In traditional robotics, model-based controllers are usually needed in order to bring a robotic plant to the next desired state, but they present critical issues when the dimensionality of the control problem increases and disturbances from the external environment affect the system behavior, in particular during locomotion tasks. It is generally accepted that the motion control of quadruped animals is performed by neural circuits located in the spinal cord that act as a Central Pattern Generator and can generate appropriate locomotion patterns. This is thought to be the result of evolutionary processes that have optimized this network. On top of this, fine motor control is learned during the lifetime of the animal thanks to the plastic connections of the cerebellum that provide descending corrective inputs. This research aims at understanding and identifying the possible advantages of using learning during an evolution-inspired optimization for finding the best locomotion patterns in a robotic locomotion task. Accordingly, we propose a comparative study between two bio-inspired control architectures for quadruped legged robots where learning takes place either during the evolutionary search or only after that. The evolutionary process is carried out in a simulated environment, on a quadruped legged robot. To verify the possibility of overcoming the reality gap, the performance of both systems has been analyzed by changing the robot dynamics and its interaction with the external environment. Results show better performance metrics for the robotic agent whose locomotion method has been discovered by applying the adaptive module during the evolutionary exploration for the locomotion trajectories. Even when the motion dynamics and the interaction with the environment is altered, the locomotion patterns found on the learning robotic system are more stable, both in the joint and in the task space. From the outside, locomotion appears to be performed spontaneously and effortlessly by both animals and humans, but a complex neural system controls it. Movements are mainly controlled by the Central Nervous System (CNS) which generates commands at a cortical and spinal level and integrate those commands based on different sensory feedback. All the muscular activation and coordination processes can be unexpectedly produced without the need for conscious control  for each leg , but onlcentral indicates that there is no need for peripheral sensory feedback to generate the rhythms. From a control point of view, the CPG has also very interesting properties such as distributed control and modulation of locomotion by simple high-level commands  which controls the shape of a skewed sine wave modulating the protraction-retraction of the hip joint of the robot as shown in the systems of equations 1-4.In our system, this biological neural function is mathematically modeled as a network of coupled non-linear oscillators and they are represented as the gray box in The CPG module is the main block involved in the evolutionary procedure (Sect. 2.3) and it is implemented in open-loop in the control architecture.phenotype for the evolutionary process, which means that they are the observable characteristics resulting from the interaction of the genotype of the robot with the environment. Equally, the CPG parameters . In fact, to not steer the evolution toward a limited area in the space of the possible genetic outcomes, the generalizability and unbiasedness of the starting values of the genotype are fundamental.The initial parameters and the boundaries of the oscillators , employerameters  represenThe selected parameters are listed in i − th robotic hip, with ϕ2π = ϕi(mod 2π):Here below, the equations of the unit oscillators model for the r is the radius of the hip oscillator, μ is its hip target amplitude, ω its frequency, ϕ its phase, o its offset and θ its output angular excursion in radians. γ is a positive gain defining the speed of convergence of the radius to the target amplitudes μ. d is the virtual duty factor since the actual duty factor depending on the robot dynamics and on parameters of the gait. The four hips of the robot are also phase-coupled to synchronize them, to achieve different gaits. More in details, the coupling between hip oscillators i and j is obtained by adding the term wijsin(ϕj − ϕi − ψij) in Equation (2), where ψij is the desired phase difference between the oscillators controlling hips i and j and wij is a positive gain. Eventually, ϕL (Equation 4) is a filter applied on the phase ϕ and cos(ϕL) is used to compute the output angle θ of the hip oscillator.The described CPG oscillator acts as a trajectory planner in the control architecture since coordinates the robotic motion, defining the locomotion characteristics. In quadrupeds, the neural signal which descends from the spinal cord along the motoneurons regulates the contraction of the peripheral muscle fibers  which encodes the internal model of a body part to more precisely perform more precise motion control Ito, .In Mossy fibers (MF): they transfer the sensory inputs to the cerebellum : they expand the sensory information from the mossy fiber to abstract the inverse model of the body movement corresponding to the specific body part : they transmit the information from the granular cells to the Purkinje cells. This layer is shared among all the cerebellar microcircuits and represents where the information is shared among the four cerebellar modules (light blue in the Purkinje cells (PC): they modulate the input from the granular cells, which is carrying information about the actual state of the robot. The modulation is performed thanks to teaching information coming from the inferior olives through the climbing fiber : it gathers and integrates inputs from the information elaborated by the Purkinje and Granular cells. It generates the final cerebellar output . pk is also transferred between two sub-modules of the learning machine  (in yellow in Cerebellar Nuclei (DCN)  algorithm. The LWPR is an algorithm for incremental nonlinear function approximation in high-dimensional spaces with redundant and irrelevant input dimensions  and the actual positions (Qlegy for y in ULMs) of all the 4 controlled joints. Then, the algorithm performs an optimal function approximation and divides the sensorimotor input space into a set of receptive fields (RFs), which represent the neurons of the cerebellar GCs layer. The RFs geometry is described by Equation (5), which describes a Gaussian weighting kernel. For each multidimensional input data point xi, a RF activation pk is computed, based on its distance to the center of the Gaussian kernel Ck.Each LWPR model is fed with the sensory inputs which are the reference position for the specific leg hip joint  has been proved to lead to a sparse code of the input data xi and this facilitates the persistence of remaining sites of plasticity for the incremental learning process, as in the biological cerebellar circuit , where wk is the weight vector of the RF and ϵk is the bias.The output of the ulqpr (Equation 7), that is the contribution from the cortical layer of the cerebellar microcircuit model. This contribution is modeled as a weighted linear combination of the kernels outputs yk(xi).Moreover, the LWPR acts as a radial basis function filter which elaborates the sensory information and returns it as pk (Equation 3) also represents the contribution which is transmitted through the parallel fiber to the Purkinje Layer (PL). The parallel fibers gather all the information from the different GCs kernels. This information is multiplied by a set of weight rk and thus, we obtain upl, the Purkinje Cell Layer (PL) output (Equation 6).rk is computed. β is a small learning rate  and ufb(xi) is the motor command from the feedback part of the controller, used as teaching signal.The learning rule used for updating the weights in the Purkinje Cells Layer is explained in Equation (7), where the update gain δuim (Equation 8).Taking inspiration from the biological cerebellar micro-structure, the final output of the entire cerebellar circuit is the neural command coming from the Deep Cerebellar Nucleus (DCN) or Deep Nuclear Cell which represents the inverse model corrective torque ut to be sent to the robot is computed as in the Equation (8).At each simulation iteration, the total effort command In evolutionary robotics, the desired robotic behaviors emerge automatically through evolution due to the optimization and interactions between the robot and its surrounding environment. As a specification for the evolutionary procedure, a fitness function, which measures the ability of a robotic individual to perform the desired task, is defined based on this optimization procedure, the algorithm identifies the optimal robotic configuration   should be also optimized by the evolutionary exploration. Their initial conditions and the boundaries for the CPG parameters are the same, as in The PID gains are part of the evolved parameters in the adapt-after-evo and adapt-in-evo. In the adapt-after-evo configuration, the PID gains are part of the evolutionary process and their boundaries are defined according to empirical evaluations on the stability of the system, while in the adapt-in-evo system configuration when the cerebellar circuit is plugged in the system, they are fixed  are related to the creation of new Receptive Fields, while the last two (w_gen and add_threshold) directly influence the local regression algorithm. All the hyper-parameters are the same for the 4 Unit Learning Machines and they are described as follows:init_D = 0.7, it represents the initial distance metric which is assigned to each new created Receptive Fields (RFs);w_gen = 0.6, it is critical for the creation of new RFs. If no local model shows an activation greater than this value, a new RF is generated;init_α = 500, it is the initialization value for the learning rate in the gradient descent algorithm which minimizes the error in the different regressions of the input space;add_threshold = 0.95, it operates as a threshold value to stand when a new regression direction should be added to the algorithm. If the ratio between the mean squared error of the current regression dimension and the same mean squared error, at the previous time iteration, is lower than this value, thus, a new regression direction can be exploited in the robot modeling process.The first two hyper-parameters considered  are set as shown in In ntroller , whose hadapt-in-evo, was plug in the adapt-after-evo. Thus, both systems are now adaptive thank to the contribution of the cerebellar control module and it is possible to test and compare the benefits of control adaptability during or after the optimization of the planning of the locomotion trajectories. The two resultant control architectures are then representative for:adapt-in-evo)control adaptability during the evolutionary optimization of the CPG locomotion patterns (adapt-after-evo)control adaptability after the evolutionary optimization of the CPG locomotion patterns .In general, to provide a fair comparison between the two systems, the distance is computed only after the cerebellar algorithm has converged, as in the initial phase, where learning occurs, we can observe some instability. After this initial phase, that lasts for around 20 s, we can notice no significant improvements in the position error on the joint trajectories, which could indicate that most of the learning has been done. This can also be observed by looking at the number of receptive fields created by the LWPR algorithm, that is not increasing anymore. Therefore, to avoid having the learning phase affecting the computation of the distance covered by the robot, a time window of 20 s is considered, from 30 to 50 s, during which the distance covered by the robot is recorded and compared between the two different cases  the error in following the reference positions is almost half compared to the other case adapt-after-evo (a). Their Root Mean Square Error (RMSE) are, respectively, 0.035 radians and 0.056 radians.ut, becomes predominant compared to the feedback controller contribution (PID). Furthermore, the PL contribution, whose teaching signal is the feedback controller ufb, follows the trend of the output of the PID controller, which decreases along with the simulation, meaning that the final motor commands to the robot are mostly relying on the uim output.Then, in On the third line, adapt-in-evo case  rather than employing it afterwards (adapt-after-evo), we investigated on the effects of altering the experimental conditions with respect to the simulation circumstances where the locomotion patterns have been found. These experiments are also useful for testing the system in more realistic scenarios, which goes toward overcoming the reality gap. The adaptation to the changes in the experimental scenario is possible since the weights of the LWPR are never locked to certain values, but they are always updating based on the experimental circumstances.After discussing the results concerning the advantages of using control adaptability during the optimization of the locomotion trajectories  fed in the encoder of the motors and randomly selected from a uniform distribution in the range of [0–10] % of the motor signal;damping coefficient, randomly taken from a uniform distribution in the range of [0.08–0.25] First, to verify the abstraction potential of the previous results, a population of 15 slightly different Fable robots is generated. After checking the consistency of the simulation in a certain range of variation of the robotic model dynamic parameters, we decided to generate 15 robots with the following features:Thus, the resulting 15 Fable robots have different dynamic characteristics and noisy signals injected in their motors' encoder. These modifications model the variability in the robotic population.x−direction of the world reference frame. The default value of the simulator for this parameter is 1, meaning maximum static friction between robot and ground and we decided to affect the experiments by giving three different levels: 0.3, 0.5, 0.95 of static friction coefficient to the interaction robot-ground. Lower coefficients imply greater disturbances to the system. To have consistent results, the previously generated robotic individuals are simulated ten times for 1 min in each of the 3 different friction conditions explained above.Subsequently, other experimental constraints have been modified. They represent the variability in the interaction robot-environment. Thus, to modulate this aspect of the simulation, the static friction coefficient is altered in the adapt-after-evo and adapt-in-evo, 10 times per individual.adapt-in-evo, and adapt-after-evo), i.e., factor controller over three different ground-robot interactions , i.e., factor ground on the explanatory variable walked distance (D), expressed in meters. Data are mean ± standard deviation. Analysis of the studentized residuals showed that there was normality, as assessed by the Shapiro-Wilk test of normality  = 7.003, p = 0.03)  = 4.288, p = 0.04, ϵ = 0.706  m and (0.80 ± 0.27) m, respectively, p-value < 0.0005  m and (1.34 ± 0.55) m, respectively, p-value < 0.05  m and (1.28 ± 0.31) m, respectively, p-value < 0.0005 (3 stars in data for high-friction Simple main effects were run for the factor ntroller . D of adadapt-after-evo (on the left column) and adapt-in-evo (on the right one) in the three different friction conditions with the terrain  and (e) (adapt-in-evo) highlight that during the first minute of simulation, the position errors at the joint level are decreasing, even if the experimental conditions (robotic model and robot-ground friction coefficient) are changed compared to the initial simulation constraints, where the locomotion patterns have been found. The error for the system adapt-in-evo (right column) is always smaller than for the other system adapt-after-evo (left column), observing both its mean and standard deviation across the four legs. In the three different robot-ground interactions  show the same trend as in adapt-after-evo and adapt-in-evo without disturbances (adapt-after-evo and adapt-in-evo demonstrate a less stable behavior if compared to the same subplots (c) and (g) in The most significant differences between the behavior of two compared systems urbances  and thaturbances –9, can burbances , 9C,G, tadapt-after-evo and adapt-in-evo is the plot showing the dynamics of the Center of Mass (CoM) of the robot . Here, the stability region in the no disturbances case is represented in red, while the behavior for the affected systems is in the remaining color gradient timeline (adapt-in-evo agent (on the right) is confined in a region of the phase space which is very close to region covered by the dynamics of the same system without disturbances (in red in subplots d and h). Instead, the dynamics of the center of mass of the adapt-after-evo experiments .For these experiments, the same representative individual we choose for designing the previous plots  are slightly different from the behavior of the no-perturbed system (in red). This happens both at joint level in adapt-in-evo system feels the change in the interaction with the environment, but it can return to a state of the system which is closer to the initial one whose response is highlighted in red. The temporary divergence of the behavior of the system is visible around second 50 either in adapt-after-evo and in the adapt-in-evo case. A significant divergence in the locomotion stability of the system is visible just in the dynamics of the CoM of the adapt-after-evo system in In conclusion, in the For the first time, taking inspiration from nature, the proposed research uses robotics to suggest the advantages and benefits of employing adaptive controllers in conjunction with optimization strategies, such as evolutionary algorithms. For this purpose, a new bio-inspired approach to control robotic locomotion is presented. The control design is based on neurophysiological evidences concerning a simplified model of the neural control in the locomotion of quadruped animals. In the proposed control architecture, the trajectory planner is a CPG-inspired system of equations and the motion controller is composed of a PID and a bio-inspired algorithm, whose weights are changing on-line with the simulation time. This latter part of the architecture models the adaptive role of the Cerebellar-inspired circuit in the locomotion of vertebrates which encodes information about the inverse dynamic model of the quadruped.The main contribution of the paper is investigating the advantages of using a learning control module during the optimization of the locomotion patterns for a quadruped robot rather than employ it when the optimal locomotion patterns have already been found .In conclusion, further investigations can be done by testing the architecture on the real Fable robot since the conducted experiments aimed at proving the suitability of employing the same controller in real scenarios. In fact, the results show that both control strategies, The datasets generated for this study are available on request to the corresponding author.The bio-inspired control architecture was primarily developed by EM and ST. The use of the evolution-based approach was mainly handled by EM, GU, AV, and JD. EM, LV, UA, and MC worked on the implementation of the experiment. EM, AS, and EF statistically analyzed and interpreted the data. EM, LV, ST, EF, and CL wrote and reviewed the manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Understanding how to harness the continuous interplay between these co-existing, bi-directional, and coordination tendencies is key to shaping system behaviors in sports training. Training programs are traditionally dominated by designs, which shape the self-organizing tendencies of players and teams at a global-to-local scale by coaches imposing a tactical/strategical plan with associated tactical principles of play. Nevertheless, recent research suggests that performers also need to be provided with opportunities to explore self-organizing tendencies that emerge at the local-to-global scale in training. This directional tendency in synergy formation can be facilitated by players being given opportunities to actively explore different adaptive and innovative performance solutions, coherent with principles of play circumscribed in an overarching game model. Developing methods  to exploit the continuous interplay between these co-existing tendencies within sports teams may promote more effective and efficient athlete skill training programs, in addition to enhancing performance.Research has revealed how inherent self-organizing tendencies in athletes and sports teams can be exploited to facilitate emergence of dynamical patterns in synergy formation in sports teams. Here, we discuss how game models, and associated tactical principles of play, may be implemented to constrain co-existing  In past decades, there has been a concerted attempt to conceptualize sports teams as complex adaptive systems (CAS) . Such syOn the one hand, system order emerges from the self-organizing interactions of individual components without ordering from an external agent  . On the Essentially, circular causality  can be differentiated from linear causality due to the causal relationships established between the whole system and its constituents. While models involving linear causality follow a single, linear direction , non-linear causality depicts a circular causation between the whole system and its parts, involving global and local  self-organizing tendencies. Thus, linear causality in systems is characterized by processes of unidirectional flow of causation between both micro and macro levels, where higher levels  are the unique result of the interactions between the constituent parts of the system . Howeverglobal-to-local tendencies in formation of synergetic relations between players in training  . This rey others .For example, in football, a midfielder with the ball perceives an opportunity to make a long pass to a winger, who initiates a penetrating run behind the defensive line, through anticipating this affordance for his/her teammate. A third player, the striker, can also time a run into the penalty box to receive an anticipated cross from the winger, because he also perceived the affordances for the two teammates, while, at the same time, providing them with another affordance – receiving a cross to score. This collective movement pattern may, therefore, constitute how a principle of play, penetration, is sustained by the coordination of individual affordances that are shared locally to form a collective affordance for an attacking sub-system of players.Essentially, global-to-local and local-to-global tendencies provide two main mechanisms through which self-organization gives rise to the emergence of a specific identity , expressThe inter-level causality evidenced by both directional processes, in team sports, may function as a heterarchy rather than a hierarchy, meaning that there is not a dominant relation of the whole relative to the parts in the system self-organizing tendencies. Imagine a situation in which a team sport coach  defines a specific tactical principle for implementation in a particular sub-phase of play. Even though this principle acts at a global-to-local scale to constrain the individual dynamics of players at a local-to-global scale, players will always have the ability to use their unique resources  to continuously reshape the proposed principle through development of local-to-global self-organizing tendencies. Hence, there is a reciprocal  interaction between both levels of self-organizing tendencies.Thus, it is important to note that inter-level causal relations flow in both directions  continuously and mutually influencing each other. In team sports, a game model and tactical principles of play may be influential at a global-to-local scale, thus shaping the local interactions of players, which function at a local-to-global scale. For instance, in football (soccer), a coach may have defined as a tactical principle of play for the attacking sub-phase, to circulate the ball through the right- and left-hand sides of the opposing defense, aiming to cross the ball toward a targeted player. This principle  can influence the local-to-global self-organizing tendencies of players by leading them to attract the opponents toward the center of the pitch in order to create and explore possible empty spaces left in both right- and left-hand sides of the opposing team. Successful sports teams can harness these co-existing tendencies to enhance team synergy formation and performance, without solely relying on a game model framework imposed by a coach.Global-to-local effects do not take the same form as local-to-global influences. The former typically entail modifications in order parameters  , 2012. IThis type of self-organizing tendency is paramount in athlete development programs since local-to-global interactions are instrumental in developing the co-adaptive processes see  needed iFor example, the In sport, environmental, individual and task constraints can alter every time an action is performed, and an “intelligent performer” is viewed as a skilled, adaptive individual who can achieve task goals or individualized functional performance solutions in different ways . Adaptivpre-planned and pre-established actions or mechanized movements that players should faithfully rehearse during practice . Such misconceptions may negatively influence the frameworks for athlete development programs for development of expertise and performance.A challenging task for many coaches in team sports is to provide suitable learning environments for developing adaptive, “intelligent performers.” Intelligent performers in sport have cognitions, perceptions, and actions deeply intertwined in all phases of play . They knThus, it is important to emphasize that the principles of play underlying a game model stipulated with a coach for each sub-phase of play should not be rigid and inflexible . Rather,It is important to ensure that athletes cannot only co-adapt to one major constraint in synergy formation: harnessing global-to-local self-organizing tendencies . Rather, athletes need to be facilitated in exploiting available local-to-global synergy forming tendencies by co-adapting to a variety of interacting personal, task and environmental constraints during performance and practice. They can achieve these adaptive skills by exploring a range of performance solutions coherent with principles of play see .A key take-home message of this article is that for a given principle of play, there exists a multitude of performance solutions or coordination patterns (synergies) attainable by performers. A fundamental question that can emerge from this opinion piece is: how can coaches develop through training both individual and collective behaviors of athletes in sports teams, in adhering to specific principles of play in different performance sub-phases?Here, it is worth alluding to the dynamical notion of an ontogenetic landscape  to referphase transitions  furnishes a rich landscape of tendencies in system organization for performers to exploit  performance solutions coherent with the proposed principles of play circumscribed in an overarching game model. This is extremely important because for a given principle of play , there is a multitude of possible tactical solutions  to be done.Understanding the interplay between these tendencies in inherent self-organizing processes may help establish more effective athlete development programs, as well as enhancing team training and performance. Such an approach may be key in achieving the aim of developing the “intelligent performer” in sport, here defined as an athlete capable of adapting to changing performance constraints rapidly and effectively. More research is needed to theoretically and empirically scrutinize the inter-level causality evidenced by existing global-to-local and local-to-global self-organizing tendencies that shape synergy formation in team sports performance.All datasets generated for this study are included in the manuscript/supplementary files.All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Depending on the mobile species involved in the resistive switching process, redox random access memories and conductive bridge random access memories are widely studied with distinct switching mechanisms. Although the two resistance switching types have faithfully proved to be electrochemically linked in metal oxide‐based memristive devices, the corresponding photo‐induced transition has not yet been realized. Here, a photo‐induced transition through the integration of a graphene layer into a titanium oxide‐based memory device is demonstrated. Coupled with Raman mapping and an electron energy loss spectroscopy technique, the photo‐induced interaction at the heterostructure of graphene/titanium oxide are considered to dominate the transition process. Moreover, a negative differential resistance effect is observed by controlling the applied voltage, which can be credited to the saturation of trap centers (oxygen vacancies) and the increase of interfacial barrier at the graphene/titanium oxide heterojunction. Memristive devices, commonly composed of metal–insulator–metal structure, are resistance switching memories whose electrical states can be reversibly modulated by programming operation.x) has received particular prominence since the milestone work carried out by Hewlett‐Packard laboratory in 2008, and oxygen vacancies are widely believed to account for the resistive switching behavior both experimentally and theoretically.x, HfOx, and TaOx memristive devices have been confirmed by Valov and co‐workers, as the migration of cation species cannot be excluded from the switching process.2 memory device;2O5‐based memristive devices.Titanium oxide  behaviors were witnessed in the graphene/titanium oxide devices, which is similar with previous reports including uniaxial strained graphene,x and TiOy) and inserted graphene layer is shown in Figurex and TiOy layer, the formation of Al2O3 layer can also be identified due to the redox reaction between Al electrode and TiOy layer, which have been reported in other work.x, TiOy, and Al2O3 layers were respectively 20, 30, and 6 nm. Although the graphene layer cannot be detected in the cross‐sectional STEM image, the AFM characterization certifies its existence . The graphene layer was prepared through wetting transfer technique and placed between the bottom electrode (ITO) and TiOy. For the purpose of comparison, devices without graphene layer were also fabricated as control samples. The Raman spectrum shown in Figure x has relatively large bandgap and already contains inherent oxygen vacancies, which is beneficial to forming free performances.1/2) and ≈459.0 eV (Ti 2p3/2) were observed in all measured samples, as illustrated in Figure x/TiOy was Ti4+.x/TiOy from that of TiOx and TiOy after annealing treatment indicate that an interaction between the interface of TiOx and TiOy presumably occurred. Moreover, the color of the homojunction film turned from brown to blue after annealing treatment , which further attested the interaction between TiOx and TiOy. As displayed in Figure x, TiOy, and TiOx/TiOy centering at ≈532.2 eV (oxygen deficient) and ≈530.8 eV (Ti4+—O band) jointly evidence the presence of defects in the oxide layers. And the similarity of the O 1s spectra before and after postannealing treatment suggests that the concentration of oxygen vacancy were preserved after annealing treatment. This is different from the previous reports where postannealing treatment would reduce the contents of oxygen vacancy, and we assume that maintained oxygen vacancy was benefit from the existence of double layer.x, TiOy, and TiOx/TiOy before and after annealing treatment. As illustrated in Figure x/TiOy were still maintained, as demonstrated by X‐ray diffraction patterns before and after annealing treatment  and the high‐resolution cross‐sectional STEM images .The graphic structure of the memristive device comprising titanium oxide homojunction  and the devices hold gradually stepwise resistive switching characteristics were clarified as the memristor. Unlike the mostly observed RRAM behavior, our device present the fingerprint of memristor characteristics, where enhanced and suppressed hysteresis loops were aroused by 15 cyclic voltage sweeps from positive and negative directions, respectively, as shown in Figurex or TiOy) as the active layer , which is in stark contrast to the definition of memristor. That is, in the strict sense, the relationship between current and voltage in a memristor is necessarily nonlinear.I–V relationship of either TiOx or TiOy was attributed to the ohmic contact established by the oxide and electrode (graphene), since the amorphous oxide layer is filling with oxygen defects as confirmed above. As a consequence, the small nonlinear characteristic observed in TiOy‐based device in the positive direction  might be stemmed from its slight higher crystallinity than that of TiOx, which is demonstrated in the AFM characterization  and secondary electron cutoffs (ESE) were derived from UPS spectra  were obtained from the Tauc plot of the absorption band position . Correspondingly, the initial state of the energy alignment was determined as illustrated in Figure a Figure c, and thFigure−2). After 30 s of UV light illumination, the resistive switching performance undergo an apparent change where noticeable SET (VSET = 0.58 V) and RESET (VRESET = −0.58 V) behaviors were observed under the same electrical measurement conditions. For the purpose of comparison, the current–voltage (I–V) plots are presented in the same manner of Figure I–V sweeping cycles are given in Figure S10 of the Supporting Information. The well repeatable current–voltage (I–V) curves illustrated in Figure x, HfOx or TiOx participated in the conductive bridge type resistance switching.x.x were excited. To verify our assumption, the same Raman mapping approach as mentioned above was used. The comparison results of Raman mapping shown in Figure 4+) became the majority mobile species inside the oxide layer and the CBRAM‐type behavior started to dominate the resistive switching process.In order to further explore the functionality of our graphene/oxides heterostructure, the feature of light assisted charge transfer in graphene was investigated in this work. 3 peak (≈459 eV) can be detected across the entire region, it shows a gradual decrease trend from point a to d, attributing to the reduction of Ti4+ and corresponding increase of reduced Ti state  reduce the current level by providing interfacial resistance (graphene/oxides); (2) monitor the movement of oxygen vacancies by Raman mapping. Moreover, multilevel conduction states were enabled by applying different amplitude of voltages, and NDR characteristic was observed by increasing the applied voltage higher than a critical value. These findings observed in our work offer fundamentally insight in designing and manipulating of memristive devices based on heterostructured materials.In summary, by integrating of graphene into titanium oxide homojunction‐based memristive device configuration, we demonstrated a photo‐induced transition between two competitive resistive switching modes. Before illuminating with UV light, the migration of oxygen vacancies under electrical field continuously altered the conductivity of the oxide layers, therefore oxygen vacancy filament was dominated. Once covalent bonding between oxygen vacancies and graphene was enabled by light excitation, TiThe authors declare no conflict of interest.SupplementaryClick here for additional data file.


Micromechanically exfoliating graphene on  The possible fields of application range from microelectronics to high sensitivity sensors . The mosHowever, these materials are non trivial to characterize, handle, or process; and many properties are obscured due to the lithographical pre or post processing that is necessary. Normally, lithographical procedures induce contamination and undesired effects on the 2D materials . Other mAs the preparation of high quality graphene on Micro-mechanical exfoliation research on other 2D materials use similar fabrication and identification methods, via manual optical microscopy imaging search. Therefore, there is a large application field that the method presented in this contribution could be useful for.This work will focus on a fully automated and reliable way to not only identify, but also electrically characterize graphene. Additionally validation of the flakes will be performed with Raman spectroscopy.The provided automation sequence will be limited to graphene flakes obtained via micro-mechanical cleavage on a 300 Automatic graphene flake identification is of vital importance in the perspectives of future graphene-based devices. Large scale analysis of substrates and the devices produced cannot be performed manually and a reliable methodology for flake localization has to be developed.Current state of the art image processing techniques for graphene identification ,8 dependOptical microscopy is used to identify 2D materials due to the size of the flakes produced, normally not bigger than tens of micro meters.Other approaches use lithographic marks on the substrate, and afterwards, find these marks to locate different flakes .Further studies  have triManual approaches to flake identification with the help of robotic systems have also been investigated before ; neverthTherefore, an automated procedure for finding flakes, that works on a centimeter scale and which does not have the pitfalls of manually reacquiring background images, could provide a valuable step forward in the implementation of systematic tools to better analyze and develop graphene devices.Automatic graphene characterization approaches are mainly based on, either the electrical properties of the graphene itself, e.g., electrical measurements of the conductance , or on tAdditionally, there have been studies related to a thorough characterization of graphene flakes with several methods, non-contact terahertz  slip-stick driven stages (SmarAct SCL), and the micro four point probe (M4PP) holder stage was assembled from three double linear (XYZ) slip-stick stages (SmarAct SCL). The stages have a range of z-axis, there is a rotation stage (SmarAct SR), which is used as the base for the substrate, the sensor resolution of this stage is 25 Mounted on the sample holder stage Micro four point probes from Capres A/S  were used, the pitch length between the fingers is 5 The micro four point probe stage is on top of a stack of two goniometers (SmarAct SGO), which helped aligning the M4PP to the substrate surface once.These robotic stages were used for both coarse and fine positioning, making them ideal for an automated task that requires a large range in the size of the substrate, but also extreme positional accuracy for the measurement of the electrical properties.The presented method relies on several steps: calibration made via optical microscope images moving the sample robotic stage to find the substrate’s true focus plane and size; a combination of a rasterized movement along the substrate with a hue, saturation, and value (HSV)-based flake identification algorithm to detect graphene flakes on the surface; and finally, an electrical characterization step, performed with the M4PP. All robotic stages, measurement devices, and the vision system are managed by a workstation running Arch Linux operating system. The low level integration of all devices is implemented using a modern C++, which is used from Python to realize the control logic. The computer vision algorithms are realized with the OpenCV library and its data types are converted to Numpy data types by the help of Boost Python.The presented methodology was tested on graphene flakes obtained via mechanical cleavage procedure on This was the first routine performed after the substrate was placed on the sample holder; it was based on images obtained via an optical microscope with a There are two main purposes of the calibration: the first is to detect the edges of the substrate to acquire the region of interest (ROI) that will be used to search for flakes; the second is to determine the focus plane of the sample’s substrate.An estimation of the substrates dimension is necessary for several reasons. First, it is important to define the region that will be investigated in the search for flakes. Second, if the boundaries are known, it is possible to locate places that are as distant from each other as possible, to calculate the focus plane with a representative sampling along the full area of the substrate.The main implementation in this step is to determine the edges of the substrate and to find the focus of the substrate next to this edge, which will be explained in more detail in the next section. This task is performed without any previous knowledge of the focus plane, so it works even when the image is not in focus.The search of the edge was developed as a binary search problem . The algAfter the four sides of the substrate have been found, the ROI to search for graphene flakes is known and the focus plane of the sample can be determined.The calculation of a focus plane for the surface of the substrate is necessary to maintain an accurate focus as the robotic system moves the sample under the microscope’s objective. Generally, the sample can not be placed absolutely in parallel to the camera system and the depth of focus of the high magnification objective is limited to 900 The plane is calculated in the coordinate system provided by the robotic system (SmarAct SCL) on which the sample is placed. For each of the four points next to the edges of the substrate, a precise automatic focus finding procedure is performed and the plane is derived as a linear fit to these points.The focus searching procedure developed here, was based on optical Newton rings, which were visible on the microscopic images when the sample was positioned The sample is brought to a position roughly 200 As it is very time consuming to search the The sample is scanned in the previously defined ROI  line by For each frame an asynchronous flake detection is executed, as described next.The methodology developed, and presented here, is based on the hue, saturation, and value (HSV) color space. This is one of the most common cylindrical coordinate representations of points in a RGB color model .hue is used to distinguish graphite from the substrate and the saturation is directly proportional to the amount of layers, which suits it perfectly to select graphene, single layer graphite. The value, also called brightness, is less important for identification, since it might change due to the not perfectly homogeneous illumination and focus. Hence, selecting a wide HSV range makes the procedure less prone for errors in different light angles and slight off focus positions.HSV color space offers some critical advantages that fit this problem well. It allows us to select a single continues range for each parameter. The The HSV ranges were empirically investigated and are only valid for graphene flakes consisting of up to three layers on a substrate with 300 As a possible flake is recognized, the contour is extracted from the image. An example detected flake is shown on the left image on Additional post processing helps to filter the results by size and eliminates the same flake being detected twice, due to the acquisition of overlapping images. The latter is achieved by checking for intersections between all the results in the global coordinate system and picking a representative candidate, based on the best fit for the M4PP (described in the next section).Once a flake is identified via the HSV range set in the previous section, it is necessary to investigate whether this flake is appropriate for the electrical characterization.It is important to decide if the flake is large enough to fit the M4PP, in order to perform the measurement. The M4PP is modeled as a rectangle that should be fitted inside the detected contour, a polygon. The center image of Fitting a polygon inside a polygon is a non trivial problem to solve, and could be time consuming if implemented naively; e.g., a brute-force try and error approach on every possible pixel position. Another complexity level is added when adjusting the orientation of the M4PP is considered.In the proposed solution is the usage of additional requirements, derived from the electrical characterization, to reduce the search space. Particularly, the M4PP should be placed as far from the borders of the flake as possible, since edge and boundary effects can affect the measurement. This can be directly translated to a reduction of possible positions for the M4PP, to the center of the flake’s polygon.This reduction is archived by a skeletonized version of the contour, created according to . As the Additionally, it could be possible to think of pruning the skeleton by half of the smaller side of the rotated enclosing rectangle of the M4PP’s polygon, to further optimize the procedure. In this study it was not necessary, since the the fitting jobs were run in parallel to the image acquisition, which is the dominant factor regarding the runtime.Finally, an image of the surrounding box of the flake is saved as a reference for the M4PP measurement.I) run through it and measure the voltage drop (V).One of the easiest paths to obtain the electronic properties of a sample, is to attach two wires to the sample, let a current  was used as the sourcing and measuring equipment during all the experiments. This device was selected because of the flexibility that it provides when setting a measuring step, as different channels can be used either in current or voltage mode, so the electrodes act either as sources or monitor channels.Limiting factors on the positioning system produce some geometrical and positional errors of the electrodes that have to be compensated when performing a M4PP measurement.A dual configuration measurement , as showThe practical implementation of the automatic measurement has to take into account the information obtained from the flake detection procedure.The orientation of the flake has to be adjusted to the previously determined position, while taking care to bring the flake back into the microscope’s field of view. The latter is achieved by determining the rough position by a rotation of the position vector around the predefined rotational axis. This position is then corrected by refocusing, and template matching a rotated and masked version of the previously acquired image of the flake.Afterwards, the M4PP is brought into focus with the flake underneath it. The substrate is then slowly moved towards the M4PP, while a continuous measurement in the M4PP is performed. As soon as the resistance drops below a certain threshold, the flake is in contact with the M4PP. Afterwards a dual configuration sweep measurement is conducted on the flake.Typical results for the electrical characterization can be seen in The electrical characterization shows how the flake charge neutrality point is located roughly between 20 and 30 V, which indicates doping on the flake. It is also possible to see the hysteresis of the two different sweep directions of the gate voltage, as the increasing gate sweep is slightly different to the decreasing gate sweep.The conductance value states that the flake most likely is not single layer graphene but few layers of graphene, as the charge neutrality point of single layer graphene should result in a much lower conductance value.As the proposed automation sequence was evaluated, speed was second to accuracy in flake finding. Bottlenecks arouse not from the constantly processing images, but from the wait that had to be implemented due to the vibration of a slip-stick driven stage.On a In conclusion, the automatic identification and electrical characterization procedure will take less than 2 In order to corroborate that the M4PP did not damage the flake during the electrical measurement, Raman spectroscopy of one of the measured flakes was obtained, excitation wavelength: 514 −1 is not much higher that the 2D peak at around 2700 cm−1.It is possible to observe from the spectra in −1, which would indicate that significant defects were introduced on the flake; as this is not the case, it is safe to assume that the procedure did not structurally modifiy the flake under study.Also noteworthy, is the lack of a peak at around 1300 cmA totally automated micro robotic graphene identification and electrical characterization methodology is presented. The procedure improves on the limitations of other robotic, state-of-the-art graphene identification methods, using HSV color space values to detect graphene flakes on a 300 It also selects suitable flakes for later experiments, based on the available area to probe the flake with a micro four point probe. Additionally, it selects the best angle configuration for the electrical characterization, so edge and boundary effects are reduced as much as possible. System-provided results were shown and described in detail.Future work will focus not only on the characterization of the 2D materials, but also on the systematic functionalization or modifications of graphene flakes. This contribution serves as the base for a series of 2D materials-scanning probe microscopy studies. These will be performed with an improved robotic platform when two independent end effectors are added.


When used as additive‐free electrodes for supercapacitors in an ionic liquid electrolyte, this porous yet dense electrode delivers a volumetric capacitance of up to 138 F cm−3, achieving high gravimetric and volumetric energy densities of 101 Wh kg−1 and 77 Wh L−1, respectively. Such a graphene‐assisted densification strategy can be extended to the densification of other carbon or noncarbon particles for energy devices requiring a high volumetric performance.Supercapacitors are increasingly in demand among energy storage devices. Due to their abundant porosity and low cost, activated carbons are the most promising electrode materials and have been commercialized in supercapacitors for many years. However, their low packing density leads to an unsatisfactory volumetric performance, which is a big obstacle for their practical use where a high volumetric energy density is necessary. Inspired by the dense structure of irregular pomegranate grains, a simple yet effective approach to pack activated carbons into a compact graphene network with graphene as the “peels” is reported here. The capillary shrinkage of the graphene network sharply reduces the voids between the activated carbon particles through the microcosmic rearrangement while retaining their inner porosity. As a result, the electrode density increases from 0.41 to 0.76 g cm Ev) not only requires a high gravimetric capacitance (Cg), but also a high electrode density and a wide operation voltage (Ev = ρ × Cg × (∆U)2/2).Supercapacitors, which store energy by the adsorption of electrolyte ions on a porous electrode, have attracted widespread attention in the energy storage field. This is mainly due to their high power density and long life.FigurePomegranates own an ideally densely packed structure of irregular particles Figure1a. In −3 that is much larger than the 0.41 g cm−3 for pristine ACs. Besides, the AC and G work synergistically for a higher capacitance, with the former providing a porous structure for ions storage and avoiding G layer restacking, and the latter constructing a 3D conductive network and modifying the ion transport. When used as an additive‐free supercapacitor electrode in an ionic liquid electrolyte, the graphene‐wrapped ACs show a gravimetric capacitance of 181 F g−1 and a volumetric capacitance of up to 138 F cm−3, resulting in high gravimetric and volumetric energy densities of 101 Wh kg−1 and 77 Wh L−1, respectively.Graphene would be an ideal candidate for such a peel due to its high strength and good conductivity,Figure The gelation process determines if all the particles can be packed in the graphene network. In our previous work, a pure graphene hydrogel was prepared by hydrothermal reduction of graphene oxide (GO) suspension  and then treated by CEID. However, the hydrothermal process was found to be ineffective here when hydrophobic AC particles were intended to be packed into graphene network through gelation . We have therefore developed a chemical‐reduction‐induced assembly method for the G‐assisted gelation of an AC suspension, in which ACs and GO were dispersed in a water/ethanol mixture and gelation occurs at a low temperature  in ambient atmosphere. This unique gelation process is critical for the later capillary shrinkage of the resulting gel. When this low‐temperature reduction was used to prepare a composite gel, the reduced GO (rGO) sheets became less hydrophilic and tend to overlap and form a 3D network−1) in the ethanol/water mixture  is twice that in water , due to the increased affinity of the mixed solvent. After AC was added, the gelation time increased even more  because the AC particles between the graphene sheets increased the layer separation and hindered the gelation. Except for the solvent and packed materials, other factors involving the reduction of GO, like initial oxygen content, temperature, and reductant, and interaction between rGO sheets, such as concentration, are also important to adjust the gelation time . In this work, the GO concentration of 1 mg mL−1 with an L‐AASS reductant concentration of 0.01 m at 80 °C for 16 h were chosen as the optimized condition to prepare the YP80/G composite.Note that not all AC particles could be packed if gelation took so long that ACs will be partially precipitated. That is the reason why the hydrothermal reduction was not efficient here and thus made it difficult to control composition of the composites. Although it was reported that a G hydrogel could form within an hour using the hydrothermal method,Shrinkage occurred in the solution as the number of oxygen‐containing functional groups further decreased, causing stronger π−π interaction between the sheets and a continuous shrinkage of the network. The gel became smaller during the following 5 h shrinkage process but then remained its size because reduction had ended . It was found that the solvent had a great influence on the shrinkage speed . Shrinkage in the pure water was quite fast (50 min) whereas in the mixed solvent it took 5 h. The shrinkage speed is greatly influenced by the interaction between the rGO sheets and the solvent.CEID played a vital role in the dense packing of ACs, since there was still a lot of empty space in the network after shrinkage in the solution, as shown in the scanning electron microscope (SEM) images of freeze‐dried samples Figure c. DuringFigure2 adsorption–desorption isotherms of the samples begin to show more YP80 features as shown in Figure 4 ions.As shown in 2 g−1, with a decrease in electrode density from 0.85 to 0.41 g cm−3. Obviously, a higher proportion of micropore‐rich YP80 produces a higher SSA. However, a certain amount of G is needed to form a well‐connected network to pack and compress AC particles to obtain a dense bulk material . If there is insufficient G, there are breaks in the G network  and this has a detrimental effect on the strength of the compression exerted by the graphene sheets to rearrange the AC particles, leading to a lower density. It is thus important to balance the SSA and density. Figure 2 g−1 and an electrode density of 0.76 g cm−3. The following parts for the discussion of YP80/G are all based on 76.5% YP80/G.Figure Figure−3, which is twice that of YP80 (0.27 g cm−3). The SEM image in Figure The monolithic form was maintained after the cylindrical wet gel experienced CEID at room temperature and annealing at 800 °C for an hour Figure3a. Thesp2‐hybridized carbon atoms, is present, while a stronger D band, attributed to disorder, indicates its long‐range disordered structure, consistent with the XRD results. After compositing the two carbon materials the ID/IG value is slightly higher because the disorder degree of graphene increases due to the insertion of YP80 between sheets. In addition, since it is important to determine whether compressing the electrodes causes the collapse of pores,2 adsorption–desorption isotherms were measured before and after the YP80 powder and YP80/G electrode were compressed , the specific surface areas of the YP80 powder and YP80/G electrode are 2147 and 1435 m2 g−1 after compressing, respectively . And they exhibit almost the same pore size distribution as shown in Figure From the X‐ray diffraction (XRD) patterns in Figure d Figure d. Here, Figure4). For comparison, the YP80 and graphene mixed powder (YP80/G‐P) with 76.5% YP80 was prepared without the above gelation process. As expected, the powder sample shows a low density of 0.51 g cm−3 due to the low density of graphene and YP80. In contrast, with the gelation process, YP80/G shows an obviously increased density of 0.76 g cm−3  than YP80/G. Besides, the thick restacked G layers in YP80/G‐P is hard to be an efficient conductive additive due to their uneven distribution among the particles , which is also unfavorable for the ion transport. Additive‐free electrodes of YP80/G were prepared thanks to the excellent conductive networks formed by the graphene sheets. The specific capacitance of YP80/G, calculated from galvanostatic charge/ discharge curves of cells charged at 4 V , reaches 181 F g−1 at 0.2 A g−1, nearly comparable to YP80 (containing 10% conductive additive) and higher than that of YP80/G‐P, 152 F g−1 , much higher than that of YP80 (37.8%) and YP80/G‐P (35.5%). Nyquist plots of the three samples , YP80/G‐P (75 F cm−3), YP80/G foam (60 F cm−3), and G (75 F cm−3). Such volumetric capacitance advantage is also demonstrated in organic and aqueous electrolytes . The Figure −1.Bulk YP80/G material has an obvious advantage regarding to volumetric capacitances benefitting from excellent gravimetric capacitance and high electrode density among all samples. As shown in Figure −1 , contribute to an increase in maximum volumetric energy density from 42 to 77 Wh L−1 compared to YP80 electrodes. Due to a superior balance of porosity and density, the YP80/G composite shows the highest volumetric energy density and power density among the samples . Besides, the tight contact between graphene sheets and ACs particles enables an excellent conductive network and the high‐efficiency ion transport channels, which are two key factors in helping the compact supercapacitors maintain their rate performance. For further comparison, the YP80/G has a volumetric energy density of 43 Wh L−1 at a power of 7.6 kW L−1, while the YP80 exhibits 15 Wh L−1 at a power of 4.1 kW L−1. And the gravimetric and volumetric values both outperform carbon‐based materials existing in previously reported electrode materials (Figure Since the porous AC forms the majority of the composite structure, its high SSA ensures a high gravimetric performance. At the same time, the more efficient packing of the AC particles produced by the graphene sheets and capillarity forces, maximizes the volumetric performance of the composite. The Ragone plots in Figure −1. This densification strategy has great potential for use with other porous yet low‐density granular carbon materials besides ACs for the production of high volumetric energy storage devices, not limited to supercapacitors. Furthermore, to our best knowledge, as the first densification strategy reported for commercial ACs, this method is simple and easily scalable, promising to accelerate the development of industrial supercapacitors with higher volumetric energy densities.We propose a general densification strategy for packing AC particles in a dense graphene monolith, inspired by the dense structure of pomegranate where irregular grains are compactly packed in the peel. The flexible graphene network package compressed by capillary forces achieve a compact arrangement of the particles and make the AC powder materials into dense bulk materials, while the porous texture of the ACs is well retained. By balancing the porosity and density of the AC/G, the optimized composite increases the electrode density to 1.9 times that of pure AC and achieves a high volumetric energy density of 77 Wh LThe authors declare no conflict of interest.SupplementaryClick here for additional data file.SupplementaryClick here for additional data file.SupplementaryClick here for additional data file.SupplementaryClick here for additional data file.


Through a number of an extensive autopsy, biomarker, and genomics studies, researchers have recently defined a novel type of dementia known as limbic-predominant age-related TDP-43 encephalopathy (LATE). LATE is perhaps best characterized by the presence of hyperphosphorylated TDP-43, which plays multi-functional roles through interactions with DNA and RNA, leading to significant alterations in the transcription and translation of particular genes. As individuals of advanced age represent a rapidly growing demographic group globally, there is a steadily increasing rate of LATE incidence that has to date received insufficient recognition despite its serious implications for public health. TDP-43 is the common pathology of various age-related dementia, therefore, it may be a potential and promising therapeutic target for such diseases. In the present review, we discuss the pathways regulating TDP-43 expression, metabolism, and disease activity in order to better understand the link between TDP-43 proteinopathy and LATE at the genetic, pathological, and clinical levels. TDP-43 is a protein first discovered in 2006 to be present within the ubiquitinated inclusions that are a hallmark of amyotrophic lateral sclerosis (ALS) and in many cases of Tau-negative frontotemporal lobar degeneration  and C-terminal domain  plaque deposition, hyperphosphorylated tau aggregates that form within neurons to generate nerve fiber tangles (NFTs) are hallmarks of AD. Recent work has shown that phosphorylated TDP-43 is also evident within the brains of those with AD  is characterized by mislocalized and phosphorylated TDP-43 that mainly affects limbic structures , and C9ORF72 have been found to be associated with TDP-43 proteinopathy (Pesiridis et al., Previous study has described two types of TDP-43 distribution: limbic and diffuse (Amador-Ortiz et al., TDP-43 pathological findings can also include the presence of abnormal TDP-43 fibrillary inclusions in astrocytes, potentially suggesting that the blood-brain barrier may be compromised in affected individuals (Lin et al., Both hippocampal sclerosis and TDP-43-associated pathology are increasingly recognized to adversely impact cognition (Dutra et al., Increased TDP-43 inclusion levels have been linked to decreased global cognition and faster cognitive decline in a linear manner (Wilson et al., In all, the symptoms associated with TDP-43-linked pathology depend on the nature of the pathology and the brain regions wherein these aggregates manifest. As such, further studies of the stages and mechanistic basis for TDP-43 progression in the context of pathological and clinical progression are warranted.in vitro (Wischik et al., There is increasing recognition of the prevalence and importance of TDP-43-associated neurodegenerative disease in individuals of advanced age. While many of the studies on this topic to date have demonstrated TDP-43 are associated with various diseases, it is unclear for the most part whether this pathology is causative, promote and/or enhance disease, or instead is a consequence of otherwise related pathophysiology. Although the mechanism is not that clear, Methylene blue and dimebon have been found to inhibit aggregation of TDP-43 in cellular models (Arai et al., GP and LZ conceived and designed the project. LZ and YC wrote the manuscript with inputs from other authors. ML and YW helped to revise the manuscript. All authors reviewed and edited the manuscript and approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Idiopathic pulmonary fibrosis (IPF) is a chronic interstitial lung disease characterised by a progressive and irreversible decline in lung function, which is associated with poor long-term survival. The pathogenesis of IPF is incompletely understood. An accumulating body of evidence, obtained over the past three decades, suggests that occupational and environmental exposures may play a role in the development of IPF. This narrative literature review aims to summarise current understanding and the areas of ongoing research into the role of occupational and environmental exposures in the pathogenesis of IPF. Idiopathic pulmonary fibrosis (IPF) is a chronic interstitial lung disease characterised by a progressive and irreversible decline in lung function, which is associated with poor long-term survival . CardinaThe incidence and prevalence of IPF varies between studies, which likely reflects historical differences in diagnostic criteria . IPF canIn a large systematic review of 34 studies evaluating global IPF incidence and mortality, Hutchinson et al.  estimateImportantly, patients with IPF experience significant morbidity and mortality. Based on the most recent analysis of UK data, the average life-expectancy in patients with IPF is around 3 years . CrucialThe poor prognosis seen in patients with IPF necessitates timely diagnosis to identify patients who might benefit from treatment targeted at maintaining lung function. However, diagnosing IPF remains a complex and challenging endeavour, which requires detailed history taking, specialist investigations and MDT input. Often, IPF has an insidious onset and many patients show symptoms as early as 5 years before the diagnosis is made .Historically, IPF has been a challenge to diagnose due to an incomplete understanding of the disease pathogenesis, inconsistent diagnostic criteria and a lack of international agreement . IPF is The most recent international consensus guideline for the diagnosis of IPF has been produced via collaboration between the American Thoracic Society, the European Respiratory Society, the Japanese Respiratory Society and the Latin American Thoracic Association (ATS/ERS/JRS/ALAT) . Key radInterestingly, a recent meta-analysis of 12 studies found very little difference in the proportion of confirmed IPF diagnoses in patients with UIP on HRCT compared to patients with possible UIP on HRCT . This suPrior to making a diagnosis of IPF, it is important to carefully consider alternative diagnoses due to the many clinical and radiological similarities between IPF and other interstitial lung diseases ,20. IndeThe pathogenesis of IPF is incompletely understood. However, accumulating evidence, obtained over the past three decades, suggests that several risk factors are associated with the development of IPF, including advanced age, male gender, genetic factors, smoking and other environmental exposures ,24,25,26There is an argument that many patients labelled as having IPF may instead have a form of interstitial lung disease, which has, in fact, been triggered by an occupational or environmental exposure . This woExamples of occupational and environmental exposures which may contribute to the pathogenesis of IPF include organic dust (livestock/agriculture/farming), metal and mineral dust, wood dust, asbestos and ambient particulate matter ,35,36,37A recent case-control study, conducted in southern Italy, found that two groups of occupations conferred a particularly high risk of developing UIP and that this risk increased with increased lengths of occupational exposure: farmers, veterinarians and gardeners  and metallurgical and steel industry workers  . FurtherData obtained from a multi-centre, case-control study in the US identified several occupations which were associated with IPF, including farming, livestock, hairdressing, raising birds, stone cutting/polishing and occupations which gave exposure to metal dust and vegetable dust/animal dust . In a muOccupational dust exposure has also been shown to impact prognosis in patients with IPF. In a national survey of 1311 patients with a diagnosis of IPF in Korea, Lee et al.  found thIn addition to data previously mentioned ,39, seveIn a recent study of 244 patients with MDT-confirmed diagnoses of IPF in Belgium, De Sadeleer et al.  separateIn a recent, comprehensive literature review, Assad et al.  evaluateSimilar findings supporting the role of metal dust inhalation in the pathogenesis of IPF have been observed in Japanese and Korean cohorts. In a multi-centre case-control study in Japan, Miyake et al.  found thSeveral studies have demonstrated an association between exposure to wood dust and IPF. In an early, UK-based case-control study, Hubbard et al.  evaluateIn a national, multi-centre case-control study based in Sweden, Gustafson et al.  analysedAdditional data supporting the idea that wood dust exposure may play a role in the pathogenesis of IPF has been presented in the form of a case-series from Italy  and, as The role of occupational asbestos exposure in the development of asbestos-related lung diseases, such as pleural plaques, asbestosis and mesothelioma, is well established . HoweverBarber et al.  attemptehttps://clinicaltrials.gov/ct2/show/NCT03211507). Studies like IPF-JES, which help to determine a relationship between asbestos exposure and risk of IPF, are important. This is particularly true given that the rate of asbestos consumption remains high in many developing countries [The IPF Job Exposure Study (IPF-JES) is an ongoing, UK, national, prospective case-control study, which will provide important new information about the association (or lack of association) between occupational asbestos exposure and the development of IPF (ountries .More recently, there has been a focus on the role of ambient particulate matter, including background air pollution, in the pathogenesis of IPF. In the first study of its kind, Conti et al.  investigIn an earlier study, Johannsen et al. , investiWinterbottom et al.  studied Sesé et al.  investigClearly, there is a need to complete a detailed occupational and environmental exposure history in the work-up for the diagnosis of IPF. This can be a difficult endeavour for several reasons. Firstly, patients may simply not recall certain occupational or environmental exposures, or may not have known they were being exposed, particularly in older cohorts before the stricter health and safety requirements came into force. Secondly, it can be difficult to estimate the intensity of an exposure and how this may have varied over time. Calculations, which provide an estimate of lifetime exposure and the cumulative burden of exposure, may provide valuable data but may be practically challenging to perform due to heterogenous levels of exposure in the ‘real world’ . It may As our understanding of the pathogenesis and clinical course of IPF develops, we are likely to see further changes in the case definition and possible subclassifications . Further


A hybrid structure gas sensor of reduced graphene oxide (RGO) decorated graphene (RGO-Gr) is designed for ultra-low concentration ammonia detection. The resistance value of the RGO-Gr hybrid is the indicator of the ammonia concentration and controlled by effective charge transport from RGO to graphene after ammonia molecule adsorption. In this hybrid material, RGO is the adsorbing layer to catch ammonia molecules and graphene is the conductive layer to effectively enhance charge/electron transport. Compared to a RGO gas sensor, the signal-to-noise ratio (SNR) of the RGO-Gr is increased from 22 to 1008. Meanwhile, the response of the RGO-Gr gas sensor is better than that of either a pristine graphene or RGO gas sensor. It is found that the RGO reduction time is related to the content of functional groups that directly reflect on the gas sensing properties of the sensor. The RGO-Gr gas sensor with 10 min reduction time has the best gas sensing properties in this type of sensor. The highest sensitivity is 2.88% towards 0.5 ppm, and the ammonia gas detection limit is calculated to be 36 ppb. Helicobacter pylori infection and halitosis [Ammonia gas is widely present in the surrounding environment. It is a colorless, irritating odor and highly toxic gas, therefore, the detection of ammonia is very important due to the potential threat to people’s health posed by ammonia ,2. Meanwalitosis ,6,7,8,9.alitosis . In ordealitosis ,11,12,13alitosis .2, NiO, and so on [Different materials are under study by different research groups for gas sensor applications. The most widely sensitive materials for gas sensors are the metal oxide materials, including ZnO, SnOnd so on ,15,16. Tnd so on , metal snd so on , binary nd so on , and oth2, ZnO, Cu2O, WO3) to form hybrid nanostructures sensors [2/RGO ternary composite, the ammonia gas sensitivity of which reached 25 ppm [Graphene and its derivatives are considered as promising gas sensor materials in the field of ultra-high sensitivity detection because of their high surface area and high carrier mobility ,21,22,23 sensors ,27,28,29 sensors ,33,34,35d 25 ppm . An ammod 25 ppm . Both ofThe electron transport of graphene is highly sensitive to most gas molecules owing to its high mobility and presence of a large number of surface atoms in its 2D structure ,39,40. TThis paper proposes a new RGO-Gr hybrid structure based on pristine CVD graphene decorated with RGO to combine the advantages of both graphene and RGO, in order to obtain good gas sensitivity and low noise characteristics. The effects of different RGO reduction time on gas sensing properties have also been studied.Graphene was synthesized on copper foil  by a chemical vapor deposition (CVD) method . RGO wasThe fabrication process of the RGO-Gr hybrid gas sensors is shown in In this graphene transfer process, RGO replaced PMMA (polymethyl methacrylate) as the protective layer of graphene, which not only avoided the organic contamination caused by the introduction of PMMA, but also reduced the damage to the graphene by reducing the experimental steps. Six sensors have been made to conduct comparative research. The sensing materials of the first two sensors were pristine CVD graphene and pristine RGO (RGO0), namely Sensor-1 and Sensor-2, respectively. The sensing materials of the latter four sensors were all RGO-Gr hybrids structures with four different reduction times of RGO. The six sensors could be classified into three kinds as listed in The morphology and structure of the graphene were characterized with a scanning electron microscope . The graphene transferred to the IDEs was measured at room temperature with a Raman spectrometer  at 514 nm with an output power of 20 mW. X-ray photoelectron spectroscopy (XPS)  was used to determine the elemental composition and state of the composites.Ammonia gas detection experiments were carried out in a simple homemade testing chamber, while the total volume of chamber, drying tube and filling pipe was measured and calculated as 4.67 L as shown in 2D/IG) of as synthesized graphene is 1.61, which implies that the as prepared graphene is monolayer [onolayer . A SEM i2 (C-C), hydroxyl (C-OH), epoxy (C-O-C) and carboxyl (COOH) functional groups in the as-synthesized RGO-Gr hybrid samples, respectively [XPS measurements have been executed on RGO0, RGO10, RGO20 and RGO40 to study the functional group changes depending on the RGO reduction time. ectively . These XMeanwhile, the total percentage of the three groups  in RGO10 is much more than those in the latter two RGOs (RGO20 and RGO40). The protocol and fitting results of split peak are shown in the SI. When the reduction time reaches 40 min, the reduction of RGO is almost complete. As the reduction time continues to increase, the percentages of each functional group will tend to be nearly stable.3) results in an increment in the electrical resistance ratio  in comparison to Sensor-2 (3.32%) and Sensor-3-1 (4.7%), as shown in the 3. A plausible sensing mechanism was established. CVD graphene is a p-type material with holes as major carriers under ambient conditions originating from adsorbed water or oxygen molecules [3 cycles has demonstrated good consistency and reversibility . According to olecules . The ressibility .Although graphene has the lowest resistance (~92.78 Ω) as seen in It is well known that the gas sensitivity characteristics of sensors are mainly related to the conductivity and adsorption sites of their sensing materials . The resThe recovery characteristics of Sensor-1 are faster than those of the other two sensors, because gas adsorption of graphene is a physisorption phenomenon with a weak binding energy, while the gas adsorption of RGO0 is a chemisorption phenomenon with strong binding energy.noise) value of the initial resistance . The SNR could be obtained after resistance variation is divided by rmsnoise as follows:The SNR could be obtained according to reference . The sig3 variation value than Sensors-3-4 with RGO40, while Sensors-3-2 with a RGO10 on graphene structure exhibits the highest response compared to the other three sensors.To study the influence of the hybrid structure on the response, comparative gas sensing experiments have been conducted on Sensor-3-1, Sensor-3-2, Sensor-3-3 and Sensor-3-4. In order to study the effects of different RGO reduction times on the hybrid structure gas sensors, the comparative response-time profiles of the four sensors are plotted in For one thing, RGO mainly contains three kinds of functional groups , respectively. Graphene modified with each group individually constitutes a particular hybrid structure. This experiment has compared the values of current and sensitivity of three hybrid structures during ammonia molecule adsorption, as described in the SI. The conclusion can be drawn that the hybrid structure of graphene modified with C-OH outputs the highest current and sensitivity compared to the other two hybrid structures. Therefore, this experiment has extracted the ratio of C-OH and C-C groups in the Gaussian fitted peaks of the sensors Sensor-3-1, Sensor-3-2, Sensor-3-3, and Sensor-3-4, as shown in the following From The interaction between ammonia molecules and C-OH causes a change of the resistance of the adsorbent layer, which also affects the resistance of the graphene transport layer via π-π stacking. The coupling efficiency in the hybrid structure is obviously reduced because of the high resistance of RGO0, which can counteract the advantages of a higher C-OH ratio, and attenuate the response of the sensor. Although the C-OH ratio of RGO0 is higher than that of the other three RGOs, the excessive adsorption resistance of RGO0 counteracts the transmission current of the hybrid structure. Therefore, in general, the responses of the other three RGO-Gr hybrid structures are higher than that of the RGO0-Gr hybrid structure, and Sensor-3-2 is the best among the four sensors. Due to the best gas sensing response and the use of graphene as a conductive layer without introducing additional noise, the signal-to-noise ratio of Sensor-3-2 is the highest, up to 1008.3 are 345 s, 325 s, 300 s and 225 s, respectively. This means that the recovery time decreases monotonically as the RGO reduction time increases.The response and recovery times, a significant indicator for gas sensors, are defined as the time to reach 90% of the final highest value after the gas is injected and the time to reach 10% of the highest value after the gas is removed, respectively . The proAs an almost insulator, RGO0 has a particularly poor conductivity. However, with the increase of the reduction time, the functional groups in the RGO gradually decrease, which leads to the increase in the conductivity of RGO and the recovery speed of sensors. Therefore, the recovery time decreases monotonically with RGO reduction time.The exposure of the four RGO-Gr hybrid gas sensors to three continuous cycles of three different concentrations of ammonia is shown in 3. This clearly suggests the strong selectivity of the RGO on graphene hybrid structure towards NH3. The strong selectivity towards NH3 may be due to the strong reducing and donor behaviour of NH3 among the introduced vapours, as this paper shows that NH3 has injected a much higher amount of negative charge carriers into the RGO on graphene hybrids compared to other introduced vapours. Therefore, the negative charge carrier density of the RGO on graphene hybrids is greatly enhanced.As for 3 concentration of Sensor-3-2 at seven ammonia concentrations are illustrated separately in In order to obtain the detection limit of these gas sensors to ammonia, Sensor-3-2 is selected as the reference sensor for the detection limit due to its excellent gas sensing response under all concentrations as compared to the other sensors. The linear responses with increase of NH 1.55 in a. Finall4OH) derived from ammonia molecules and water molecules on graphene, which has stronger N doping ability than ammonia, so the gas sensing response of the graphene-based sensors increases evidently [Because humidity is an important factor affecting the gas sensitivity of graphene sensors, the effect of humidity on the gas sensing properties of the graphene sensors was studied. Here, the response of Sensor-3-2 toward 10 ppm ammonia gas under different humidity conditions was tested, as shown in vidently .2 modified with Pt nanoparticles has a good gas-sensitive response [The performance of our sensor was compared with several sensors recently reported by other groups. response , howeverIn summary, a hybrid structure gas sensor of reduced GO decorated graphene (RGO-Gr) has been designed to take advantages of both the high conductivity of graphene with its perfect lattice structure and RGO with lots of adsorption sites to realize ultra-low concentration ammonia detection. The RGO-Gr hybrid sensor not only exhibits the best gas sensing performance compared to pristine graphene or pristine RGO sensors, but also greatly improves the anti-noise ability with the SNR increasing from 22 to 1008 compared to the RGO gas sensor. In order to further improve the properties of these gas sensors, the influence of RGO reduction time on sensing properties has been investigated. The RGO10-Gr hybrid structure exhibits an excellent response of 2.88% at 0.5 ppm and good selectivity to ammonia gas in contrast to ethanol, formaldehyde and isopropanol. The gas detection limit of the RGO10-Gr hybrid structure is calculated to be 36 ppb. We believe that this work may be valuable for the future research on gas sensors.