Nishan Bhattarai, Pradeep Wagle

Remote Sensing • 0

<jats:p>Evapotranspiration (ET) plays an important role in coupling the global energy, water, and biogeochemical cycles and explains ecosystem responses to global environmental change. However, quantifying and mapping the spatiotemporal distribution of ET across a large area is still a challenge, which limits our understanding of how a given ecosystem functions under a changing climate. This also poses a challenge to water managers, farmers, and ranchers who often rely on accurate estimates of ET to make important irrigation and management decisions. Over the last three decades, remote sensing-based ET modeling tools have played a significant role in managing water resources and understanding land-atmosphere interactions. However, several challenges, including limited applicability under all conditions, scarcity of calibration and validation datasets, and spectral and spatiotemporal constraints of available satellite sensors, exist in the current state-of-the-art remote sensing-based ET models and products. The special issue on “Remote Sensing of Evapotranspiration II” was launched to attract studies focusing on recent advances in remote sensing-based ET models to help address some of these challenges and find novel ways of applying and/or integrating remotely sensed ET products with other datasets to answer key questions related to water and environmental sustainability. The 13 articles published in this special issue cover a wide range of topics ranging from field- to global-scale analysis, individual model to multi-model evaluation, single sensor to multi-sensor fusion, and highlight recent advances and applications of remote sensing-based ET modeling tools and products.</jats:p>

Mila Koeva, Rohan Bennett, Claudio Persello

Remote Sensing • 0

<jats:p>Contemporary land administration (LA) systems incorporate the concepts of cadastre and land registration. Conceptually, LA is part of a global land management paradigm incorporating LA functions such as land value, land tenure, land development, and land use. The implementation of land-related policies integrated with well-maintained spatial information reflects the aim set by the United Nations to deliver tenure security for all (Sustainable Development Goal target 1.4, amongst many others). Innovative methods for data acquisition, processing, and maintaining spatial information are needed in response to the global challenges of urbanization and complex urban infrastructure. Current technological developments in remote sensing and geo-spatial information science provide enormous opportunities in this respect. Over the past decade, the increasing usage of unmanned aerial vehicles (UAVs), satellite and airborne-based acquisitions, as well as active remote sensing sensors such as LiDAR, resulted in high spatial, spectral, radiometric, and temporal resolution data. Moreover, significant progress has also been achieved in automatic image orientation, surface reconstruction, scene analysis, change detection, classification, and automatic feature extraction with the help of artificial intelligence, spatial statistics, and machine learning. These technology developments, applied to LA, are now being actively demonstrated, piloted, and scaled. This Special Issue hosts papers focusing on the usage and integration of emerging remote sensing techniques and their potential contribution to the LA domain.</jats:p>

Mariana Belgiu, Alfred Stein

Remote Sensing • 0

<jats:p>In this paper, we discuss spatiotemporal data fusion methods in remote sensing. These methods fuse temporally sparse fine-resolution images with temporally dense coarse-resolution images. This review reveals that existing spatiotemporal data fusion methods are mainly dedicated to blending optical images. There is a limited number of studies focusing on fusing microwave data, or on fusing microwave and optical images in order to address the problem of gaps in the optical data caused by the presence of clouds. Therefore, future efforts are required to develop spatiotemporal data fusion methods flexible enough to accomplish different data fusion tasks under different environmental conditions and using different sensors data as input. The review shows that additional investigations are required to account for temporal changes occurring during the observation period when predicting spectral reflectance values at a fine scale in space and time. More sophisticated machine learning methods such as convolutional neural network (CNN) represent a promising solution for spatiotemporal fusion, especially due to their capability to fuse images with different spectral values.</jats:p>

Kunal Goel, A. Bindal

2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC) • 2018

This paper presents the importance of precision agriculture over the tradition agriculture that how farmers can get the exact details about their cultivation land while deploying the sensor and measure the various parameters of the land and then do the farming according to that parameters so they can earn more and through the deployment of sensor in land also helps the farmers in excellent crop yield. The wireless sensor system which is discussed in this paper tells about the various sensors used for measure the various parameters of land like moisture, temperature and soil salinity, which is the most important part of farming. Another thing is deployment and communication techniques through the hybrid network and the concept of microbial fuel cell membrane, in which anode and cathode is there and various bacterias will used to produce the current work as a acetate which will make the sensor node self powering. Bacteria's perform the reactions through the anode and cathode in the compartment and also added the various chemicals like methylene blue, neutral red, thionine produce which will accelerate the current for batteries used for sensor, which will expand the network lifespan.

Celal Erbay, Salvador Carreon-Bautista, E. Sánchez-Sinencio et al.

Environmental Science &amp; Technology • 2014

Microbial fuel cell (MFC) that can directly generate electricity from organic waste or biomass is a promising renewable and clean technology. However, low power and low voltage output of MFCs typically do not allow directly operating most electrical applications, whether it is supplementing electricity to wastewater treatment plants or for powering autonomous wireless sensor networks. Power management systems (PMSs) can overcome this limitation by boosting the MFC output voltage and managing the power for maximum efficiency. We present a monolithic low-power-consuming PMS integrated circuit (IC) chip capable of dynamic maximum power point tracking (MPPT) to maximize the extracted power from MFCs, regardless of the power and voltage fluctuations from MFCs over time. The proposed PMS continuously detects the maximum power point (MPP) of the MFC and matches the load impedance of the PMS for maximum efficiency. The system also operates autonomously by directly drawing power from the MFC itself without any external power. The overall system efficiency, defined as the ratio between input energy from the MFC and output energy stored into the supercapacitor of the PMS, was 30%. As a demonstration, the PMS connected to a 240 mL two-chamber MFC (generating 0.4 V and 512 μW at MPP) successfully powered a wireless temperature sensor that requires a voltage of 2.5 V and consumes power of 85 mW each time it transmit the sensor data, and successfully transmitted a sensor reading every 7.5 min. The PMS also efficiently managed the power output of a lower-power producing MFC, demonstrating that the PMS works efficiently at various MFC power output level.

J. Mangaiyarkkarasi, J. Shanthalakshmi Revathy

Advances in Wireless Technologies and Telecommunication • 2024

<jats:p>This chapter explores the synergy between radar and radio frequency (RF) front-end systems, ushering in a new era of wireless connectivity. It discusses the collaborative potential of radar and RF, emphasizing their role in enhancing security, reducing interference, and boosting adaptability. The chapter covers radar-based spectrum sensing, which enhances network efficiency, particularly in high-frequency scenarios like 5G. Radar and RF enable precise localization for IoT and autonomous vehicles, surpassing the capabilities of GPS. The chapter highlights radar's contributions to security, threat detection, and reducing signal interference. Radar-assisted RF improves vehicle communication, cooperative driving, and traffic management. In environmental monitoring and disaster management, radar augments RF for early warnings. This integration offers transformative potential, benefiting diverse applications and offering theoretical and practical insights for researchers and engineers. Radar and RF convergence offers a more connected, adaptable, and efficient wireless future.</jats:p>

G. Jeeva, P. Mahalakshmi, S. Thenmalar

Advanced Computing Solutions for Healthcare • 2025

<jats:p>The integration of smart sensors in wearable devices, particularly smart watches, has revolutionized the landscape of personal health monitoring. This review paper provides a comprehensive analysis of recent advancements in smart sensor technology and their application in smartwatches for health monitoring. The paper begins with an overview of the evolution of smartwatches and their transition from timekeeping devices to sophisticated health monitoring tools. It then delves into the key components of smart sensor technology, encompassing biometric sensors, environmental sensors, and activity trackers. The review extensively covers the diverse range of health parameters that can be monitored by smartwatches, including physical activity levels, oxygen saturation, blood pressure, and heart rate. Furthermore, the paper evaluates the accuracy and reliability of these sensors, considering factors such as sensor placement, calibration, and data processing techniques. The paper also explores the potential integration of machine learning and artificial intelligence in data analysis and interpretation, highlighting their potential to enhance the effectiveness and efficiency of smartwatch health monitoring. In addition, the review addresses challenges and limitations associated with smartwatch health monitoring, including privacy concerns, data security, and battery life. This paper provides an up-to-date overview of smart sensor technology as applied to health monitoring in smartwatches. It serves as a valuable resource for researchers, healthcare professionals, and technology enthusiasts interested in understanding the potential and limitations of this rapidly evolving field.</jats:p>

Anna Espinoza-Tofalos, Francesca Formicola, Pierangela Cristiani et al.

ECS Meeting Abstracts • 2019

<jats:p> Bioelectrochemical Systems (BES) are a novel technology in which microorganisms degrade the organic matter in anaerobic conditions by using an electrode (anode) as final electron acceptor. Therefore, BES can be used as an effective strategy in environments where the absence of suitable electron acceptors limits classic bioremediation. Researches in progress demonstrated the possibility of apply innovative microbial electrochemical technologies for the monitoring and recovering of low concentration of organics, metals and micronutrients from polluted water environments, out of the electric grid. Recently BES have been also studied to stimulate the anaerobic degradation of hydrocarbons [1,2]. Although bioremediation is often inexpensive compared to physical-chemical methods, it typically requires more time and it is currently applied to a limited variety of pollutants. Benzene is a very toxic hydrocarbon and the pollution of this compound in fresh and groundwater causes many health and environmental problems. By monitoring the current produced by a BES, the rate of specific metabolic processes and the substrate concentration can be quantified in real time. The aim of this work is to study the correlation between the current produced in a BES and the concentration of benzene, in the rage of 10–60 mg/L. Tests were performed in single cell membraneless bioelectrochemical systems. The first run consisted in the inoculation of a BES with a refinery waste water in order to colonize the anode with an electroactive benzene-degrading bacterial community. Benzene was periodically supplemented to select a community able to degrade benzene. Current and benzene concentration were monitored, in order to correlate these parameters with the development of an electroactive biofilm. The application of this technology as biosensor for the monitoring of toxic compounds in water presents several advantages: low operational cost, versatility and the possibility to monitor in real time the concentration of pollutants from the environment. </jats:p> <jats:p>[1] M. Daghio, A. Espinoza Tofalos, B. Leoni, P. Cristiani, M. Papacchini, E. Jalilnejad, G. Bestetti, A. Franzetti. Bioelectrochemical BTEX removal at different voltages: assessment of the degradation and characterization of the microbial communities. Journal of Hazardous Materials, Volume 341, 5 November 2018, Pages 120-127 </jats:p> <jats:p>[2] S. Zhanga, J.Youa, C. Kennes, Z. Cheng, J. Ye, D. Chen, J. Chen, L. Wang. Current advances of VOCs degradation by bioelectrochemical systems: A review. Chemical Engineering Journal, Volume 334, 15 February 2018, Pages 2625-2637 </jats:p>

Pachhaiammal Alias Priya M, P. Karthikeyani, N. Arunfred et al.

2024 4th International Conference on Innovative Practices in Technology and Management (ICIPTM) • 2024

The Internet of Things (IoT) in hydrogen transport and fuel cell vehicle infrastructure is a fundamental transformation. This work discusses how IoT devices transformed the system efficiently. By connecting IoT devices, this infrastructure improves effectiveness, security, and sustainability. Hydrogen sensors monitor storage, pipelines, and filling stations to optimize hydrogen supply. Flow meters control distribution and consumption, while pressure and temperature sensors maintain safety. Performance data from vehicle telematics optimizes fuel usage and battery health. Real-time IoT data optimizes hydrogen production and distribution with energy availability in energy management systems. Remote monitoring devices provide quick system health intervention. Communication gateways provide centralized control by connecting everything. Predictive maintenance sensors monitor equipment status to prevent downtime, and IoT strengthens security systems. Smart grid integration integrates renewable and hydrogen generation for sustainability. IoT solutions collect and analyze device data to provide performance insights. Environmental sensors optimize storage. This complete integration strengthens hydrogen transport and fuel cell vehicle infrastructure, making mobility safer, more efficient, and more sustainable.

Anwar Elhadad, Maryam Rezaie, Seokheun Choi

2022 21st International Conference on Micro and Nanotechnology for Power Generation and Energy Conversion Applications (PowerMEMS) • 2022

Miniaturized bio-solar cells have emerged as a potential sustainable power source for the Internet of Things (IoT) applications deployed in unattended, difficult-to-reach aquatic environments such as oceans, rivers, and lakes. Here, we create a scalable, high-power, long-lasting, and buoyant bio-solar cell stack that integrates significantly improved miniature bio-solar cells in an array. Each cell incorporates a symbiotic microbial consortium consisting of Synechocystis sp. PCC6803 and Bacillus subtilis. While Synechocystis sp. generates electricity through its photosynthetic and respiratory activities, it produces an organic fuel through its photosynthesis, which sustains B. subtilis in the consortium. Each cell was able to generate a sustainable maximum power density of ~70 μWcm−2.

Yongchao Tian, Chuwen Huang, Guohao Shangguan et al.

2025 IEEE 5th International Conference on Electronic Technology, Communication and Information (ICETCI) • 2025

This study focuses on the development of an innovative monitoring device that integrates sensors, IoT and software data processing technologies. First, the device is based on microbial electrochemistry and relies on the metabolic activities of electroactive microorganisms to generate electrical signals with the help of microbial fuel cell technology. Sensors play a key role in collecting the electrical signals with high precision to ensure that the acquired data are accurate and reliable. Secondly, the use of Internet of Things (IoT) technology realizes the remote and rapid transmission of the collected data, breaks the geographical limitation of data transmission, and enables the data to be summarized to the processing terminal in a timely manner. The software data processing system, on the other hand, analyzes and processes the transmitted data in depth, and through the establishment of specific algorithms and models, transforms the collected electrical signals into intuitive pollution indicator data, and realizes the storage, management and visualization display of the data. Finally, after testing and verification, the device has outstanding advantages such as realtime monitoring and accurate data, which greatly improves the monitoring efficiency and shows a broad application prospect in the field of monitoring system.

Gabriela Marcano, P. Pannuto

Proceedings of the 1st ACM Workshop on No Power and Low Power Internet-of-Things • 2021

This paper explores the power delivery potential of soil-based microbial fuel cells. We build a prototype energy harvesting setup for a soil microbial fuel cell, measure the amount of power that we can harvest, and use that energy to drive an e-ink display as a representative example of a periodic energy-intensive load. Microbial fuel cells are highly sensitive to environmental conditions, especially soil moisture. In near-optimal, super moist conditions our cell provides approximately 100 μW of power at around 500 mV, which is ample power over time to power our system several times a day. We further explore how cell performance diminishes and recovers with varying moisture levels as well as how cell performance is affected by the load from the energy harvester itself. In sum, we find that the confluence of ever lower-power electronics and new understanding of microbial fuel cell design means that "soil-powered sensors" are now feasible. There remains, however, significant future work to make these systems reliable and maximally performant.

Sanowar Hossain, Md Asif Adnan, Abu Shufian et al.

2023 IEEE 11th Region 10 Humanitarian Technology Conference (R10-HTC) • 2023

Chemical manufacturing, textile processing, and other industrial activities have increased the importance of effective wastewater treatment. This study gives a systematic look at how well an artificially ventilated tidal flow Microbial Fuel Cell (MFC) wetland system cleans the water. The system uses IoT monitoring to continuously collect data on the MFC's performance, which can be used to optimize the system for wastewater treatment and energy generation. Integrating IoT monitoring with the ESP8266-12 and ADS1115 improves real-time evaluation and makes it possible to keep improving the performance of MFCs for efficient treatment of wastewater and production of energy. The main feature of this system is that - (i) it can generate electricity. Secondly, (ii) it has waste disposal facilities, and (iii) the chemical elements of wastewater can produce hydroponic plants. The study used three materials: Jhama brick, brick surkhi, and rubber tire fragments. By testing NH4_N, N02_N, N03_N, TN, TSS, TKN, BOD, and COD. The experiment results show that TKN (65.8%) and TN (76.9%) removed the most nitrogen due to better nitrification-denitrification and media-oriented chemical adsorption. The highest pollutant and coliform removed were found at around 87.5% and 95.09% of COD, respectively. The proposed system can produce bioenergy with a maximum voltage of 142 mV, a maximum current density of 27.85 mA/m3, and a maximum power density of 3954.03 mW/m3. Pollutant removal was highest when jhama brick was used as a medium in MFC and lowest when rubber tire fragments were used.

Ian E.Y. Li, Tiger Y. S. Cheng, Kelvin W. L. Wong

2024 7th International Conference on Green Technology and Sustainable Development (GTSD) • 2024

Based on the market research report by the International Market Analysis Research and Consulting Group (IMARC) Group, the global market for environmental monitoring has surpassed USD$20 billion in 2022. It is projected to grow to over USD$31 billion by 2028, primarily driven by the increasing demand for environmental monitoring in developing nations, particularly China. However, implementing an Environmental Monitoring System (EMS) poses significant challenges in terms of scale and cost. In rural areas, the deployment of EMS typically requires a substantial number of sensors, predominantly powered by battery packs, solar panels, and stationary power supplies. Except for stationary power supplies, the other two methods entail a considerable amount of resources for monitoring and replacement, leading to higher operational costs. To address these challenges, this research project aims to propose a self-sustaining Internet of Things (IoT) monitoring system. This system integrates a power generator based on moss-based microbial fuel cells (MFCs), which generate a voltage output through photosynthesis. Additionally, the system will incorporate a voltage booster circuit to amplify the power output to a usable level of 3.3 volts. This voltage level enables the system to power various IoT devices, such as MCUs and a wide range of sensors, enhancing its versatility and applicability. By eliminating the need for solar panels and reducing maintenance costs and frequency, the proposed system has the potential to reduce overall expenses significantly. This cost reduction would facilitate wider adoption of the system by companies and countries, contributing to the mitigation of environmental pollution.

Ruolin He, Jinyu Zhang, Yuanzhe Shao et al.

PLOS Computational Biology • 2023

Non-ribosomal peptide synthetase (NRPS) is a diverse family of biosynthetic enzymes for the assembly of bioactive peptides. Despite advances in microbial sequencing, the lack of a consistent standard for annotating NRPS domains and modules has made data-driven discoveries challenging. To address this, we introduced a standardized architecture for NRPS, by using known conserved motifs to partition typical domains. This motif-and-intermotif standardization allowed for systematic evaluations of sequence properties from a large number of NRPS pathways, resulting in the most comprehensive cross-kingdom C domain subtype classifications to date, as well as the discovery and experimental validation of novel conserved motifs with functional significance. Furthermore, our coevolution analysis revealed important barriers associated with reengineering NRPSs and uncovered the entanglement between phylogeny and substrate specificity in NRPS sequences. Our findings provide a comprehensive and statistically insightful analysis of NRPS sequences, opening avenues for future data-driven discoveries. Author Summary NRPS, a gigantic enzyme that produces diverse microbial secondary metabolites, provides a rich source for important medical products including antibiotics. Despite the extensive knowledge gained about its structure and the large amount of sequencing data available, the frequent failure of reengineering NRPS in synthetic biology highlights the fact that much is still unknown. In this work, we applied existing knowledge to data mining of NRPS sequences, using well-known conserved motifs to partition NRPS sequences into motif-intermotif architectures. This standardization allows for integrating large amounts of sequences from different sources, providing a comprehensive overview of NRPSs across different kingdoms. Our findings included new C domain subtypes, novel conserved motifs with implication in structural flexibility, and insights into why NRPSs are so difficult to reengineer. To facilitate researchers in related fields, we constructed an online platform “NRPS Motif Finder” for parsing the motif-and-intermotif architecture and C domain subtype classification (http://www.bdainformatics.org/page?type=NRPSMotifFinder). We believe that this knowledge-guided approach not only advances our understanding of NRPSs but also provides a useful methodology for data mining in large-scale biological sequences.

N. Madondo, S. Rathilal, B. Bakare et al.

Chemistry – An Asian Journal • 2023

The selectivity of catalytic materials suitable for oxygen reduction potential of bioelectrochemical systems is very affluent. In this study, the application of magnetite-nanoparticles and a static magnetic field on a microbial fuel cell (MFC) in anaerobic digestion was investigated. The experimental set-up included four 1 L biochemical methane potential tests: a) MFC, b) MFC with magnetite-nanoparticles (MFCM), c) MFC with magnetite-nanoparticles and magnet (MFCMM), and d) control. The highest biogas production obtained was 545.2 mL/g VSfed in the MFCMM digester, which was substantially greater than the 117.7 mL/g VSfed of the control. This was accompanied by high contaminant removals for chemical oxygen demand (COD) of 97.3%, total solids (TS) of 97.4%, total suspended solids (TSS) of 88.7%, volatile solids (VS) 96.1%, and color of 70.2%. The electrochemical efficiency analysis revealed greater maximum current density of 12.5 mA/m2 and coulombic efficiency of 94.4% for the MFCMM. Kinetically, the cumulative biogas produced data obtained were well fitted on the modified Gompertz models and the greatest coefficient of determination (R2 = 0.990) was obtained in the MFCMM. Therefore, the application of magnetite-nanoparticles and static magnetic field on MFC showed a high potential for bioelectrochemical methane production and contaminant removal for sewage sludge.

F. Ma, Yankai Yin, Shaopeng Pang et al.

IEEE Access • 2019

Microbial fuel cells (MFCs) are devices that transform organic matters in wastewater into green energy. Microbial fuel cells systems have strong nonlinearity and high coupling, which involves control science, microbiology, electrochemistry and other disciplines. According to the requirements of microbial fuel cell system for model robustness and accuracy, we designed a comprehensive model optimization framework. Firstly, the influence of uncertain parameters on system was analyzed by combining global sensitivity analysis with uncertainty analysis. In accordance with analysis results, the uncertain parameters were optimized. Secondly, based on the optimized stochastic model, a simplified model was proposed by combining variable selection with neural networks. The results shown that the proposed framework can deeply analysis the influence of uncertain parameters on output, and provide theoretical basis for experimental research. It fully simplifies the original MFCs model, and has guiding significance for other types of fuel cells.

F. Ma, Yankai Yin, Min Li

Mathematical Problems in Engineering • 2019

Sediment microbial fuel cells (SMFCs) are a typical microbial fuel cell without membranes. They are a device developed on the basis of electrochemistry and use microbes as catalysts to convert chemical energy stored in organic matter into electrical energy. This study selected a single-chamber SMFC as a research object, using online monitoring technology to accurately measure the temperature, pH, and voltage of the microbial fuel cell during the start-up process. In the process of microbial fuel cell start-up, the relationship between temperature, pH, and voltage was analysed in detail, and the correlation between them was calculated using SPSS software. The experimental results show that, at the initial stage of SMFC, the purpose of rapid growth of power production can be achieved by a large increase in temperature, but once the temperature is reduced, the power production of SMFC will soon recover to the state before the temperature change. At the beginning of SMFC, when the temperature changes drastically, pH will change the same first, and then there will be a certain degree of rebound. In the middle stage of SMFC start-up, even if the temperature will return to normal after the change, a continuous temperature drop in a short time will lead to a continuous decrease in pH value. The RBF neural network and ELM neural network were used to perform nonlinear system regression in the later stage of SMFC start-up and using the regression network to forecast part of the data. The experimental results show that the ELM neural network is more excellent in forecasting SMFC system. This article will provide important guidance for shortening start-up time and increasing power output.

Jia-rui Han, Shuai Li, Wen-Jun Li et al.

Advanced Biotechnology • 2024

Extreme environments such as hyperarid, hypersaline, hyperthermal environments, and the deep sea harbor diverse microbial communities, which are specially adapted to extreme conditions and are known as extremophiles. These extremophilic organisms have developed unique survival strategies, making them ideal models for studying microbial diversity, evolution, and adaptation to adversity. They also play critical roles in biogeochemical cycles. Additionally, extremophiles often produce novel bioactive compounds in response to corresponding challenging environments. Recent advances in technologies, including genomic sequencing and untargeted metabolomic analysis, have significantly enhanced our understanding of microbial diversity, ecology, evolution, and the genetic and physiological characteristics in extremophiles. The integration of advanced multi-omics technologies into culture-dependent research has notably improved the efficiency, providing valuable insights into the physiological functions and biosynthetic capacities of extremophiles. The vast untapped microbial resources in extreme environments present substantial opportunities for discovering novel natural products and advancing our knowledge of microbial ecology and evolution. This review highlights the current research status on extremophilic microbiomes, focusing on microbial diversity, ecological roles, isolation and cultivation strategies, and the exploration of their biosynthetic potential. Moreover, we emphasize the importance and potential of discovering more strain resources and metabolites, which would be boosted greatly by harnessing the power of multi-omics data.

Lauren F. Messer, Charlotte E. Lee, R. Wattiez et al.

Microbiome • 2024

Background Microbial functioning on marine plastic surfaces has been poorly documented, especially within cold climates where temperature likely impacts microbial activity and the presence of hydrocarbonoclastic microorganisms. To date, only two studies have used metaproteomics to unravel microbial genotype–phenotype linkages in the marine ‘plastisphere’, and these have revealed the dominance of photosynthetic microorganisms within warm climates. Advancing the functional representation of the marine plastisphere is vital for the development of specific databases cataloging the functional diversity of the associated microorganisms and their peptide and protein sequences, to fuel biotechnological discoveries. Here, we provide a comprehensive assessment for plastisphere metaproteomics, using multi-omics and data mining on thin plastic biofilms to provide unique insights into plastisphere metabolism. Our robust experimental design assessed DNA/protein co-extraction and cell lysis strategies, proteomics workflows, and diverse protein search databases, to resolve the active plastisphere taxa and their expressed functions from an understudied cold environment. Results For the first time, we demonstrate the predominance and activity of hydrocarbonoclastic genera ( Psychrobacter , Flavobacterium , Pseudomonas ) within a primarily heterotrophic plastisphere. Correspondingly, oxidative phosphorylation, the citrate cycle, and carbohydrate metabolism were the dominant pathways expressed. Quorum sensing and toxin-associated proteins of Streptomyces were indicative of inter-community interactions. Stress response proteins expressed by Psychrobacter , Planococcus , and Pseudoalteromonas and proteins mediating xenobiotics degradation in Psychrobacter and Pseudoalteromonas suggested phenotypic adaptations to the toxic chemical microenvironment of the plastisphere. Interestingly, a targeted search strategy identified plastic biodegradation enzymes, including polyamidase, hydrolase, and depolymerase, expressed by rare taxa. The expression of virulence factors and mechanisms of antimicrobial resistance suggested pathogenic genera were active, despite representing a minor component of the plastisphere community. Conclusion Our study addresses a critical gap in understanding the functioning of the marine plastisphere, contributing new insights into the function and ecology of an emerging and important microbial niche. Our comprehensive multi-omics and comparative metaproteomics experimental design enhances biological interpretations to provide new perspectives on microorganisms of potential biotechnological significance beyond biodegradation and to improve the assessment of the risks associated with microorganisms colonizing marine plastic pollution. Video Abstract

A. Shaheen, A. Elsayed, R. El-Sehiemy et al.

Engineering Optimization • 2021

A modified marine predators optimizer (MMPO) is proposed for simultaneous distribution network reconfiguration (DNR) associated with the allocation of distributed generators (DGs). In the MMPO, the predator’s strategies are merged to consider the possibilities for variation in the environmental and climatic circumstances. The suggested MMPO is contrasted with the standard marine predators optimizer (MPO) and genetic, harmony search, fireworks, firefly and improved sine–cosine optimizers. The proposed MMPO is validated on single and multiple objectives using 33- and 69-bus distribution systems at light, nominal and heavy loading levels. The results obtained by the proposed MMPO are compared with those obtained by the original MPO and other optimizers. The achieved simulation outputs reveal a great improvement over the standard MPO and demonstrate the superiority of the proposed MMPO for simultaneous DNR and DG allocation.

Nitin Liladhar Rane, Ömer Kaya, Jayesh Rane

Artificial Intelligence, Machine Learning, and Deep Learning for Sustainable Industry 5.0 • 2024

<jats:p>The use of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) significantly has the touch of transformational potential towards bringing the Sustainable Development Goals (SDGs) to be addressed in various industries. This research investigates the new developments and applications of these technologies in advancing sustainability programs in industry-intensive domains. Industries are beginning to undergo a major change by making today with the help of AI, ML, and DL that resources can be optimized, energy efficiency can be improved, and environmental impacts can be mitigated. A number of other trends - including predictive analytics and intelligent automation, allow for smarter and more efficient production, waste minimization and circular economy practices. AI-powered solutions are also now being used in the energy sector to maximize the generation of renewable energy, optimize grid management, and aid in the transition to low carbon energy systems. This will enable industries achieve better environmental benefits and higher operational efficiencies through big data analytics and IoT. AI and ML are also crucial in smart cities, urban planning, public services that delivery efficiency and overall support the sustainability agenda. The results reinforce the importance of strong regulatory structures and interdisciplinary collaboration to optimally leverage AI, ML, and DL to the SDGs, which will be intrinsic to designing for resilience and sustainability.</jats:p>

Nitin Liladhar Rane, Ömer Kaya, Jayesh Rane

Artificial Intelligence, Machine Learning, and Deep Learning for Sustainable Industry 5.0 • 2024

<jats:p>This book offers an insight into the applications of Artificial Intelligence (AI)- Machine Learning Algorithms and Deep Learning (DL) in Bigdata Analytics to Industry 4.0/5.0 and Society 5.0 with transformative power responsibly. It has delved into how these technologies are disrupting industries, fostering innovation, and solving age-old social problems-so that readers have an understanding of where the digital world is headed. These chapters cover the big picture subjects of using AI with Big data analytics aimed mostly at increasing industrial efficiency, healthcare optimization, retail transformation, construction industry transformation, autonomous vehicles development and environmental sustainability improvement. The book covers each of these technologies extensively applied to full chapters devoted to detail studies, methodologies and practical usages. One of the central concepts in the book is how we evolve from industry 4.0 to industry 5.0. Therefore, Industry 4.0 relies on the automation and data exchange in manufacturing technologies using cyber-physical systems, the Internet of Things and cloud computing route to intelligent factories. During this phase, it improves operational efficiency, predictive maintenance and real-time monitoring which lowers down time and other operating costs by considerable amount.</jats:p>

Song Gao

Geography • 0

<p>Nowadays, artificial intelligence (AI) is bringing tremendous new opportunities and challenges to geospatial research. Its fast development is powered by theoretical advancement, big data, computer hardware (e.g., the graphics processing unit, or GPU), and high-performance computing platforms that support the development, training, and deployment of AI models within a reasonable amount of time. Recent years have witnessed significant advances in geospatial artificial intelligence (GeoAI), which is the integration of geospatial studies and AI, especially machine learning and deep learning methods and the latest AI technologies in both academia and industry. GeoAI can be regarded as a study subject to develop intelligent computer programs to mimic the processes of human perception, spatial reasoning, and discovery about geographical phenomena and dynamics; to advance our knowledge; and to solve problems in human environmental systems and their interactions, with a focus on spatial contexts and roots in geography or geographic information science (GIScience). Thus, it would require the knowledge of AI theory, programming and computation practices as well as geographic domain knowledge to be competent in GeoAI research. There have already been increasingly collaborative GeoAI studies for GIScience, remote sensing, physical environment, and human society. It is a good time to provide a key reference list for educators, students, researchers, and practitioners to keep up with the latest GeoAI research topics. This bibliographical entry will first review the historical roots for AI in geography and GIScience and then list up to ten selective recent works with annotations that briefly describe their importance for each topic of interest in the GeoAI landscape, ranging from fundamental spatial representation learning to spatial predictions and to various advancements in cartography, earth observation, social sensing, and geospatial semantics.</p>

Yiming Huang, Ravi U. Sheth, Shijie Zhao et al.

Nature Biotechnology • 2023

Pure bacterial cultures remain essential for detailed experimental and mechanistic studies in microbiome research, and traditional methods to isolate individual bacteria from complex microbial ecosystems are labor-intensive, difficult-to-scale and lack phenotype–genotype integration. Here we describe an open-source high-throughput robotic strain isolation platform for the rapid generation of isolates on demand. We develop a machine learning approach that leverages colony morphology and genomic data to maximize the diversity of microbes isolated and enable targeted picking of specific genera. Application of this platform on fecal samples from 20 humans yields personalized gut microbiome biobanks totaling 26,997 isolates that represented >80% of all abundant taxa. Spatial analysis on >100,000 visually captured colonies reveals cogrowth patterns between Ruminococcaceae , Bacteroidaceae , Coriobacteriaceae and Bifidobacteriaceae families that suggest important microbial interactions. Comparative analysis of 1,197 high-quality genomes from these biobanks shows interesting intra- and interpersonal strain evolution, selection and horizontal gene transfer. This culturomics framework should empower new research efforts to systematize the collection and quantitative analysis of imaging-based phenotypes with high-resolution genomics data for many emerging microbiome studies. A machine learning isolation and genotyping platform enable high-throughput bacterial culture generation.

K. Lesnik, Wenfang Cai, Hong Liu

Environmental Science &amp; Technology • 2019

Stability as evaluated by functional resistance and resilience is critical to the effective operation of environmental biotechnologies. To date, limited tools have been developed that allow operators of these technologies to predict functional responses to environmental and operational disturbances. In the present study, 17 Microbial Fuel Cells (MFCs) were exposed to a low pH perturbation. MFC power dropped 52.7 ± 35.8% during the low pH disturbance. Following the disturbance, 3 MFCs did not recover while 14 took 60.7 ± 58.3 hours to recover to previous current output levels. Machine learning models based on genomic data inputs were developed and evaluated on their ability to predict resistance and resilience. Resistance and resilience levels corresponding to risk of deactivation could be classified with 70.47 ± 15.88% and 65.33 ± 19.71 % accuracy, respectively. Models predicting resistance and resilience coefficient values projected post-perturbation current drops within 6.7 - 15.8% and recovery times within 5.8 - 8.7% of observed values. Results suggest that abundance of specific genera are better predictors of resistance while overall microbial community structure more accurately predicts resilience. This approach can be used to assess operational risk and is a first step towards further understanding and improving overall stability of environmental biotechnologies.

M. S. Chaitanya, Uday Kiran Reddy B, S. K et al.

2025 6th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI) • 2025

This research study proposes a novel Smart Irrigation System that integrates Microbial Fuel Cells (MFCs) with IoT and Machine Learning for sustainable agricultural practices. The system uses MFCs to generate electricity from soil microorganisms, providing a renewable energy source for irrigation. IoT sensors monitor real-time environmental parameters such as temperature, humidity, and soil moisture, and transmit data to cloud platforms for analysis. Machine learning algorithms are used to process the historical data, weather forecast, and sensor information in real-time to predict the irrigation requirement and optimize water usage. Renewable energy generation, IoT-based monitoring, and machine learning-driven decision support strategy are integrated in this system to improve water efficiency, reduce energy consumption, and support sustainable agricultural practice.

Jing Wang, Qilun Wang

Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering • 2018

Aiming at the online control problem of microbial fuel cells, this article presents a class of explicit model-predictive control methods based on the machine learning data model. The proposed method is divided into two stages: off-line design and on-line control. In the off-line design stage, (1) a feasible data set is collected by sampling the admissible state in the feasible region and solving the optimal model predictive control law for each sampling data point off-line, (2) a feasible sample discriminator is constructed based on the support vector machine–based binary classification in order to judge the whether the real sampling state is feasible, and (3) according to the feasible samples and the corresponding optimal control law, the control surface of explicit model predictive controller is constructed based on the machine learning methods. In the on-line control stage, the process data are collected in real time and the feasible control output is calculated by using the trained explicit predictive control surface. Extensive testing and comparison among the different machine learning algorithms, such as artificial neural network, extreme learning machine, Gaussian process regression, and relevance vector machine, are performed on the benchmark model of a class of microbial desalination fuel cells. These results demonstrate that the proposed explicit model predictive control method can avoid the exhausting optimization computing and is easy to realize on-line with good control performance.

Fatih Gurcan

PeerJ Computer Science • 0

<jats:sec> <jats:title>Background</jats:title> <jats:p>The continuous increase in carbon dioxide (CO<jats:sub>2</jats:sub>) emissions from fuel vehicles generates a greenhouse effect in the atmosphere, which has a negative impact on global warming and climate change and raises serious concerns about environmental sustainability. Therefore, research on estimating and reducing vehicle CO<jats:sub>2</jats:sub> emissions is crucial in promoting environmental sustainability and reducing greenhouse gas emissions in the atmosphere.</jats:p> </jats:sec> <jats:sec> <jats:title>Methods</jats:title> <jats:p>This study performed a comparative regression analysis using 18 different regression algorithms based on machine learning, ensemble learning, and deep learning paradigms to evaluate and predict CO<jats:sub>2</jats:sub> emissions from fuel vehicles. The performance of each algorithm was evaluated using metrics including R<jats:sup>2</jats:sup>, Adjusted R<jats:sup>2</jats:sup>, root mean square error (RMSE), and runtime.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>The findings revealed that ensemble learning methods have higher prediction accuracy and lower error rates. Ensemble learning algorithms that included Extreme Gradient Boosting (XGB), Random Forest, and Light Gradient-Boosting Machine (LGBM) demonstrated high R<jats:sup>2</jats:sup> and low RMSE values. As a result, these ensemble learning-based algorithms were discovered to be the most effective methods of predicting CO<jats:sub>2</jats:sub> emissions. Although deep learning models with complex structures, such as the convolutional neural network (CNN), deep neural network (DNN) and gated recurrent unit (GRU), achieved high R<jats:sup>2</jats:sup> values, it was discovered that they take longer to train and require more computational resources. The methodology and findings of our research provide a number of important implications for the different stakeholders striving for environmental sustainability and an ecological world.</jats:p> </jats:sec>

Fred Farrell, Orkun S. Soyer, Christopher Quince

• 0

<jats:title>Abstract</jats:title><jats:p>The increasing popularity of genome resolved meta genomics - the binning of genomes of potentially uncultured organisms direct from the environmental DNA - has resulted in a deluge of draft genomes. There is a pressing need to develop methods to interpret this data. Here, we used machine learning to predict functional and metabolic traits of microbes from their genomes. We collated an extensive database of 84 phenotypic traits associated with 9407 prokaryotic genomes and trained different machine learning models on this data. We found that a lasso logistic regression based on the frequency of gene orthologs had the best combination of functional prediction performance and interpretability. This model was able to classify 65 phenotypic traits with greater than 90</jats:p>

M. Mashkour, M. Rahimnejad, F. Raouf et al.

Biofuel Research Journal • 2021

Materials at the nanoscale show exciting and different properties. In this review, the applications of nanomaterials for modifying the main components of microbial fuel cell (MFC) systems (i.e., electrodes and membranes) and their effect on cell performance are reviewed and critically discussed. Carbon and metal-based nanoparticles and conductive polymers could contribute to the growth of thick anodic and cathodic microbial biofilms, leading to enhanced electron transfer between the electrodes and the biofilm. Extending active surface area, increasing conductivity, and biocompatibility are among the significant attributes of promising nanomaterials used in MFC modifications. The application of nanomaterials in fabricating cathode catalysts (catalyzing oxygen reduction reaction) is also reviewed herein. Among the various nanocatalysts used on the cathode side, metal-based nanocatalysts such as metal oxides and metal-organic frameworks (MOFs) are regarded as inexpensive and high-performance alternatives to the conventionally used high-cost Pt. In addition, polymeric membranes modified with hydrophilic and antibacterial nanoparticles could lead to higher proton conductivity and mitigated biofouling compared to the conventionally used and expensive Nafion. These improvements could lead to more promising cell performance in power generation, wastewater treatment, and nanobiosensing. Future research efforts should also take into account decreasing the production cost of the nanomaterials and the environmental safety aspects of these compounds.

Ren Wei, W. Zimmermann

Microbial Biotechnology • 2017

Petroleum‐based plastics have replaced many natural materials in their former applications. With their excellent properties, they have found widespread uses in almost every area of human life. However, the high recalcitrance of many synthetic plastics results in their long persistence in the environment, and the growing amount of plastic waste ending up in landfills and in the oceans has become a global concern. In recent years, a number of microbial enzymes capable of modifying or degrading recalcitrant synthetic polymers have been identified. They are emerging as candidates for the development of biocatalytic plastic recycling processes, by which valuable raw materials can be recovered in an environmentally sustainable way. This review is focused on microbial biocatalysts involved in the degradation of the synthetic plastics polyethylene, polystyrene, polyurethane and polyethylene terephthalate (PET). Recent progress in the application of polyester hydrolases for the recovery of PET building blocks and challenges for the application of these enzymes in alternative plastic waste recycling processes will be discussed.

Katherine E. Duncker, Zachary A. Holmes, L. You

Microbial Cell Factories • 2021

Many applications of microbial synthetic biology, such as metabolic engineering and biocomputing, are increasing in design complexity. Implementing complex tasks in single populations can be a challenge because large genetic circuits can be burdensome and difficult to optimize. To overcome these limitations, microbial consortia can be engineered to distribute complex tasks among multiple populations. Recent studies have made substantial progress in programming microbial consortia for both basic understanding and potential applications. Microbial consortia have been designed through diverse strategies, including programming mutualistic interactions, using programmed population control to prevent overgrowth of individual populations, and spatial segregation to reduce competition. Here, we highlight the role of microbial consortia in the advances of metabolic engineering, biofilm production for engineered living materials, biocomputing, and biosensing. Additionally, we discuss the challenges for future research in microbial consortia.

B. D. Batista, B. Singh

Microbial Biotechnology • 2021

The use of microbial tools to sustainably increase agricultural production has received significant attention from researchers, industries and policymakers. Over the past decade, the market access and development of microbial products have been accelerated by (i) the recent advances in plant‐associated microbiome science, (ii) the pressure from consumers and policymakers for increasing crop productivity and reducing the use of agrochemicals, (iii) the rising threats of biotic and abiotic stresses, (iv) the loss of efficacy of some agrochemicals and plant breeding programs and (v) the calls for agriculture to contribute towards mitigating climate change. Although the sector is still in its infancy, the path towards effective microbial products is taking shape and the global market of these products has increased faster than that of agrochemicals. Promising results from using microbes either as biofertilizers or biopesticides have been continually reported, fuelling optimism and high expectations for the sector. However, some limitations, often related to low efficacy and inconsistent performance in field conditions, urgently need to be addressed to promote a wider use of microbial tools. We propose that advances in in situ microbiome manipulation approaches, such as the use of products containing synthetic microbial communities and novel prebiotics, have great potential to overcome some of these current constraints. Much more progress is expected in the development of microbial inoculants as areas such as synthetic biology and nano‐biotechnology advance. If key technical, translational and regulatory issues are addressed, microbial tools will not only play an important role in sustainably boosting agricultural production over the next few decades but also contribute towards other sustainable development goals, including job creation and mitigation of the impacts of climate change.

S. Mandal, William W. Van Treuren, Richard A. White et al.

Microbial Ecology in Health &amp; Disease • 2015

Background Understanding the factors regulating our microbiota is important but requires appropriate statistical methodology. When comparing two or more populations most existing approaches either discount the underlying compositional structure in the microbiome data or use probability models such as the multinomial and Dirichlet-multinomial distributions, which may impose a correlation structure not suitable for microbiome data. Objective To develop a methodology that accounts for compositional constraints to reduce false discoveries in detecting differentially abundant taxa at an ecosystem level, while maintaining high statistical power. Methods We introduced a novel statistical framework called analysis of composition of microbiomes (ANCOM). ANCOM accounts for the underlying structure in the data and can be used for comparing the composition of microbiomes in two or more populations. ANCOM makes no distributional assumptions and can be implemented in a linear model framework to adjust for covariates as well as model longitudinal data. ANCOM also scales well to compare samples involving thousands of taxa. Results We compared the performance of ANCOM to the standard t-test and a recently published methodology called Zero Inflated Gaussian (ZIG) methodology (1) for drawing inferences on the mean taxa abundance in two or more populations. ANCOM controlled the false discovery rate (FDR) at the desired nominal level while also improving power, whereas the t-test and ZIG had inflated FDRs, in some instances as high as 68% for the t-test and 60% for ZIG. We illustrate the performance of ANCOM using two publicly available microbial datasets in the human gut, demonstrating its general applicability to testing hypotheses about compositional differences in microbial communities. Conclusion Accounting for compositionality using log-ratio analysis results in significantly improved inference in microbiota survey data.

Ting Yu, Shixuan Su, Jing Hu et al.

Advanced Materials • 2022

Microorganisms can serve as biological factories for the synthesis of inorganic nanomaterials that can become useful as nanocatalysts, energy‐harvesting–storage components, antibacterial agents, and biomedical materials. Herein, the development of biosynthesis of inorganic nanomaterials into a simple, stable, and accurate strategy for distinguishing microorganisms from multiple classification levels (i.e., kingdom, order, genus, and species) without gene amplification, biochemical testing, or target recognition is reported. Gold nanoparticles (AuNPs) biosynthesized by different microorganisms differ in color of the solution, and their features can be characterized, including the particle size, the surface plasmon resonance (SPR) spectrum, and the surface potential. The inter‐relation between the features of micro‐biosynthetic AuNPs and the classification of microorganisms are exploited at different levels through machine learning to establish a taxonomic model. This model agrees well with traditional classification methods that offers a new strategy for microbial taxonomic identification. The underlying mechanism of this strategy is related to the biomolecules produced by different microorganisms including glucose, glutathione, and nicotinamide adenine dinucleotide phosphate‐dependent reductase that regulate the features of micro‐biosynthetic AuNPs. This work broadens the application of biosynthesis of inorganic materials through micro‐biosynthetic AuNPs and machine learning, which holds great promise as a tool for biomedical research.

J. Chow, P. Pérez-García, Robert F. Dierkes et al.

Microbial Biotechnology • 2022

Global economies depend on the use of fossil‐fuel‐based polymers with 360–400 million metric tons of synthetic polymers being produced per year. Unfortunately, an estimated 60% of the global production is disposed into the environment. Within this framework, microbiologists have tried to identify plastic‐active enzymes over the past decade. Until now, this research has largely failed to deliver functional biocatalysts acting on the commodity polymers such as polyethylene (PE), polypropylene (PP), polyvinylchloride (PVC), ether‐based polyurethane (PUR), polyamide (PA), polystyrene (PS) and synthetic rubber (SR). However, few enzymes are known to act on low‐density and low‐crystalline (amorphous) polyethylene terephthalate (PET) and ester‐based PUR. These above‐mentioned polymers represent >95% of all synthetic plastics produced. Therefore, the main challenge microbiologists are currently facing is in finding polymer‐active enzymes targeting the majority of fossil‐fuel‐based plastics. However, identifying plastic‐active enzymes either to implement them in biotechnological processes or to understand their potential role in nature is an emerging research field. The application of these enzymes is still in its infancy. Here, we summarize the current knowledge on microbial plastic‐active enzymes, their global distribution and potential impact on plastic degradation in industrial processes and nature. We further outline major challenges in finding novel plastic‐active enzymes, optimizing known ones by synthetic approaches and problems arising through falsely annotated and unfiltered use of database entries. Finally, we highlight potential biotechnological applications and possible re‐ and upcycling concepts using microorganisms.

B. Nataraj, S. Ali, P. Behare et al.

Microbial Cell Factories • 2020

Probiotics have several health benefits by modulating gut microbiome; however, techno-functional limitations such as viability controls have hampered their full potential applications in the food and pharmaceutical sectors. Therefore, the focus is gradually shifting from viable probiotic bacteria towards non-viable paraprobiotics and/or probiotics derived biomolecules, so-called postbiotics. Paraprobiotics and postbiotics are the emerging concepts in the functional foods field because they impart an array of health-promoting properties. Although, these terms are not well defined, however, for time being these terms have been defined as here. The postbiotics are the complex mixture of metabolic products secreted by probiotics in cell-free supernatants such as enzymes, secreted proteins, short chain fatty acids, vitamins, secreted biosurfactants, amino acids, peptides, organic acids, etc. While, the paraprobiotics are the inactivated microbial cells of probiotics (intact or ruptured containing cell components such as peptidoglycans, teichoic acids, surface proteins, etc.) or crude cell extracts (i.e. with complex chemical composition)”. However, in many instances postbiotics have been used for whole category of postbiotics and parabiotics. These elicit several advantages over probiotics like; (i) availability in their pure form, (ii) ease in production and storage, (iii) availability of production process for industrial-scale-up, (iv) specific mechanism of action, (v) better accessibility of Microbes Associated Molecular Pattern (MAMP) during recognition and interaction with Pattern Recognition Receptors (PRR) and (vi) more likely to trigger only the targeted responses by specific ligand-receptor interactions. The current review comprehensively summarizes and discussed various methodologies implied to extract, purify, and identification of paraprobiotic and postbiotic compounds and their potential health benefits.

S. De Corte, T. Hennebel, B. De Gusseme et al.

Microbial Biotechnology • 2011

While precious metals are available to a very limited extent, there is an increasing demand to use them as catalyst. This is also true for palladium (Pd) catalysts and their sustainable recycling and production are required. Since Pd catalysts exist nowadays mostly under the form of nanoparticles, these particles need to be produced in an environment‐friendly way. Biological synthesis of Pd nanoparticles (‘bio‐Pd’) is an innovative method for both metal recovery and nanocatalyst synthesis. This review will discuss the different bio‐Pd precipitating microorganisms, the applications of the catalyst (both for environmental purposes and in organic chemistry) and the state of the art of the reactors based on the bio‐Pd concept. In addition, some main challenges are discussed, which need to be overcome in order to create a sustainable nanocatalyst. Finally, some outlooks for bio‐Pd in environmental technology are presented.

Hang Xu, Yong Xiao, Meiying Xu et al.

Nanotechnology • 2018

Bimetallic nanoparticles (NPs) often exhibit improved catalytic performance due to the electronic and spatial structure changes. Herein, a novel green biosynthesis method for Pd–Pt alloy NPs using Shewanella oneidensis MR-1 was proposed. The morphology, size and crystal structure of Pd–Pt alloy NPs were studied by a suite of characterization techniques. Results showed Pd–Pt alloy NPs were successfully synthesized inside and outside the cell. The biosynthesized Pd–Pt alloy NPs were polycrystalline and face-centered-cubic structure with the particle size ranged from 3–40 nm. Furthermore, the catalytic experiment demonstrated that the Pd–Pt alloy NPs exhibited the highest performance for the catalytic reduction of nitrophenol and azo dyes compared with the as-synthesized Pd and Pt monometallic NPs. This enlarged catalytic activity resulted from the synergistic effect of Pd and Pt element. Thereby, this paper provided a simple biosynthesis method for producing bimetallic alloy nanocatalyst with superior activity for contaminant degradation.