on the basis of a biological recognition. An aptasensor is advantageous because it incorporates a unique biologic recognition element, i.e., an aptamer, coupled to a transducer to convert a biological interaction to readable signals that can be easily processed and reported. In such biosensors, the specificity of aptamers is comparable to and sometimes even better than that of antibodies. Using the SELEX technique, aptamers with high specificity and affinity to various targets can be isolated from large pools of different oligonucleotides. Nowadays, new modifications of the SELEX technique and, as a result, easy generation and synthesis of aptamers have led to the wide application of these materials as biological receptors in biosensors. In this regard, aptamers promise a bright future. In the present research a brief account is initially provided of the recent developments in aptasensors for various targets. Then, immobilization methods, design strategies, current limitations and future directions are discussed for aptasensors.

Prediction of anaerobic digestion performance and identification of critical operational parameters using machine learning algorithms

Machine learning has emerges as a novel method for model development and has potential to be used to predict and control the performance of anaerobic digesters. In this study, several machine learning algorithms were applied in regression and classification models on digestion performance to identify determinant operational parameters and predict methane production. In the regression models, k-nearest neighbors (KNN) algorithm demonstrates optimal prediction accuracy (root mean square error = 26.6, with the dataset range of 259.0-573.8), after narrowing prediction coverage by excluding extreme outliers from the validation set. In the classification models, logistic regression multiclass algorithm yields the best prediction accuracy of 0.73. Feature importance reveals that total carbon was the determinant operational parameter. These results demonstrate the great potential of using machine learning algorithms to predict anaerobic digestion performance.

Biogeographic Patterns in Members of Globally Distributed and Dominant Taxa Found in Port Microbial Communities

Microbes are ubiquitous throughout the world and are highly diverse. Characterizing the extent of variation in the microbial diversity across large geographic spatial scales is a challenge yet can reveal a lot about what biogeography can tell us about microbial populations and their behavior. Machine learning approaches have been used mostly to examine the human microbiome and, to some extent, microbial communities from the environment. Here, we display how supervised machine learning approaches can be useful to understand microbial biodiversity and biogeography using microbes from globally distributed shipping ports. Our findings indicate that the members of globally dominant phyla are important for differentiating locations, which reduces the reliance on rare taxa to probe geography. Further, this study displays how global biogeographic patterning of aquatic microbial communities (and other systems) can be assessed through populations of the highly abundant and ubiquitous taxa that dominant the system.

We conducted a global characterization of the microbial communities of shipping ports to serve as a novel system to investigate microbial biogeography. The community structures of port microbes from marine and freshwater habitats house relatively similar phyla, despite spanning large spatial scales. As part of this project, we collected 1,218 surface water samples from 604 locations across eight countries and three continents to catalogue a total of 20 shipping ports distributed across the East and West Coast of the United States, Europe, and Asia to represent the largest study of port-associated microbial communities to date. Here, we demonstrated the utility of machine learning to leverage this robust system to characterize microbial biogeography by identifying trends in biodiversity across broad spatial scales. We found that for geographic locations sharing similar environmental conditions, subpopulations from the dominant phyla of these habitats (Actinobacteria, Bacteroidetes, Cyanobacteria, and Proteobacteria) can be used to differentiate 20 geographic locations distributed globally. These results suggest that despite the overwhelming diversity within microbial communities, members of the most abundant and ubiquitous microbial groups in the system can be used to differentiate a geospatial location across global spatial scales. Our study provides insight into how microbes are dispersed spatially and robust methods whereby we can interrogate microbial biogeography.

IMPORTANCE Microbes are ubiquitous throughout the world and are highly diverse. Characterizing the extent of variation in the microbial diversity across large geographic spatial scales is a challenge yet can reveal a lot about what biogeography can tell us about microbial populations and their behavior. Machine learning approaches have been used mostly to examine the human microbiome and, to some extent, microbial communities from the environment. Here, we display how supervised machine learning approaches can be useful to understand microbial biodiversity and biogeography using microbes from globally distributed shipping ports. Our findings indicate that the members of globally dominant phyla are important for differentiating locations, which reduces the reliance on rare taxa to probe geography. Further, this study displays how global biogeographic patterning of aquatic microbial communities (and other systems) can be assessed through populations of the highly abundant and ubiquitous taxa that dominant the system.

Recent development of biofuel cell based self-powered biosensors

BFC-based SPBs have been used as power sources for other devices and as sensors for detecting toxicity and BOM.