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A nature-inspired solution (NIS) methodology is proposed as a systematic platform for innovation and to inform transformative technology required to address Grand Challenges, including sustainable development. Scalability, efficiency, and resilience are essential to nature, as they are to engineering processes. They are achieved through underpinning fundamental mechanisms, which are grouped as recurring themes in the NIS approach: hierarchical transport networks, force balancing, dynamic self-organization, and ecosystem properties. To leverage these universal mechanisms, and incorporate them effectively into engineering design, adaptations may be needed to accommodate the different contexts of nature and engineering applications. Nature-inspired chemical engineering takes advantage of the NIS methodology for process intensification, as demonstrated here in fluidization, catalysis, fuel cell engineering, and membrane separations, where much higher performance is achieved by rigorously employing concepts optimized in nature. The same approach lends itself to other applications, from biomedical engineering to information technology and architecture.

Manufacturing industries are aiming to reduce weight of the products at uncompromised structural performance. Topology optimization is a reliable technique to achieve the improved topologies at minimum material utilization. Nature is known as best manufacturer to bring complex structures with the existed materials. The present research is aimed to identify the constructal design patterns that existed in nature through topology optimization. Structures with different boundary conditions are modeled and optimized using Solid Isotropic Material Penalization (SIMP) method through ANSYS software under structural and thermal loading conditions. The performed case studies revealed that the optimized topologies are very close to the nature inspired patterns.

Here, we aimed to develop manipulatable catalytic paradigms, expounding how to endue the catalysts with on/off-selective catalytic ability. Inspired from the nature, this target was hit by reporting a polymer catalyst prepared by imprinting substrate molecules in a metal-nanoparticle- encapsulated smart polymer that held mobile molecular chains in the polymeric networks. These mobile functional chains, by being “frozen” or “thawed”, allowed the smart polymer carrier to function with either recognition or misrecognition properties for the imprinted substrate, resulting in on/off-selective catalytic ability. This catalyst exhibited selective catalytic behaviors towards the imprinted substrate at relatively low temperatures, due to the “frozen” functional chains which admitted recognition properties. In contrast, this catalyst did not provide selective catalytic behaviors at relatively high temperatures, arising from the growing mobility of these functional chains which dismantled the recognition system. In this way, this catalyst showed the on/off- selective catalytic ability. The formulation of this catalyst shares an attractive prospect with the development of programmable catalytic processes.

The utilization of nature-inspired algorithms for logistic domains and its potential to manage uncertainty evolve solutions and conduct optimization leftovers to be an antecedent research domain. The present article has considered primary bio-inspired processes to solve the logistics problem systematically until Feb 2020. We have opted 36 articles to summarize and review. The significant algorithm has been ranked into five primary categories: ant colony optimization, particle swarm optimization, memetic algorithm, genetic algorithm, and artificial bee colony. It is evident from the outcomes that within the past 10 years, bio-inspired procedures have experienced fast progress. They have prosperously been applied for optimization and design of particularly intricate systems like logistics distribution systems. It can be deducted from the outcomes that the other algorithms in the logistics distribution’s optimization have to be enhanced, leading to the elevation of the effectiveness of logistics enterprises. Also, the policy assistance for the logistics organization has been empowered to boost the efficient and healthy progress of it. We can observe that the genetic algorithm and its hybrids have indicated the best efficiency until now. So, it is essential to investigate the logistics distribution route optimization by recent nature-inspired algorithms for optimizing the logistics and selecting a rational distribution scheme.

Flexible electronics has received growing attention due to its intriguing properties in many fields, including health monitoring, human-machine interface etc. However, the narrow working range, dis...

Unconventional techniques to benefit from the low-cost and high-efficiency monocrystalline silicon solar cells can lead to new device capabilities and engineering prospects. Here, a nature-inspired spherical solar cell is demonstrated, which is capable of capturing light three-dimensionally. The proposed cell architecture is based on monocrystalline silicon and is fabricated using a corrugation technique. The spherical cell shows an increase in power output by up to 101% with respect to a traditional flat cell with the same projection area using different reflective materials. Finally, the spherical cell shows advantages in terms of enhanced heat dissipation and reduced dust accumulation over conventional cells.

Nowadays, it has become a necessity for operational and reliable operation of networks due to our increased dependency over the network services. However, intruders are continuously attempting to break into the networks and disturbing the network services using a variety of attack vectors and technologies. This motivates us to develop the techniques that ensure operational and reliable network, even in changing scenarios. Recently, most of the researchers have focused on the employment of techniques inspired by a natural phenomenon to detect the intrusions effectively. Nature-Inspired Techniques (NITs) have the ability to adapt to a constantly changing environment. Thus, they help to provide in-built resiliency to failures and damages, collaborative, survivable, self-organizing and self-healing capabilities to IDSs. The paper presents an analysis of NITs, and their classification based on the source of their inspiration. A comprehensive review of various NITs employed in intrusion detection is presented. Analysis of prominent research indicates that NITs based IDSs offers high detection rate and low false positive rate in comparison to the conventional IDSs. The NITs enables more flexibility in IDSs because of their employability into hybrid IDSs leading to detection on the basis of anomalies as well as signatures, leading in improving detection results of known and unknown attacks. The paper attempts to identify NITs’ advantages, disadvantages and significant challenges to the successful implementation of NITs in the intrusion detection area. The main intention of this paper is to explore and present a comprehensive review of the application of NITs in intrusion detection, covering a variety of NITs, study of the techniques and architectures used and further the contribution of NITs in the field of intrusion detection. Finally, the paper ends with the conclusion and future aspects.

A common problem that the world faces is the waste of energy. In water pump stations, the situation is not different. Employees still use the traditional, manual, and empirical operation of the water pumps. This process gradually generates unwanted losses of energy and money. To avoid such profligacy, this paper presents two Adaptive and one Improved Multi-population based nature-inspired optimization algorithms for water pump station scheduling. The main goal here is to obtain the optimal operational scheduling of each group of pumps, wasting the minimum amount of energy. Therefore, since the objective function relies on the shaft power consumption of all the pumps running together, our aim becomes feasible. We implemented and tested the algorithms in the main water pump station of Shanghai, in China. Based on traditional multi-population based nature-inspired optimization algorithms, such as Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO), this work adapts and improves the models to fit the complex constraints and characteristics of the system. It also compares and analyses the performance of each method used in this case study, considering the obtained results. The method which demonstrated outperformance was chosen as the best solution for the present problem.

Nowadays, solving hard optimization problems using metaheuristic algorithms has attracted bountiful attention. Generally, these algorithms are inspired by natural metaphors. A novel metaheuristic algorithm, namely Grey Wolf Optimization (GWO), might be applied in the solution of geodetic optimization problems. The GWO algorithm is based on the intelligent behaviors of grey wolves and a population based stochastic optimization method. One great advantage of GWO is that there are fewer control parameters to adjust. The algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. In the present paper, the GWO algorithm is applied in the calibration of an Electronic Distance Measurement (EDM) instrument using the Least Squares (LS) principle for the first time. Furthermore, a robust parameter estimator called the Least Trimmed Absolute Value (LTAV) is applied to a leveling network for the first time. The GWO algorithm is used as a computing tool in the implementation of robust estimation. The results obtained by GWO are compared with the results of the ordinary LS method. The results reveal that the use of GWO may provide efficient results compared to the classical approach.

While many cancer centers suggest treating pain with medication and nondrug treatment, few include the use of virtual reality (VR) as an alternative for stress and pain relief therapy. The purpose of this research was to determine whether a nature-inspired VR simulation reduced stress and pain levels among patients in a cancer treatment center. Using a repeated measures design, 50 patients attending their regularly scheduled chemotherapy infusion were measured for pain and stress during their intravenous (IV)/port access. At the patient’s second visit, they viewed a nature-inspired VR simulation while receiving their IV/port access and were measured for pain and stress again. The paired, one-tailed t tests found significant increases in relaxation, feelings of peace, and positive distractions. While patients felt significantly less frustrated, measures for stress and pain were not significant. Future research should include additional stress and pain measures to determine the viability of VR for chemotherapy infusions.

Autonomous robot navigation (ARN) requires intelligent systems equipped with human capabilities to achieve controlled motion among flats, ridgesor obstacles while avoiding collision in known or par...

The design of materials that can mimic encoded shape evolution in nature is important but challenging. Here we present a simple yet versatile strategy for programming the sequential deformation of hydrogel sheets to acquire desired actuation motions and geometric shapes. The method relies on the dual-gradient structure-enabled snapping deformation of hydrogels through the accumulation and burst release of elastic energy, as well as the patterning of the prestored energy in gels. Pretreating distinct regions of the hydrogel sheets with different durations of the same stimulus (or with different stimuli) allows for locally prestoring chemical energy that can be converted to temporospatially patterned elastic energy and abruptly released to drive the successive snapping of different regions of hydrogels in predefined onset sequences. The sequence of energy release (i.e., the sequence of snapping deformation) of the local regions for hydrogels can be reprogrammed by different local prestimulation methods, which allows one gel to deform into various defined geometric configurations. The general mathematic criteria are developed to predict the energy release and snapping of the hydrogels. This work can provide guidance for the design of new-generation actuators and soft robotics.

The traditional many-to-one transmission pattern allows all sensor nodes to propagate their packets toward a single sink in wireless sensor networks, resulting in imbalanced energy depletion in the network. In this paper, we propose a data dissemination strategy named transmission with multiple load balancing schemes (TMLBSs) which utilizes a nature-inspired approach, ant colony optimization, to construct transmission paths for nodes in different places. The distinct characteristics of TMLBS are three load balancing schemes, which help to construct transmission paths formed into a path tree. The first one is the load decentralization scheme, which creates multiple path subtrees in the early stage and scatters the whole load to such path subtrees, so as to avoid excessive load concentration. The second one is the load maintenance scheme, which adopts an appropriate pheromone update mechanism to retain previous good paths, yielding excellent next-generation solutions. The last one is the load diversion scheme, which employs the heuristic factor to transfer traffic load to paths with light traffic load to eliminate poor solutions. Finally, extensive simulations are conducted to validate the effectiveness and advantages of the new transmission strategy.

A bidirectional turbine used in an oscillating water column device extracts wave energy from oscillating airflow. To improve its power output, a concept of static extended trailing edge found in the wings of owl and merganser was adopted. The static extended trailing edge with 0–10% of chord length ( C) was analyzed for different flow coefficients via Reynolds-averaged Navier–Stokes equation-based computational fluid dynamics (CFD) analysis. ANSYS-CFX 15.0 was used to simulate the flow. Grid convergence index was calculated to obtain optimum mesh, and numerical validation was done with experimental results. The static extended trailing edge with 5% C enhanced relative mean torque by 23.4% and, reduced relative mean efficiency by 5.4%, before stall condition. The modification increased pressure difference between the suction side and the pressure side and enhanced torque. The increased pressure drop reduced the efficiency. A further longer static extended trailing edge showed poorer stall characteristics.

Inspired by marine compounds the derivatization of the natural pyrrolo[2,3-d]pyrimidine lead scaffold led to a series of novel compounds targeting the histamine H3 receptor. The focus was set on improved binding towards the receptor and to establish an initial structure-activity relationship for this compound class based on the lead structure (compound V, Ki value of 126 nM). As highest binding affinities were found with 1,4-bipiperidines as basic part of the ligands, further optimization was focused on 4-([1,4′-bipiperidin]-1′-yl)-pyrrolo[2,3-d]pyrimidines. Related pyrrolo[2,3-d]pyrimidines that were isolated from marine sponges like 4-amino-5-bromopyrrolo[2,3-d]pyrimidine (compound III), showed variations in halogenation pattern, though in a next step the impact of halogenation at 2-position was evaluated. The chloro variations did not improve the affinity compared to the dehalogenated compounds. However, the simultaneous introduction of lipophilic cores with electron-withdrawing substitution patterns in 7-position and dehalogenation at 2-position (11b, 12b) resulted in compounds with significantly higher binding affinities (Ki values of 7 nM and 6 nM, respectively) than the initial lead structure compound V. The presented structures allow for a reasonable structure-activity relationship of pyrrolo[2,3-d]pyrimidines as histamine H3 receptor ligands and yielded novel lead structures within the natural compound library against this target.

Raman spectroscopy is an optical vibrational spectroscopic technique capable of probing specific biochemical structures and conformation of tissue and cells in biomedical systems. This work aims to assess the clinical utility of a fiber-optic Raman spectroscopy with nature-inspired genetic algorithms for enhancing in vivo detection and diagnosis of nasopharyngeal carcinoma (NPC) patients. The Raman diagnostic platform is developed based on simultaneous fingerprint (FP) and high-wavenumber (HW) fiber-optic Raman endoscopy associated with genetic algorithms-partial least-squares-linear discriminant analysis (GA-PLS-LDA). A total of 2126 in vivo FP/HW Raman spectra (598 NPC, 1528 normal) acquired from 113 tissue sites of 14 NPC patients and 48 healthy subjects during nasopharyngeal endoscopic examinations. Distinct Raman peaks have been identified (853 cm-1 - proteins, 1209 cm-1 - phenylalanine, 1265 cm-1 - proteins, 1335 cm-1 - proteins and nucleic acids, 1554 cm-1 - tryptophan, porphyrin, 2885 cm-1 - lipids, 2940 cm-1 - proteins, 3009 cm-1 - lipids, and 3250 cm-1 - water) that are related to the significant biochemical changes ( p < 1 × 10-5) in NPC compared to normal tissue. Raman diagnostic performance is evaluated through the leave-one-object (tissue site)-out cross-validation (LOOCV) method. A statistically significant GA-PLS-LDA model ( p < 1 × 10-5) on FP/HW Raman yields a CV diagnostic accuracy of 98.23% (111/113), sensitivity of 93.33% (28/30), and specificity of 100% (83/83) for NPC classification. This work demonstrates that the fiber-optic FP/HW Raman diagnostic platform developed has great promise for improving real-time in vivo detection and diagnosis of NPC at the molecular level during clinical nasopharyngeal endoscopy.

The development of an innovative interfacial wetting strategy known as liquid infused systems offers great promise for the advanced design of superwetting and superantiwetting substrates to overcome the drawbacks of textured surfaces classified under the heading of Cassie/Wenzel states. The potential value of nature-inspired surfaces has significant potential to address scientific and technological challenges within the field of interfacial chemistry. The objective of the current review is to provide insights into a fruitful and young field of research, highlight its historical developments, examine its nature-inspired design principles, gauge recent progress in emerging applications, and offer a fresh perspective for future research.

Nature-inspired water collection technology has been well-recognized as an effective solution for relieving water shortage hardships, and yet remains challenging when being used in an actual natural environment. In this work, we have successfully developed a promising water-collecting windmill that can be used in complex windy environments, by taking integrative inspiration from the liquid-manipulation strategies adopted by rice leaves, cacti, Nepenthes pitcher plants, and butterflies. The unique directional grooves on the blade surface with ridge-like walls with a shape gradient, combined with a molecular slippery layer, are crucial for not only water deposition but also directional drainage in water collection. Besides, the engineering design of rotatable blades turns the adverse effect of strong winds into a positive one, along with the nature-inspired surface topography and physicochemical property. Such a novel windmill has shown unprecedented water-collecting performance in a static environment, in strong wind, and in intermittent wind. Furthermore, the windmill can sense the wind-blowing direction and adjust its facing direction accordingly to ensure maximum utilization of wind power. It is believed that this work will bring a broad guiding significance to the design of smart water-harvesting materials and devices for application in more complex situations.

Ordered protein assemblies are attracting interest as next-generation biomaterials with a remarkable range of structural and functional properties, leading to potential applications in biocatalysis, materials templating, drug delivery and vaccine development. This Review covers ordered protein assemblies including protein nanowires/nanofibrils, nanorings, nanotubes, designed two- and three-dimensional ordered protein lattices and protein-like cages including polyhedral virus-like cage structures. The main focus is on designed ordered protein assemblies, in which the spatial organization of the proteins is controlled by tailored noncovalent interactions (including metal ion binding interactions, electrostatic interactions and ligand–receptor interactions among others) or by careful design of modified (mutant) proteins or de novo constructs. The modification of natural protein assemblies including bacterial S-layers and cage-like and rod-like viruses to impart novel function, e.g. enzymatic activity, is also considered. A diversity of structures have been created using distinct approaches, and this Review provides a summary of the state-of-the-art in the development of these systems, which have exceptional potential as advanced bionanomaterials for a diversity of applications.

In this paper optimization algorithms name as particle swarm optimization (PSO) and teacher learning based optimization (TLBO) has been implemented on continuous stirred tank reactor (CSTR). Conventional PID controller, PSO based PID control scheme and TLBO based PID control scheme are used to control the system concentration and temperature. Optimization algorithms are used to improve system performance by minimizing mean square error (MSE) and optimize the controller parameters like Kp, Ki, Kd. The Simulation results shows that TLBO based PID controller gives better performance, optimized values of controller parameters and minimum values of MSE in less iteration as compare to PSO based PID controller and Conventional PID controller.

Despite the progress in the area of food safety, foodborne diseases still represent a massive challenge to the public health systems worldwide, mainly due to the substantial inefficiencies across the farm-to-fork continuum. Here, we report the development of a nano-carrier platform, for the targeted and precise delivery of antimicrobials for the inactivation of microorganisms on surfaces using Engineered Water Nanostructures (EWNS). An aqueous suspension of an active ingredient (AI) was used to synthesize iEWNS, with the ‘i’ denoting the AI used in their synthesis, using a combined electrospray and ionization process. The iEWNS possess unique, active-ingredient-dependent physicochemical properties: i) they are engineered to have a tunable size in the nanoscale; ii) they have excessive electric surface charge, and iii) they contain both the reactive oxygen species (ROS) formed due to the ionization of deionized (DI) water, and the AI used in their synthesis. Their charge can be used in combination with an electric field to target them onto a surface of interest. In this approach, a number of nature-inspired antimicrobials, such as H2O2, lysozyme, citric acid, and their combination, were used to synthesize a variety of iEWNS-based nano-sanitizers. It was demonstrated through foodborne-pathogen-inactivation experiments that due to the targeted and precise delivery, and synergistic effects of AI and ROS incorporated in the iEWNS structure, a pico-to nanogram-level dose of the AI delivered to the surface using this nano-carrier platform is capable of achieving 5-log reductions in minutes of exposure time. This aerosol-based, yet ‘dry’ intervention approach using iEWNS nano-carrier platform offers advantages over current ‘wet’ techniques that are prevalent commercially, which require grams of the AI to achieve similar inactivation, leading to increased chemical risks and chemical waste byproducts. Such a targeted nano-carrier approach has the potential to revolutionize the delivery of antimicrobials for sterilization in the food industry.

Artificial biology is an emerging concept that aims to design and engineer the structure and function of natural cells, organelles, or biomolecules with a combination of biological and abiotic building blocks. Cell mimicry focuses on concepts that have the potential to be integrated with mammalian cells and tissue. In this feature article, we will emphasize the advancements in the past 3–4 years (2017-present) that are dedicated to artificial enzymes, artificial organelles, and artificial mammalian cells. Each aspect will be briefly introduced, followed by highlighting efforts that considered key properties of the different mimics. Finally, the current challenges and opportunities will be outlined. This article is categorized under: Nanotechnology Approaches to Biology > Nanoscale Systems in Biology.

There is a groundswell of interest in applying phototrophic microorganisms, specifically microalgae and cyanobacteria, for biotechnology and ecosystem service applications. However, there are inherent challenges associated with conventional routes to their deployment (using ponds, raceways and photobioreactors) which are synonymous with suspension cultivation techniques. Cultivation as biofilms partly ameliorates these issues; however, based on the principles of process intensification, by taking a step beyond biofilms and exploiting nature inspired artificial cell immobilisation, new opportunities become available, particularly for applications requiring extensive deployment periods (e.g., carbon capture and wastewater bioremediation). We explore the rationale for, and approaches to immobilised cultivation, in particular the application of latex-based polymer immobilisation as living biocomposites. We discuss how biocomposites can be optimised at the design stage based on mass transfer limitations. Finally, we predict that biocomposites will have a defining role in realising the deployment of metabolically engineered organisms for real world applications that may tip the balance of risk towards their environmental deployment.

Emulating the unique combination of structural, compositional, and functional gradation in natural materials is exceptionally challenging. Many natural structures have proved too complex or expensive to imitate using traditional processing techniques despite recent advances. Recent innovations within the field of additive manufacturing (AM) or 3D Printing (3DP) have shown the ability to create structures that have variations in material composition, structure, and performance, providing a new design-for-manufacturing platform for the imitation of natural materials. AM or 3DP techniques are capable of manufacturing structures that have significantly improved properties and functionality over what could be traditionally-produced, giving manufacturers an edge in their ability to realize components for highly-specialized applications in different industries. To this end, the present work reviews fundamental advances in the use of naturally-inspired design enabled through 3DP / AM, how these techniques can be further exploited to reach new application areas, and the challenges that lie ahead for widespread implementation. An example of how these techniques can be applied towards a total hip arthroplasty application is provided to spur further innovation in this area.

Breast cancer is one of the significant reasons for death among women in today’s world. As early detection saves the patient, much research work is going on in this area to diagnose the disease in the early stages. Traditional machine learning models can effectively diagnose breast cancer even though still improvement is needed to get the most accurate results. Swarm intelligence algorithms help in giving an accurate diagnosis. In this paper, different swarm intelligence algorithms like Particle Swarm optimization, Ant colony, Firefly algorithm and Bat optimization were implemented. The performance of these algorithms was evaluated using the Naive Bayes classifier. The results show that particle swarm optimization and firefly algorithm has generated the highest accuracy of 98%.

With the aid of machine learning method, namely artificial neural networks, we established data-driven proxy models that could be utilized to maximize the net present value of a waterflooding process by adjusting the well control injection rates over a production period. These data-driven proxies were maneuvered on two different case studies, which included a synthetic 2D reservoir model and a 3D reservoir model (the Egg Model). Regarding the algorithms, we applied two different nature-inspired metaheuristic algorithms, i.e., particle swarm optimization and grey wolf optimization, to perform the optimization task. Pertaining to the development of the proxy models, we demonstrated that the training and blind validation results were excellent (with coefficient of determination,R2being about 0.99). For both case studies and the optimization algorithms employed, the optimization results obtained using the proxy models were all within 5% error (satisfied level of accuracy) compared with reservoir simulator. These results confirm the usefulness of the methodology in developing the proxy models. Besides that, the computational cost of optimization was significantly reduced using the proxies. This further highlights the significant benefits of employing the proxy models for practical use despite being subject to a few constraints.

Wave-induced scour depth below pipelines is a physically complex phenomenon, whose reliable prediction may be challenging for pipeline designers. This study shows the application of adaptive neuro-fuzzy inference system (ANFIS) incorporated with particle swarm optimization (ANFIS-PSO), ant colony (ANFIS-ACO), differential evolution (ANFIS-DE) and genetic algorithm (ANFIS-GA) and assesses the scour depth prediction performance and associated uncertainty in different scour conditions including live-bed and clear-water. To this end, the non-dimensional parameters Shields number (θ), Keulegan–Carpenter number (KC) and embedded depth to diameter of pipe ratio (e=D) are considered as prediction variables. Results indicate that the ANFIS-PSO model (R 2 live bed ¼ 0:832 and R 2 clear water ¼ 0:984) is the most accurate predictive model in both scour conditions when all three mentioned non-dimensional input parameters are included. Besides, the ANFIS-PSO model shows a better prediction performance than recently developed models. Based on the uncertainty analysis results, the prediction of scour depth is characterized by larger uncertainty in the clear-water condition, associated with both model structure and input variable combination, than in live-bed condition. Furthermore, the uncertainty in scour depth prediction for both live-bed and clear-water conditions is due more to the input variable combination (R-factor ave ¼ 4:3) than it is due to the model structure (R-factor ave ¼ 2:2).

New nature-inspired and plant-derived p-hydroxycinnamate esters and p-hydroxycinnamate diesters provide excellent protection against UV radiation when incorporated into a matrix. Herein, an efficient and sustainable pathway is reported to graft these phenolic compounds onto cellulose nanocrystals (CNCs) via click-type copper-catalyzed azide/alkyne cycloaddition (CuAAC) reaction. The successful grafting of the phenolic esters on CNC surface was evidenced by a range of chemical analyses, and the degrees of substitution (DS) of the CNC were found to depend on the structure of the phenolic ester grafted. Moreover, aqueous suspensions of the phenolic ester-grafted CNCs not only strongly absorb in both the UVA and UVB regions, but they also exhibit average to very high photostability. Their wide spectrum UV-absorbing properties and their stability upon exposure to UV are highly influenced by the structure of the phenolic ester, particularly by the extra ester group in p-hydroxycinnamate diesters. These findings demonstrate that cellulose nanocrystals decorated with such plant-derived and nature-inspired phenolic esters are promising sustainable nanomaterials for anti-UV applications.

Nature is fascinating. It is even more awesome when scru-tinised from a scientific, mechanistic angle across lengthscales, down to the nanoscale, unravelling complex structuresand patterns. Natural systems consist of a dynamic, complexassembly of interacting components and systems, leadingto emergent behaviour, achieving tasks ultimately aiming atsurvival, which, very roughly, translates into sustaining cur-rent activities. Such activities include processing matter andenergy, transporting fluids, changing phase state to obtainproducts of high quality, and communication. The similar-ity is striking with what processes in chemical engineeringare intended for, since they also deal with the processing ofmatter, energy and data...

The future of food is one of the major world-wide challenges. In this perspective paper, we set-up a framework for a multi-disciplinary future food systems approach, building on the UN Sustainable Development Goals. We hereby combine a truly sustainable approach including social science aspects combined with the One Health approach. Scientists from a large number of backgrounds have addressed four key areas that are discussed in more detail in this paper: (i) nature inspired food production, (ii) sustainable immune resilience, (iii) social and cultural change of food behavior, and (iv) food fairness. We believe that transformations to integrated future food system approaches should move beyond single solutions and can only be solved by working in transdisciplinary settings of science, society, and industry.

Well placement optimization is one of the major challenging factors in the field development process in the oil and gas industry. This chapter aims to survey prominent metaheuristic techniques, which solve well the placement optimization problem. The well placement optimization problem is considered as high dimensional, discontinuous, and multi-model optimization problem. Moreover, the computational expenses further complicate the issue. Over the last decade, both gradient-based and gradient-free optimization methods were implemented. Gradient-free optimization, such as the particle swarm optimization, genetic algorithm, is implemented in this area. These optimization techniques are utilized as standalone or as the hybridization of optimization methods to maximize the economic factors. In this chapter, the authors survey the two most popular nature-inspired metaheuristic optimization techniques and their application to maximize the economic factors.

With the advent of big data, interest for new data mining methods has increased dramatically. The main drawback of traditional data mining methods is the lack of comprehensibility. In this paper, the firefly algorithm was employed for standalone binary classification, where each solution is represented by two classification rules that are easy understandable by users. Implicitly, the feature selection is also performed by the algorithm. The results of experiments, conducted on three well-known datasets publicly available on web, were comparable with the results of the traditional methods in terms of accuracy and, therefore, the huge potential was exhibited by the proposed method.

Nature-inspired structures as transparent conducting electrodes are exciting alternatives to conventional TCEs because they provide higher transmittance and conductivity at low temperatures on flexible substrates. The current work is focussed to develop a metal mesh structure with the help of plant leaf vein as a template. Bilayer metal mesh of thickness 85%) and electrical resistivity of the order of 10−4 Ω cm. The metal mesh TCE based on leaf vein template opens up new ways of obtaining large charge transfer resolving the junction resistance problem encountered in case of metal nanowires.

Many biological materials, such as the squid beak and the spinal disc, have a combination of stiff and soft parts with very different mechanical properties, for example, the elastic modulus (stiffness) of the stiffest part of the squid beak is about 100 times that of the softest part. Researchers have attempted to mimic such structures using hydrogels but have not succeeded in synthesizing bulk gels with such large variations in moduli. Here, we present a general approach that can be used to form hydrogels with two or more zones having appreciably different mechanical characters. For this purpose, we use a technique developed in our lab for creating hybrid hydrogels with distinct zones. For the soft zone of the gel, we form a polymer network using a conventional acrylic monomer [ N, N'-dimethylacrylamide (DMAA)] and with laponite (LAP) nanoparticles as the cross-linkers. For the stiff zone, we combine DMAA, LAP, and a methacrylated silica precursor ([3-(methacryloyloxy)-propyl]trimethoxy-silane). When this mixture is polymerized, nanoscale silica particles (∼300 nm in diameter) are formed, and these serve as additional cross-links between the polymer chains, making this network very stiff. The unique character of each zone is preserved in the hybrid gel, and different zones are covalently linked to each other, thereby ensuring robust interfaces. Rheological measurements show that the elastic modulus of the stiff zone can be more than 100 times that of the soft zone. This ratio of moduli is the highest reported to date in a single, continuous gel and is comparable to the ratio in the squid beak. We present different variations of our soft-stiff hybrid gels, including multizone cylinders and core-shell discs. Such soft-stiff gels could have utility in bioengineering, such as in interfacing stiff medical implants with soft tissues.

Solar steam generation, due to its capability of producing clean water directly by solar energy, is emerging as a promising eco-friendly and energy-efficient technology to address global challenges of water crisis and energy shortage. Although diverse materials and architectures have been explored to improve solar energy utilization, high efficiency in solar steam generation could be accomplished only with external optical and thermal management. For the first time, we report a deployable, three-dimensional (3D) origami-based solar steam generator capable of near full utilization of solar energy. This auxetic platform is designed based on Miura-ori tessellation and is able to efficiently recover radiative and convective heat loss as well as to trap solar energy via its periodic concavity pattern. The 3D solar steam generator device with a nanocarbon composite of graphene oxide and carbon nanotubes being photothermal component in this work shows a very strong dependence between its solar energy efficiency and surface areal density. The device yields an extraordinary solar energy efficiency close to 100% under 1 sun illumination at a highly folded configuration. The 3D origami device can withstand a great number of folding and unfolding cycles and shows unimpaired solar steam generation performances. The unique structural feature of the 3D origami structure offers a new insight into the future development of highly efficient and easily deployable solar steam generator.

A nature-inspired bioorthogonal reaction has been developed, hinging on an inverse-electron-demand Diels-Alder reaction of tetrazine with β-caryophyllene. Readily accessible from the cheap starting material through a scalable synthesis, the newly developed β-caryophyllene chemical reporter displays appealing reaction kinetics and excellent biocompatibility, which renders it applicable to both in vitro protein labeling and live cell imaging. Moreover, it can be used orthogonally to the strain-promoted alkyne-azide cycloaddition for dual protein labeling. This work not only provides an alternative to the existing bioorthogonal reaction toolbox, but also opens a new avenue to utilize naturally occurring scaffolds as bioorthogonal chemical reporters.

In 2015 the Biomimicry Institute released a call ’Biomimicry Global Challenge Design 2015′. The Biomimicry Institute is partnering with the Ray C. Anderson Foundation and food and conservation experts to solicit nature inspired solutions for food systems improvement. We decided to create a feasible concept applicable for next decades with the use of modern technology and biomimicry. In our device, we used a biomimetic approach to design artificial surfaces that are capable of directed liquid transport, based on the principles found in the Texas horned lizard. For this purpose, we analyzed the morphology of the capillary network of Phrynosoma cornutum and characterized the material properties of the animal’s surface. The final result was a balcony cultivator – sustainable device which is divided into three functional parts that mutually support each other. The first is a bottom part for composting, the second part is used for plant cultivation and the third part serves the irrigation. Balcony cultivator project is still at the conceptual stage, but within the Biomimicry Global Design Challenge Accelerator programme, the research team has already solved prototyping, testing, and developing business plans for product launch. We created unique design, which besides artistic value, carries sophisticated thinking and sustainable function.

The current needs of finding more efficient materials with tunable properties have served as a motivation for the scientific community to move toward nature-inspired hierarchical materials adhering to the green analytical chemistry principles also because of the high complexity and efficiency of systems in nature. Such type of materials has exhibited highly dynamic interfacial interactions, which are outstandingly valuable for sensing and energy storage applications. Nature-based systems have displayed remarkable features, in terms of energy density, coulombic efficiency behavior, cycling stability energy, durability, and sensitivity. More interestingly, the aforementioned properties could be tuned by modifying the interfacial interactions within the material components. Along this contribution, recent developments on the design of green analytical nature-inspired hierarchical materials will be addressed, especially paying attention to the relationship between interfacial interactions and material features.

Biological systems include several well-known patterns with different forms and dimensions (nano to macro) such as cracks, whiskers, hierarchical, interlocked, porous, and bristle-like structures, as well as hetero-layered brick-and-mortar structures. These structures and surfaces typically have several functions based on their structure and surface chemistry which include static coloration, self-cleaning, adhesive, underwater navigation and object detection, electric charge generation, and highly sensitive olfactory organ to sense and detect various substances. A new era of bio-inspired sensing materials with an effort to copy structures or functions of living creatures (i.e. plant and animals) has recently attracted widespread attention. This review summarizes the latest developments in bio-inspired sensing and biosensing materials and focuses on the design and mechanisms to analyse their potential in multifunctional sensing applications. Primarily, this review highlights various bioinspired structural features and their functional properties, followed by an overview of the fundamental mechanisms of distinct living creatures, including animals and plants, in brief. Thereafter, various sensing and biosensing applications of these nature-inspired structures/functions are elaborately described. Finally, the challenges and prospects of nature-inspired smart sensing and biosensing technology for the 21st century are provided.

Nature inspired biomimetic superhydrophobic surface (SHS) development is a thrust area of advanced materials science because of its wide interest and application in industries. SHS could effectively prevent the entrance of moisture and therefore find suitable for corrosion prevention. SHS offers protection against metal corrosion by creating an air layer at the metal/coating interface. In addition to this SHS with low surface energy and high surface roughness weaken the adsorption of corrosive ions on the metal surface. The versatile properties possessed by SHS can be tuned by the proper combination of materials and fabrication methods. The present review explores how the synchronised function of materials and coating technologies contribute to the enhanced anticorrosive property of superhydrophobic coatings. Influence of materials (metallic, non-metallic and hybrid) with different structural characteristics & energy properties in boosting anticorrosive performance, the role and need of dynamic fabrication strategies in achieving better corrosion inhibition activities were also given. The review also highlights the recent advances in materials and the state of the art strategies in developing durable anticorrosive-superhydrophobic coatings, insighted from the top most publications in the last three years. An overview of fundamental theories of SHS, mechanisms of corrosion inhibition by SHS, the present challenges that restrict transformation of new findings from lab to industry and novel ideas in overcoming the challenges associated with the fabrication & use of SHS, were also dilineated. A deep understanding of SHS is extremely desired due to its versatile properties, which may extend its application to other potential areas.

Material science is a relevant discipline in support of regenerative medicine. Indeed, tissue regeneration requires the use of scaffolds able to guide and sustain the natural cell metabolism towards tissue regrowth. This need is particularly important in musculoskeletal regeneration, such as in the case of diseased bone or osteocartilaginous regions for which calcium phosphate-based scaffolds are considered as the golden solution. However, various technological barriers related to conventional ceramic processing have thus far hampered the achievement of biomimetic and bioactive scaffolds as effective solutions for still unmet clinical needs in orthopaedics. Driven by such highly impacting socioeconomic needs, new nature-inspired approaches promise to make a technological leap forward in the development of advanced biomaterials. The present review illustrates ion-doped apatites as biomimetic materials whose bioactivity resides in their unstable chemical composition and nanocrystallinity, both of which are, however, destroyed by the classical sintering treatment. In the following, recent nature-inspired methods preventing the use of high-temperature treatments, based on (i) chemically hardening bioceramics, (ii) biomineralisation process, and (iii) biomorphic transformations, are illustrated. These methods can generate products with advanced biofunctional properties, particularly biomorphic transformations represent an emerging approach that could pave the way to a technological leap forward in medicine and also in various other application fields.

It is still a challenge to integrate high sensitivity, mechanical adaptability, and self-powered properties for hydrogels. Herein, we report a conductive polyvinyl alcohol (PVA) hydrogel based on natural nanoclay and cellulose nanofibrils (CNFs). The CNFs and PVA chains could construct a double network structure, resulting in a high mechanical composite hydrogel. Meanwhile, the nanoclay could be well dispersed and immobilized in the network of the hydrogel, thus improving mechanical adaptability of the hydrogel for curved and dynamic surfaces. Moreover, the conductive ions (Al3+) imparted the hydrogel with high conductivity (6.67 S m-1) and gauge factor (1.17). Therefore, the composite hydrogel exhibited high sensitivity to tiny pressure changes, enabling recognition of the complicated sounding and handwriting. More importantly, the composite hydrogel possessed self-powered property, which could generate an output voltage of up to 78 mV. In summary, the multi-functional composite hydrogel may have giant applications in artificial electronic skins or wearable devices.

Nature inspired Swarm algorithms have proven to be effective in solving recent complex optimization problems. Comparing such algorithm is a difficult task due to many facts, the nature of the swarm, the nature of the optimization problem itself and number of controlling parameters of the swarm algorithm. In this work we compared two recent swarm algorithms applied to the community detection problem which are the Bat Algorithm (BA) and Artificial Fish Swarm Algorithm (AFSA). Community detection is an active problem in social network analysis. The problem of detecting communities can be represented as an optimization problem where a quality fitness function that captures the intuition of a community as a group of nodes with better internal connectivity than external connectivity is chosen to be optimized. We also investigated the application of the BA and AFSA in solving the community section problem. And introduced a comparative analysis between the two algorithms and other well-known methods. The study show the effectiveness and the limitations of both algorithms.

Exciting advancements have been made in the field of flexible electronic devices in the last two decades and will certainly lead to a revolution in peoples’ lives in the future. However, because of the poor sustainability of the active materials in complex stress environments, new requirements have been adopted for the construction of flexible devices. Thus, hierarchical architectures in natural materials, which have developed various environment-adapted structures and materials through natural selection, can serve as guides to solve the limitations of materials and engineering techniques. This review covers the smart designs of structural materials inspired by natural materials and their utility in the construction of flexible devices. First, we summarize structural materials that accommodate mechanical deformations, which is the fundamental requirement for flexible devices to work properly in complex environments. Second, we discuss the functionalities of flexible devices induced by nature-inspired structural materials, including mechanical sensing, energy harvesting, physically interacting, and so on. Finally, we provide a perspective on newly developed structural materials and their potential applications in future flexible devices, as well as frontier strategies for biomimetic functions. These analyses and summaries are valuable for a systematic understanding of structural materials in electronic devices and will serve as inspirations for smart designs in flexible electronics. NRF (Natl Research Foundation, S’pore) MOE (Min. of Education, S’pore) Accepted version

Static charge generated by contact electrification on surfaces can lead to many undesirable consequences such as a reduction in the efficiency of manufacturing processes, damage to equipment, and explosions. However, it is extremely challenging to avoid contact electrification because it is ubiquitous: almost all types of materials charge on contact. Here, we coated materials with naturally occurring polydopamine (PDA) and tannic acid (TA) for preparing noncharging surfaces. Importantly, these coatings are very versatile and can be coated on a wide range of materials, including metals, inorganic materials, semiconductors, and polymers. Once coated, the amount of charge generated was found to reduce dramatically at different humidities. The reduction in charge may be due to the radical-scavenging property of PDA and TA. This simple general approach is ideal for coating the vast variety of materials that need to resist charging by contact electrification.

Cell mimicry is a nature inspired concept that aims to substitute for missing or lost (sub)cellular function. This review focuses on the latest advancements in the use of enzymes in cell mimicry for encapsulated catalysis and artificial motility in synthetic bottom-up assemblies with emphasis on the biological response in cell culture or more rarely in animal models. Entities across the length scale from nano-sized enzyme mimics, sub-micron sized artificial organelles and self-propelled particles (swimmers) to micron-sized artificial cells are discussed. Although the field remains in its infancy, the primary aim of this review is to illustrate the advent of nature-mimicking artificial molecules and assemblies on their way to become a complementary alternative to their role models for diverse biomedical purposes.

This study is aimed at addressing the present challenge in tandem catalysis, where the sequential catalytic processes are often not compatible with each other and even engage in each other. The objective is met by reporting an artificial “active” nanoreactor capable of sequential catalytic ability. This nanoreactor is made of nature-inspired “active” compartments consisting of core-and-shell architectures. The “active” compartments allow the nanoreactor to catalytically separate the tandem processes from each other, avoiding the occurrence of mutual interferences. Unlike the use of reported nanoreactors and conventional bi-functional catalysts, the tandem catalysis at this nanoreactor involves neither the mutual competition nor the spatiotemporal mismatching between the tandem processes. This nanoreactor suggests a potential model for achieving sequential catalytic ability.

Composed of the heart, cardiac valves and blood vessels, the blood circulation system can manage the aggregation and the movement of the blood regularly and efficiently. Due to the analogical fluidity of the charge carrier and the blood, such system may act as the inspiration to design the advanced gas-sensing material. As a proof-of-concept, firstly, the porous WO 3 monomer was synthesized and recombined with the one-dimensional NiO monomer through the P-N junction to construct a similar structure. Subsequently, a series of gas-sensing tests towards the ethanol were measured. The obtained result indicates this nature-inspired nanocomposite indeed shows an enhanced gas-sensing property towards the ethanol, testifying the rationality of our design.