The assembled MoS2/graphene catalysts exhibit significantly enhanced electrocatalytic activity and cycling stability towards the splitting of water in acidic solution. Furthermore, a strategic balance of abundant active sites at the edge of the S–Mo–S layers with efficient electron transfer in the MoS2/graphene hybrid catalyst plays a key role in controlling the electrochemical performance of the MoS2 nanosheets. Most importantly, the hybrid MoS2/graphene nanosheet paper shows excellent flexibility and high electrocatalytic performance under the various bending states. This work demonstrates an opportunity for the development of flexible electrocatalysts, which have potential applications in renewable energy conversion and energy storage systems.In this article, an exquisite flexible hybrid MoS 2/graphene free-standing electrocatalyst paper was fabricated by a one-step in situ solvothermal process for hydrogen evolution reaction applications.An improved flexible hybrid MoS One possible route to target is to utilize solar energy directly or indirectly  to split water to produce H2.1 Presently, producing H2 from the electrocatalytic splitting of water using the hydrogen evolution reaction (HER) holds tremendous promise because of the high energy efficiency and the economical and practical advantages.2 An advanced catalyst for the HER should play an important role in reducing the overpotential and consequently increasing the efficiency of this electrochemical process. Although the most effective HER electrocatalysts are Pt or Pt-based materials, their high cost and low abundance substantially restrict their large-scale application. Thus, it remains challenging to develop highly active HER catalysts based on non-noble-metal materials that are more abundant and lower cost.3–8To address the issues of the global energy crisis and environmental contamination, researchers have devoted considerable attention to hydrogen (H2) is a layered transition metal dichalcogenide (TMD), which has triggered broad attention as a typical two-dimensional (2D) semiconductor nanomaterial. The structure of MoS2 is based on a hexagonal crystal, where each Mo atom is six-fold coordinated, hexagonally packed between two three-fold coordinated sulfur atoms. Each S–Mo–S layer is weakly bonded to other S–Mo–S layers by van der Waals forces. Recent works showed MoS2 to be a promising electrocatalyst for the HER.9–15 It has been suggested that the electrocatalyst activation originates from the unsaturated sulfur atoms at the edges of the MoS2 plates, while the basal planes were often thought to be catalytically inert with some exceptions. As a result, nanosized MoS2 with a high degree of exposed edges should be more active for HER electrocatalysis than materials in bulk forms.16–18 Enormous effort has been dedicated to exposing more edge sites to enhance MoS2 HER activity by engineering various morphologies, such as nanoparticles,19 nanoplates,20 nanosheets,21,22 nanoflowers,23 nanoboxes,24,25 nanoporous films26 and heterolayer nanowires.27 The electrical conductivity of catalysts is another crucial factor affecting the electrocatalytic activity because high conductivity ensures fast electron transport during the catalytic process. However, poor electrical conductivity often exists both in dispersed nanostructure MoS2 catalysts and between the S–Mo–S layers of MoS2, restricting the activity.28 Considerable attention has been focused on accelerating electron transfer by coupling MoS2 with conductive substrates, such as nanoporous gold,29,30 3D Ni foam,31 metal oxides,32–34 activated carbon,35 carbon fibers,36,37 and carbon nanotubes (CNTs).38,39 Among these conductive supports, graphene, particularly reduced graphene oxide (RGO), has attracted much attention because of its excellent electron transport properties, chemical stability and solution-phase processing compatibility.40,41 Therefore, graphene supported MoS2 nanostructures appear to be highly active and stable electrocatalysts. However, all of the MoS2/graphene hybrid nanostructure catalysts mentioned above were prepared in powdered form. Thus they had to be dropped onto the surface of a conductive substrate (e.g. a glassy carbon electrode) and then be immobilized by binding Nafion or PTFE (polytetrafluoroethylene) polymer as a working electrode. As a result, the binding polymer might block some catalytically active sites, increase the series resistance, and thus decrease the overall activity of the material. For this reason, binder-free HER catalysts based on graphene with high electrocatalytic performance are highly interesting. On the other hand, an appropriate loading rate of MoS2 onto the graphene nanosheet is one of the significant parameters to study in MoS2/graphene hybrid electrocatalysts to balance abundant active sites at the edge of S–Mo–S layers with efficient electron transfer. However, so far there have been almost no reports on the activity of MoS2/graphene hybrid electrocatalysts sensitive to the MoS2 nanostructure loading rate.Molybdenum disulfide  was added, and the mixture was stirred for 10 min in an ice water bath. Then, 0.5 g of NaNO3 powder was added and stirring continued for 20 min. Next, 3 g of KMnO4 was slowly added to the flask, keeping the reaction temperature below 5 °C for 30 min. Then, the solution was heated in an oil bath at 35 °C and allowed to stir for 0.5 h. Afterward, the flask was removed from the oil bath, and 50 mL of water and 5 mL of 30% H2O2 were added to end the reaction. The obtained dispersion was filtered and washed with 10% HCl aqueous solution followed by ample water to remove metal ions and residual acid, respectively. The resultant GO solution was sonicated for 1 h and centrifuged at 5000 rpm. The concentration of GO was determined by weighing the quantity of GO by vacuum freeze-drying 10 mL of GO solution. The final concentration of GO solution was modulated to 5 mg mL−1.GO was made using a modified Hummers’ method. Briefly, 1 g of graphite powder was put into a 250 mL round-bottom flask. A total of 25 mL of concentrated H2/GO nanosheets were synthesized through a one-step solvothermal reaction according to Dai’s group’s method.42 Briefly, 25 mg of GO was dispersed in 40 mL of dimethylformamide (DMF) solution and stirred for 10 min. Then, different amounts of (NH4)2MoS4  were added to the mixture, followed by sonicating for 10 min to obtain a well dispersed solution. After that, the solution was transferred into a 50 mL Teflon-lined stainless steel autoclave and sealed for reaction at 210 °C for 12 h. After cooling down, the black products were collected by centrifugation at 8000 rpm for 3 min and washed repeatedly with DI water and ethanol 3 times. Finally, the hybrid suspension was vacuum filtered using polyvinylidene difluoride (PVDF) membranes . The wet MoS2/GO composite paper along with the PVDF membrane was fabricated, then washed with ethanol to remove the remaining solvent. After vacuum freeze-drying, the composite paper could be easily peeled of as a freestanding MoS2/GO paper. The hybrid papers with various loading amounts of MoS2 were denoted by weight percentage content as: MoS2/GO-5%, MoS2/GO-10%, MoS2/GO-20%, MoS2/GO-30% and MoS2/GO-40%. To obtain highly conductive graphene sheets and highly crystalline MoS2, the MoS2/GO composite papers were cut into 2 cm × 1 cm pieces, then heat-treated at 700 °C for 2 hours in Ar flow with a heating rate of 6 °C min−1. For comparison, pure RGO paper was also prepared via the above method without the addition of the (NH4)2MoS4 precursor.MoSθ = 10° to 80° using a Bruker Advanced D8 diffractometer under Cu Kα radiation (λ = 0.1542 nm), with a voltage of 40 kV and a current of 40 mA. The phase structures were characterized using Raman spectroscopy (Jobin-Yvon HR800), with the excitation source an argon-ion laser (λ = 515 nm). Transmission electron microscopy (TEM) and high resolution transmission electron microscopy (HRTEM) images were obtained under an acceleration voltage of 200 kV with a FEI Tecnai G20 TEM instrument. The high resolution energy dispersive X-ray spectroscopy (EDS) mapping measurements were carried out with a probe-Cs-corrected HRTEM instrument (FEI Titan G2 60-300) in scanning TEM mode (STEM). The chemical composition and bonding states were investigated by X-ray photoelectron spectroscopy  using an Al Kα source 1486.6 eV anode. All XPS spectra were corrected using the C 1s line at 284.6 eV.The morphology of the as-obtained products was observed using field emission scanning microscopy . X-ray diffraction (XRD) was carried out from 22SO4 at room temperature using an electrochemical workstation . The prepared MoS2/graphene freestanding paper was measured as a working electrode. A commercial 20% Pt–C catalyst was used as a reference sample. An Ag/AgCl electrode with saturated KCl solution and a Pt wire were used as the reference electrode and counter electrode, respectively. The potential values reported in this study are versus the reversible hydrogen electrode (RHE). The HER activities of the samples were evaluated using linear sweep voltammetry at 10 mV s−1. Continuous cyclic voltammetry (CV) was conducted at 50 mV s−1 between 0.1 and −0.5 V to investigate the cycling stability. EIS was carried out from 105 to 0.1 Hz with an amplitude of 10 mV at the open-circuit voltage.All electrochemical experiments were performed in a standard three electrode setup in 0.5 M H2/graphene nanocomposites and prepare the free-standing MoS2/graphene electrode paper using a vacuum filtration method, which was a facile and time-saving method. 2/graphene freestanding paper after peeling off the membrane, with a size of 2 inches. The hybrid film appeared uniform and was a dark gray color. These freestanding papers can be randomly wrapped around a glass rod with no noticeable damage, indicating their remarkably flexible and durable properties . The hybrid MoS2/RGO films show two diffraction peaks relating to RGO and MoS2 (100), respectively. The absence of a (002) low-angle diffraction peak indicates that the atomic arrangement of MoS2 in the hybrid with graphene sheets was different from that in the powder form. Raman spectroscopy, as shown in 2 at 378 and 402 cm−1, corresponding to the E2g1 and A1g modes as shown in the inset, respectively. The lower intensity of E2g1 (in-plane Mo–S phonon mode) compared to that of A1g (out-of plane Mo–S phonon mode) indicates that the MoS2 crystals tended to show the basal-edge-rich feature of the ultrathin plate growth, in good agreement with the absence of a (002) peak in the XRD measurements.We employed a one step solvothermal reaction method to synthesize the MoSinset in . Fig. 1b2/graphene nanosheets were further obtained by TEM. As shown in 2 nanosheets are homogeneously dispersed on the graphene sheets, indicating that the graphene sheets could be used as an efficient substrate for the nucleation and growth of MoS2 nanosheets. On the other hand, the graphene sheets show good ability to prevent the aggregation of MoS2 nanosheets, serving to enlarge the active area of HER. Furthermore, plate-like ultrathin MoS2 can be further verified by HRTEM, where each sheet contains several S–Mo–S layers with an interlayer spacing of 0.62 nm, as shown in 2 nanosheet, in agreement with the results of XRD and Raman characterization. Furthermore, two different aligned  MoS2 nanosheets were found on the GO sheet under higher resolution TEM investigations, as shown in 2 show that a few layers of MoS2 were stacked with hexagonal lattice symmetry. Part of the MoS2 layer is fully vertically aligned, resulting in dominantly exposed molybdenum or sulfur atoms, which is beneficial for higher chemical catalytic activity for the MoS2 sheets. The HRTEM image indicates the vertically aligned MoS2 composed of a several-layer structure and a lattice fringe spacing of 0.62 nm. The chemical composition of the hybrid nanosheet was further determined by an energy dispersive X-ray spectroscopy (EDS) curve and mapping using a probe-Cs-corrected TEM instrument. 2/GO hybrid films. The results show that there were six kinds of element  detected, of which C and O peaks come from GO, Mo and S come from MoS2, and Cu and Si come from the TEM grid and silicon escape peak. 2/graphene nanosheet hybrid structure. The signal for O comes from the incomplete reduction of graphene oxide after the solvothermal reaction. To obtain highly conductive graphene sheets, the freestanding MoS2/graphene paper was further treated by annealing at 700 °C for 2 h in Ar flow.The morphology, microstructure and chemical element information for the ultra thin plate MoS2/graphene catalyst films were carried out to elucidate the chemical bonding structures. A typical XPS survey scanning in the binding energy range from 0 to 1200 eV is shown in 2/graphene hybrid film, Mo, S, O and C elements were detected. Meanwhile, the S 2p region  and intact (ACC) carbon atoms, as shown in 2 carbon  scale" fill="currentColor" stroke="none"><path d="M0 440 l0 -40 320 0 320 0 0 40 0 40 -320 0 -320 0 0 -40z M0 280 l0 -40 320 0 320 0 0 40 0 40 -320 0 -320 0 0 -40z"/></g></svg>C, 284.3 eV) and epoxy/hydroxyls , respectively. The ratio ACO/ACC of annealed MoS2/graphene is about 0.25, which is much lower than that of the GO nanosheet published elsewhere,43 indicating that the GO sheet has been reduced to RGO efficiently by annealing.X-ray photoelectron spectroscopy (XPS) analyses of the annealed MoSp region  exhibits2 play a key role in improving the HER activity.29,44 Therefore, efficient HER activity can be expected for the higher loading rate of MoS2 in the hybrid films, which introduced more edge sites. We investigated the HER activities of our MoS2/graphene hybrid thin films with different loading rates. The electrocatalytic HER performance of freestanding MoS2/graphene paper was measured in 0.5 M H2SO4 solution using linear sweep voltammetry (LSV) in a three-electrode system. The polarization curve was recorded within a cathodic potential window in the range 0.1 to −0.5 V at a 10 mV s−1 scan rate. For comparison, pure RGO film and commercial Pt–C were also assessed. 2/graphene hybrid films loaded with various MoS2 nanosheet percentages from 5 to 40%. All the polarization curves have no IR compensation. As expected, the commercial Pt–C catalyst displayed the highest electrocatalytic activity, with an onset overpotential  of 23.9 mV. In sharp contrast, the pure RGO film without MoS2 nanosheets exhibited the lowest electrocatalytic activity. The onset overpotential gradually decreased from 263.7 mV to 94.2 mV when the MoS2 nanosheet content percentage increased from 5% to 30%. However, in contrast the overpotential increased to 221.6 mV when the MoS2 nanosheet percentage was 40%. Generally, the overpotential value for a HER current density of 10 mA cm−2 is an important reference because significant hydrogen evolution can be observed and the solar-light-coupled HER apparatuses usually operate at 10–20 mA cm−2 under standard conditions .45 According to a previous report, monolayer MoS2 loaded on a 3D nanoporous gold substrate shows a low operating voltage of 226 mV as a typical overpotential for a few-layer MoS2 nanosheet.29 To achieve this current density in this work, the 5 wt%-MoS2/GO catalyst requires an overpotential of 490.2 mV. Moreover, the overpotential shows an obvious decrease from 430.3 mV to 238.5 mV when the weight percentage content of MoS2 nanosheets increases from 10% to 30%, which indicates that the MoS2 nanosheets have significantly accelerated the HER process. However, the overpotential was remarkably increased from the lowest value 238.5 mV to 377.2 mV, as the MoS2 loading rate increases from 30% to 40% in the MoS2/GO hybrid thin films, which indicated that the HER activity was suppressed in the catalyst in spite of the higher loading rate bringing more active sites. We speculate that the surface loading rate of the RGO film was the major reason for the HER activity performance degradation. Part of the MoS2 nanosheets were interstitial in the RGO hybrid thin films when the concentration of the MoS2 precursor was higher than the loading capacity of the GO sheets. This may lead to inefficient electron transfer because of the semiconducting properties of the S–Mo–S layers. For comparison, I–V curve measurements of the thin hybrid film with various loading rates were carried out to elucidate their electrical resistivity  after 50 hours of durability determination, indicating that MoS2/graphene nanosheet catalysts are stable electrocatalysts in acidic solution.The stability of the MoS46 Therefore, it is highly interesting and urgent to investigate flexible electrocatalysts to develop a flexible power source with remarkable electrochemical performance for integration into flexible electronics.47 To develop flexible electrocatalysts, the catalytic performance should remain unchanged under various bending states. The current stability of MoS2/RGO hybrid films under four various bending curvatures was monitored at a fixed voltage of 1 V (2/RGO catalyst was hardly affected by bending stress. Furthermore, the effect of the flexibility of MoS2/RGO on catalytic activity was investigated at four various bending states, as shown in 2SO4 acidic solution at 10 mV s−1. It is clear to see from 2/RGO is an excellent flexible electrocatalyst and its electrocatalytic activity is almost unaffected by the various bending states.Recently, flexible and wearable electronic devices and energy storage and conversion systems, such as flexible displays, supercapacitors and solar cells, have attracted great attention.e of 1 V , and eacin situ solvothermal process for the fabrication of improved flexible hybrid MoS2/graphene free-standing electrocatalyst paper for HER applications. The MoS2/graphene catalysts exhibit significantly enhanced electrocatalytic activity and cycling stability towards the splitting of water in acidic solution, indicating the promising potential of solar energy. Furthermore, a strategic balance of abundant active edge sites with efficient electron transfer in the MoS2/graphene hybrid catalyst plays a key role in controlling the electrochemical performance of the MoS2 nanosheets. Most importantly, because of invariant performance under various bending states, the MoS2/graphene hybrid films provide a fundamental opportunity for the development of flexible electrocatalysts, which have potential technological applications in renewable energy conversion and energy storage systems.In conclusion, we have demonstrated a one-step There are no conflicts to declare.RA-008-C8RA01226A-s001


Brands can be defined as psychological constructs residing in our minds. By analyzing brand associations, we can study the mental constructs around them. In this paper, we study brands as parts of an associative network based on a word association database. We explore the communities–closely-knit groups in the mind–around brand names in this structure using two community detection algorithms in the Hungarian word association database ConnectYourMind. We identify brand names inside the communities of a word association network and explain why these brand names are part of the community. Several detected communities contain brand names from the same product category, and the words in these categories were connected either to brands in the category or to words describing the product category. Based on our findings, we describe the mental position of brand names. We show that brand knowledge, product knowledge and real word knowledge interact with each other. We also show how the meaning of a product category arises and how this meaning is related to brand meaning. Our results suggest that words sharing the same community with brand names can be used in brand communication and brand positioning. Brands can be seen as companies’ most valuable assets. The literature describes brands in different contexts, e.g. distinguishing symbols or entities influencing consumer decisions –4. A recOne possible research direction interprets brands as entities of the mind (cf. e.g. Franzen and Bouwman in ). BrandsBrands occupy a specific position in the mind. This mental position can be determined by the words (and concepts) associated with brands , 7. ThesBoth Aaker  and KellAs Dzyabura and Peres  point ouAs we see, brand meaning can be defined through the associations connected to a brand. For this reason, the associations that brand names evoke in our minds are important brand assets. But equally important are the words which evoke the brands–the cues from which a brand name comes to our minds . Since tWord association databases provide an opportunity for analyzing cues that evoke brand names. The data in these databases are word association data, where a cue word is presented, and respondents have to name the first word that comes to their mind when seeing the cue word. Brand names regularly occur in these databases, and they enable us to gain an insight into the mental representations and connections of brand names.In our paper, we analyze one such database with methods from network science. We examine the connections around brands; we search for communities–closely-knit word groups–of the database with two different detection algorithms.In the first part of the paper, we describe associations. We then show why association databases can be seen as networks and how networks and communities can help us to better understand the structures of word association data. Finally, we show that brands in the mind can be analyzed as parts of the mental network.In the second part of the paper, we analyze the brand-related communities of a Hungarian word association database and show communities in which brand names appear. For finding communities, we use the hub percolation method , 18, theBased on the results and previous research, we define how brand names are stored in the mind’s communities, and we show that these word communities share brand and product characteristics.Words–or more precisely, lexical units–in the mind carry pieces of information; information about a word’s frequency, meaning, morphology, phonology and syntax is stored together with pragmatic, stylistic, graphemic and encyclopedic knowledge , 22.Words stored in the mind can be researched by collecting and analyzing word association data. In word association experiments, a cue word is presented to the participants, and they must write down or name the first word that comes to their minds. The method has been known since Galton , 24 and Word associations can be collected in several ways; participants can name a single association or multiple associations. Associations can be restricted where some characteristics of the allowed answers are determined (e.g. the word class of the response). In some experimental settings, participants are asked to name all of the associations that come to mind (i.e. continuous associations). The used methods, their variants and early results of word association research are described by Cramer .Word association data is never the data of one individual; data is collected from hundreds or thousands of participants. Thus, the database represents not just the connections of one individual; rather, it represents an aggregate of all the connections we can build between words. Word association research has created several word association databases –30.Since the 2000s, the method has experienced a renaissance; new, large-scale data is available, and methods from other disciplines–above all network science–can be used to analyze and describe the characteristics of the connections in the mind –33. One All in all, word association networks can be used for the description of several cognitive tasks, for example, predicting lexical norms , memory The concept of graphs (or networks) has been studied extensively, first as a branch of discrete mathematics. The usefulness and expressivity of the concept became increasingly apparent as other fields started adopting it into their methodology, including sociology, biology, economics and linguistics . NetworkNetworks also provide a natural way to describe word associations; words are connected when one word–the cue–primes another , 32. TheNetworks in real life share several common characteristics. One of these is community structure; a network’s nodes can be grouped into sets so that the nodes inside the sets are densely connected, while the links between the sets are relatively sparse , 49.In social networks, this phenomenon corresponds to the tendency of people to form groups according to common interests and occupations. Word association networks also have a distinct community structure. It has been shown that the communities have an overlapping character in word association networks , and worCommunity detection, the identification of communities in a network, is a very popular subfield of network science, and a great many algorithms have been proposed to solve this task. The majority of the literature defines communities in undirected, unweighted networks and does not allow overlaps between the identified groups. The most notable approaches include modularity maximization methods , 42, stoThe idea of mental networks has a long history in psychology and linguistics , 63. In Similarly, it has been assumed since the 1990s that brand names are also part of these networks; they are represented by nodes in memory and connected via different types of connections to each other, brand and product characteristics and other words . Brands Early research only formulated assumptions on how the network structures around brands are organized–empirical data was missing . ResearcThere are several possible methods to analyze and create these brand-related networks. One of the earliest empirical analyses of networked structures around brands is from Henderson, Iacobucci and Bobby , 70, whoThe most often used method is creating Brand Association Maps , 69, 71,Assuming that brand names are stored like lexical units, phonological and graphemic information and meaning are also stored with brand names. However, the meaning of a brand name arises differently than a word’s meaning. A brand itself can be seen as ‘a cluster of meanings’ [10: 6], where a brand is ‘the consumer perception and interpretation of a cluster of associated attributes, benefits and values’ [10: 6].Based on existing research , 74, we product categorycountry-of-originlogocolorssloganexperiences with the brandexperiences of others with the brand  Are brand names parts of communities in the mental lexicon?RQ2) Are overlapping or non-overlapping methods better for brand community detection?RQ3) What is the advantage of analyzing communities around brands instead of associations connected to brands?Based on the results, we describe how brand names are represented in the mind and show the implications of the results.Research data used in this paper was collected from the word association database ConnectYourMind, which collected associations online between 2008 and 2014, primarily in Hungarian. During that period, approximately 182.000 associations were collected from 1035 respondents.The data collection was a classic word association experiment online. Participants were shown cue words, and as a response, they had to write down any words or combination of words that came to their mind. In the experiment, no restrictions were given according to the responses; any word or part of a sentence (up to 255 characters) could function as a response to the cue words. Responses could be proper names or inflected forms. The language of the responses was also not restricted; thus, the website also collected data–although only several hundred words–in English, German and Italian.The data was collected in two ways; the first 134 words appeared in a given order to all respondents. These 134 words were the initial cue words of the database . After tParticipants took part in the experiment voluntarily, and they could end the experiment anytime by simply navigating away from the webpage. Details of the data collection are described in more detail in  and exhaWhile the mean number of responses per user was 156, the standard deviation of this value was 155, showing that there was a considerable difference in user patience. This also means that the first cue words received more associations than the rest, and we acknowledge this as a limitation of our work.The database contains 699 brands annotated by an expert. The distribution of brands inside the word associations is highly inhomogeneous, with the top 50 brands receiving 50% of the total number of associations containing brands .. As we The network itself is constructed in the following way: the connections between words represent the cue word–association relationship. In the network, each word represents a node, and each given association (response) is represented as a directed, weighted edge in the form of a cue word → response. For example, to the cue word “apple”, a respondent typed in “pear”; in the database, the word “pear” was stored, and a connection between them was created: apple → pear, with a connection strength of 1. When a second person gave the same answer (again pear to apple), the strength of the connection was increased . In the database–due to the data collection process–the first 134 words collected more associations and enabled a deeper analysis of the associative structure for these words . On the We used community detection to create the groups of associations examined in this paper. We applied several detection algorithms to determine which one fits our specific application best. We have experimented with the following non-overlapping, overlapping and directed algorithms:The hub percolation method  is a cliDirected hub-percolation , the dirThe modularity maximization method of  is basedThe Infomap algorithm  seeks toStochastic blockmodels  are geneThe LEMON algorithm is an overlapping detection algorithm based on local expansion via minimum one norm . The metWe applied all of the above detection algorithms to the Hungarian word association network and measured their performance according to multiple metrics, such as conductance , hub-domWe can see that different methods capture completely different aspects of the community structure of the word association network. Modularity maximization produces low-transitivity communities organized around hubs, with relatively low modularity for the end result. As can be expected from a clique-based method, hub-percolation identifies many small, quasi-clique communities with high transitivity and hub-dominance. The directed version of the algorithm produces the opposite: few large communities. SBM inference could not find a sufficient number of communities. The LEMON algorithm had the advantage that community seed candidates could be provided as inputs of the algorithm. We used expert-annotated brand names for this purpose.However, since our aim in this paper is to examine the association network’s communities from the perspective of brand analysis, we ultimately selected the methods that promised to provide the most meaningful results from this practical perspective. These were undirected hub-percolation, the LEMON algorithm and modularity maximization. The LEMON algorithm was especially useful in our analysis, since we are interested in communities organized around a priori known brand names and we could simply provide a list of brand names to the algorithm as input, which would then grow communities around them. Both the star-like structure of the communities identified by the modularity maximization algorithm, and the high-resolution quasi-clique communities found by undirected hub-percolation were easy to interpret.In the first step of our analysis, we searched for communities in our word association network with the two community-finding methods defined in the previous section. In the identified communities, we searched for brand names manually. We define brand names as any name that can influence consumer decisions. We do not differentiate if the brand name is a registered trademark or not, an umbrella brand name or an individual brand name, or a product or a services brand.The first detection algorithm we used was the hub percolation method , 18. TheAltogether 81 communities contain brand names. Large communities– 70+ words–are heterogeneous , and if more brand names appear in one community, they are partially from different product categories.CIB, OTP, MKB, CIB Bank and Erste appear–all brand names of banks operating in Hungary. The same applies to community nr. 183 (54 words), containing the brands Alcatel, T-mobile, v630i, Nokia, Sony, Blackberry, Motorola, LG and Samsung–mobile phone brands and a service provider from the pre-smartphone era in Hungary (note: data collection took place between 2008 and 2014). Likewise, community nr. 642 (30 words) contains Nokia, iPhone, Apple, Motorola, Samsung, HTC and Sony. It is also interesting that if a community has one brand, it likely has another. However, some communities contain brands from different product categories; community nr. 116 (65 words) contains Gucci, Mercedes, Bugatti and Audi, of which three are car brands and one is a fashion brand. However, all can be considered brands in the luxury segment of the given product category; thus, they share the same segment, although in different product categories.The picture changes when the communities become less than 70 words. In this case, the communities containing a brand name tend to contain more brand names, and these brand names are clearly related to each other. Like in community nr. 111, containing 66 words, the brand names The pictures change again in communities with less than 20 members; communities containing brand names only have a single brand name. The picture from the used community detection method is not clear. Communities having between 20 and 70 members seem to be homogeneous considering brand names. Under a homogenous community, we will understand a community where the brand names are from the same, or closely related, product categories; for example, when only vehicle brand names appear in a community, we consider it as homogenous regarding brand names. Communities above this size are heterogeneous, and below this size, they contain only one brand name as a member.Citroën, Xsara, Honda, Accord, Yamaha, Toyota, Suzuki, Avensis, Peugeot, Mx-5, Subaru, Volkswagen, Alfa Romeo, Fiat, R5, GTO, Polo, Micra, Nissan, Suzuki Hayabusa), community nr. 23 has lifestyle brands, mostly watch brands  and community nr. 280 has 40 members with the brands Gucci, Dior, Armani and Prada.Using another overlapping algorithm (LEMON), all communities are guaranteed to have brand names. The communities found by the algorithm are–according to brand names–homogenous. The brand names in a given community belong to the same product category or closely related product categories; for example, communities nr. 4 and 6 consist of vehicle brands  and prestige (presztízs), and with the expression expensive things (drága dolgok). It is clear that this community is centered around (expensive) brands; hence, brands of different product categories are present.However, heterogeneous communities–brands from different product categories–were also identified, for example, community nr. 62., 81 words, 29 brands: Citroën is part of 24 communities, Ferrari of 18 communities, and Nikon of 31 communities. This means that based on this database, to have a clear picture of the brand Citroën, all words in 24 different communities should be researched, and the connections between the words should be analyzed.It is clearly impossible to verify one-by-one the consistency and all words of all communities; it can be said, however, that from brands’ point of view, most communities seem to be homogenous. The algorithm produces good, overlapping results; thus, due to the overlapping nature of the method, it is possible to see several community structures the brand is part of–on the other hand, the analysis is impeded exactly by the fact that several contexts have to be analyzed and minor differences must be accounted for. For example, the brand name To refine our results, we decided to use the modularity maximization algorithm of Blondel et al.  to see iOf the 692 communities, 32 contained brand names. With this method, the brand names in the communities were from the same category or product group. The communities detected with these methods were more homogeneous than the ones detected by the hub percolation, making it possible to determine the key words and ideas the community is structured around. The key words of the community were identified manually by the authors. We used the automatic detection method–manual analysis in line with Brookes and McEnery .Rolex and Swatch appear; and in the community ‘fashion’ (83 words), we found the fashion brands Gucci, Armani, DG, and Mayo Chix along with A´rka´d (a shopping center), Takko (a textile discount store) and L’Or´eal. In this community, we found the city names Paris and Milan too. In the community ‘reading and news’, brand names of newspapers, magazines and television channels appear (31 from 145), and names of writers  and fictional characters (Bridget Jones) are also present.For example, in the community ‘credit and banking’, the appearing brand names are all banks (11 brand names from 84 words); in the community ‘shopping’, they are supermarkets, discount supermarkets and local chains (15 from 148 words); in the community ‘time’ (54 words), two watch brands, Ferrari, Greenpace, Fradi (a Hungarian soccer team with distinctive green color), Jonathan (an apple name), Snow White and the Smurfs, with their Hungarian name Hupik´ek T¨orpik´ek (‘blue smurfs’). The last two are brand names usable for merchandising and licensing . It contained the name cf. Lind , Simon ).Running the LEMON algorithm, however, we found that 18 communities contain the brand name Ferrari (and 28 communities contain Volkswagen). It is clear that 18 (or 28) meanings of a brand do not exist. For Ferrari, we could name as broad categories 1) the brand context , 2) the product context–and/or 2a) sportscar and/or 2b) luxury car, 3) another product context for luxury products and, perhaps, 4) a lifestyle context together with brand use. Seeing the communities of LEMON, however, we found that these possible contexts are simultaneously present in each community.The overlapping community detection method provided good results as it managed to identify communities and brands in the communities, and the communities themselves are explainable in the product-brand context. However, the method failed to distinguish between brand and product meaning.We are aware that current results would suggest that better–marketing-relevant–results can be obtained by using only non-overlapping methods for community detection. It must be stressed, however, that the structure of the mental lexicon is overlapping by its nature; therefore, community detection methods must be improved to provide results that can also be used for practical purposes (e.g. showing the exact contexts brands are part of in our minds).Our results show that we must distinguish between brand associations and associations around brands (RQ3).Brand associations are the associations brands trigger. However, as we have seen in the case of communities, associations do not have to be connected to a brand name to be in the same community as the brand name (cf. point 3). This result aligns with secondary and tertiary associations described by Keller . SecondaThe question arises: Why are community detection methods better as simple associations to explain the mental position of brands (RQ3)?To answer this question, we must first consider the above description of secondary and tertiary associations; these are the associations that are not directly connected to the brand. How can we trigger these associations? Clearly, naming the brand as a cue word is not enough as it collects only the brand’s direct associations. We can also come up with some ideas about which words would trigger the brand name itself , but it is unlikely we will test all possible triggers. Here word association data could come into play; they are collected not focused on a given brand but focused on the research of the mental lexicon. Therefore–as we have seen–brand names will be part of these databases, but they are not the focus. As an example, we take two Hungarian mineral water brands, Theodora and Szentkirályi , and FerAs we can see, the direct network is not complex, while the community structures present a more complex structure with secondary and tertiary associations, showing not only the structure in the immediate vicinity of the brand.Brand association data is collected by presenting the brand name as a stimulus, thus picking out an entity (a brand) from the complex networked structure of the mind and analyzing the picked out entity alone, without knowing the surroundings it naturally has in the consumer’s mind. This shortage of brand analysis is known to research; hence, the recommendation to also study secondary and tertiary associations :brand names (Audi)brand/product characteristics brand/product parts and ingredients (wheel)contexts where the brand/product is used results of using the brand/product substitute product (bicycle)The observed words in the identified communities are in line with brand associations in general , 6, 7. OAlthough the starting point of our research was not brand names–we did not initially use brand names as cue words–we identified associations that are in line with brand-focused research. These results show that when describing a brand’s cognitive position, it is advisable to take into account not only the associations a brand is directly connected to but also the complex associative structure around brands.In this regard, the communities contain brand information and product information (see previous point and literature review). We surmise that the associations of brands define the meaning of the brand, while communities around several brands define the meaning of a product category. Thus, communities do not necessarily provide relevantly more information about a brand compared to other marketing research methods; rather, word association data and communities can help to gain a deep insight into product category information and the interacting connection between product categories and brands. The method has the advantage of using one large dataset to answer several product- and brand-related research questions with the same dataset and directly compare the differences and similarities of different brands (thank you for a reviewer to pointing out this advantage).In addition, the gained information may help to plan advertisement campaigns more precisely; words from the community (which are not directly connected to the brand) may help to formulate messages, which are more easily accepted by the target group since the words are already part of the community. However, at the same time, words missing from the communities can also show what words may be used for differentiation from other brands (see Implications).At this point, it is important to emphasize an assumption that arises from previous research but has to be verified empirically in the future.As described in 2.1, lexical units store several different pieces of information in the mind, and the stored information enables an effective organization of the units and helps to connect them , 107. ExSince brand names were present in word association experiments, we assume that brand names are represented as nodes in the mind, connected to other words and brand names  and some language-specific information . Pieces of this multisensory information are always present and active, regardless of the language and linguistic context. For example, driving in a foreign country and seeing the logo of a fast-food brand, all the information about the brand, products, price category and tastes will be activated, even if one does not speak the language of the given country.This assumption–if verified–could indicate that for a global brand, it is important to use brand communication patterns, which are not country- and language-specific, meaning that, for example, texts should not be the focus of an advertisement. The focus should be multisensory, language-independent information, which in the consumer’s mind does not need to be connected to a given language, thus enhancing the storage of brand-related information on the encyclopedic layer. This may result in brands relying on techniques that are accessed more easily in a context where text is less relevant (e.g. in a foreign country).Several well-known global brands seem to use this technique already. It should be interesting to learn whether the global success of these brands was connected to the fact that they–unconsciously to the layers of the lexicon–used the method described above in their campaigns.We must note that, at this point, this is only an assumption based on the analysis of one database.The description of brand names as nodes belonging to closely-knit communities in the mind shows that brand communication of a well-known or young brand cannot exist in an empty space–all the other brands and the product category the brand belongs to and the attitudes and expectations of consumers towards the product category shape how the brand is perceived.Product-related communities–and words in these communities–may be used by new brands in a product category. First, new brands (and brand names) have to be connected to the product category in as many ways as possible to ensure that the new brand is immediately perceived as part of the given product category and thereby benefits from all the associations of the product category. This can be done by using words in advertisements, which are part of the community of the product category.At the same time, however, they have to ensure that the community around the brand is different from the communities of all other brands and that it contains not only product-related characteristics but also a distinctive logo and slogan that are specific to the brand. A good example is the Hungarian herbal liqueur Zwack Unicum. It is a widely known Hungarian brand with over 100 years of history. It has a distinctive product feature: its bottle has a unique round shape that characterizes the brand. A previous experiment showed that the shape of the bottle frequently appears as a brand association with words like bottle shape, small round bottle, small spherical bottle, round bottle, round, shaped bottle, round, sphere .Advertisements could profit from a better understanding of communities around brands . An examAs we can see in the community in Why is this important? As seen, the community of the product category mineral water already contains the word healthy. Using this word in an advertisement will not help to differentiate a brand–it emphasizes, however, one important feature of the product. Since the word healthy is already part of the community centered around mineral waters, this strong connection can be used in advertising (cf. Shamsollahi ). On theThe communities around brand names are not necessarily the same as those communicated through advertisements and marketing efforts. The meaning of a brand and its perceived and accepted characteristics always develop through a negotiation process between the brand owner and society. The result of this process is the actual meaning of the brand in a given society, which can be different from the meanings the brand owner intended to engrave into the brand , 110.The communities around brands and product categories can contribute to finding these negotiated meanings of brands. Neglecting these meanings can result in brand disasters. In this regard, the communities identified by our analysis can be used to see whether the brand is positioned in the mind of the consumer the way the brand owner intended. Therefore, it is important to analyze not only associations connected to a brand but also the complex associative structures and communities around the brand and the whole product category.Current results are obtained from a word association database collected for linguistic research. Although some demographic variables were collected, no information was asked from the respondents about their brand or product preferences. Therefore, whether the found community structures are general or user-group specific cannot be analyzed.It must be assumed, however, according to Franzen and Bouwman  (cf. 1111), thatIn this paper, we analyzed communities in a word association database and searched for brand names inside the communities.We started with hub percolation, and results showed that the communities we detected identified communities around brands, with the communities being partly homogenous. The LEMON algorithm provided good results since it takes initially known members of communities as an input. We are interested in communities organized around previously known brand names, and we could simply provide a list of brand names to the algorithm as input, which would then grow communities around them. The communities provided by the modularity maximization algorithm were homogeneous and focused on individual brand names from the same product category, enabling us to better explore the associative structures around product categories.Our results showed that brand names are present in word association databases, and we found that the algorithms were able to find homogeneous communities in these databases. We also showed that communities contain brand names from the same product category since brands of a given product category share common characteristics.Based on the results presented here and our previous works, we described how brand names are stored in the mind; they are multisensory entities strongly connected to the product category. We also showed that overlapping and non-overlapping algorithms both provide valuable insights, but currently, non-overlapping methods provide results that are easier to interpret. We also showed that communities in the mind play several roles; communities around brands define the meaning of the brand, while communities around several brands of the same product category define the meaning of a product category.Future research has to describe a brands’ position in these communities more precisely. This can happen by analyzing larger association databases or through target-specific large-scale data collection in several languages. In further research, the characteristics of the test participants must be taken into account, for example, their gender, age or consumer habits. A better understanding of communities in the mind contributes to the understanding of brands as psychological constructs, enabling the use of more effective brand management practices in the future.


Cancer is one of the deadliest diseases worldwide, and there is a critical need for diagnostic platforms for applications in early cancer detection. The diagnosis of cancer can be made by identifying abnormal cell characteristics such as functional changes, a number of vital proteins in the body, abnormal genetic mutations and structural changes, and so on. Identifying biomarker candidates such as DNA, RNA, mRNA, aptamers, metabolomic biomolecules, enzymes, and proteins is one of the most important challenges. In order to eliminate such challenges, emerging biomarkers can be identified by designing a suitable biosensor. One of the most powerful technologies in development is biosensor technology based on nanostructures. Recently, graphene and its derivatives have been used for diverse diagnostic and therapeutic approaches. Graphene-based biosensors have exhibited significant performance with excellent sensitivity, selectivity, stability, and a wide detection range. In this review, the principle of technology, advances, and challenges in graphene-based biosensors such as field-effect transistors (FET), fluorescence sensors, SPR biosensors, and electrochemical biosensors to detect different cancer cells is systematically discussed. Additionally, we provide an outlook on the properties, applications, and challenges of graphene and its derivatives, such as Graphene Oxide (GO), Reduced Graphene Oxide (RGO), and Graphene Quantum Dots (GQDs), in early cancer detection by nanobiosensors. Cancer is the leading cause of mortality worldwide ,2. CanceBiosensor technology is a growing field to satisfy the need for sensitive and rapid detection problems . In addi2 hybridized, and the σ C-C bond is the strongest bond in materials, the pi (π) bond is responsible for the electron conduction of graphene [In electrochemical biosensors, a redox reaction takes place between the bioreceptor and the target molecule . The reagraphene . With thgraphene . Graphengraphene . GO is agraphene . GO is ographene . RGO hasgraphene . GQDs hagraphene . Much of2, sp3) [2 hybridization is a fine and zero bandgap semiconductor, but the sp3 hybridization diamond is a hard insulator. Graphene is highly thermally conductive, chemically stable, and flexible [2g−1) [The physical properties of carbon materials are related to their hybridization stats  ,39,40,41flexible . One of flexible . The chaflexible . Especiae [2g−1) ,33 and ie [2g−1) . It can e [2g−1) . The sene [2g−1) .2 carbon network and shows many defects, and functional groups make its insulators [3 carbons are converted to sp2 carbons. The electrical, thermal, and mechanical properties of RGO and graphene are similar [2 functional groups make RGO crafty for biological applications [Other unique properties of graphene, including its optical, magnetic, and high elasticity, make it a suitable monolayer structure for preparing several graphene-based nanocomposites. Graphene has one of the highest tensile strengths of all materials and a high Young’s modulus, which is the relation between stress and strain, that gives graphene its great mechanical properties ,48. Usinsulators . GO is nsulators . RGO is  similar . RGO exhications . RGO hasications ,58,59. Gications . GQDs exications . These Gications . GQDs haications .Usual sensing methods are expensive, require high-accuracy equipment and costly reagents, and the majority of reactions are not quantifiable in real-time. Graphene-based sensors are now being used as another method for the identification of cancer-related biomolecules. Graphene and its derivatives must be produced at a low cost to fabricate biosensors for early cancer detection successfully. The synthesis of graphene can be applied in two main ways: top-down (destruction) and bottom-up (construction) methods . Most ofThe top-down approaches such as arc discharge , oxidati2SO4. The solution was put in the ice bath and stirred for a few minutes on the stirrer. Then, 3 g of KMnO4 was added to the mentioned solution to turn the color of the solution green. After 5 min, 50 mL of distilled water was added dropwise to the obtained solution, and after 10 min, 100 mL of distilled water was added. Finally, 35 mL of H2O2 was appended to the solution and stirred for 24 h to synthesize GO well. As always, process safety and environmental cost need to be considered during process scale-up. Recently, the Hummer’s method has been used for the synthesis of GO because it is a fast and safe process [As shown in  process .4), acetylene (C2H2), ethylene (C2H4), and hexane (C6H14), to grow graphene sheets on metallic catalysts such as Cu and Ni [2, the deposition of GO is formed on the metal plate and at the end reduced to graphene. Graphene deposition scratches from metal plates are separated by HCL. In another method called epitaxial growth, utilizing carbon solubility in various substrates is a method used to produce graphene layers. The process begins by giving the substrate intense heat, and it allows the carbon to dissolve. This heat depends on the properties of the substrate. The carbon source can be added to the substrate using hydrocarbon molecules. The whole sample is then slowly cooled, reducing the solubility of carbon in growth, resulting in carbon atoms separating from the mass of matter and forming graphene islands on its surface. Different metals and compounds such as Ruthenium (Ru), nickel (Ni), and silicon carbide (SiC) can be used in this process [−1 of ethanol, 2 mg·min−1 was produced [The bottom-up approaches include chemical vapor deposition (CVD) , Substrau and Ni ,79. The u and Ni ,81. Methu and Ni ,83. Alte process ,85. Grap process . SiC was process ,88. In s process . In this process . From 16produced . Howeverproduced .Biosensors are diagnostic tools defined as having a biorecognition element for analyte specificity and a transducer for a measurable signal. There are various measurement methods for measuring biomarkers, and electrochemical measurement methods can be divided into the following categories:1. Amperometry, the most widely used technique for detecting cancer cells and cancer biomarkers, continuously measures the current resulting from the oxidation or reduction of an electrical species in a biochemical reaction.2. Chronoamperometry, in which a square wave potential is applied to the working electrode and a steady-state current is measured as a function of time. Potentiometers determine the difference in electrical potential between two electrodes when the cell current is zero. Potentiometric measurements are made through the Nernst equation, the relationship between concentration and potential. In fact, in this method, the measurement of potential is performed as a function of time in response to a constant current or a square wave.3. Voltammetry is an electro-analytical method that measures current through analytical potential change information. Due to the different methods of measuring potential change, there are also different voltammetric methods, such as cyclic voltammetry (CV), reciprocating voltammetry, linear sweep voltammetry (LSV), differential pulse voltammetry (DPV), square wave voltammetry (SWV), and adsorptive stripping voltammetry (AdSV). Among the various voltammetric methods, SWV and DPV are often used due to their high sensitivity .Many biorecognition elements have been used, ranging from naturally occurring to synthetic constructs . NaturalThe limit of detection (LOD) is commonly used as evidence of the quality of a biosensor. The limit of detection is expressed in units of concentration and, following the IUPAC definition, indicates the smallest solute concentration that a given analytical system is able to distinguish with reasonable reliability of a sample without an analyte. The biorecognition element describes both the selectivity and the sensitivity of diagnostic devices. Between the nanomaterials worked for biosensor fabrication, graphene and its derivatives have been displaying the most promise, since they present an enhanced signal response in a variety of sensing usages ,81. In aOver the last few decades, attempts have been made to utilize semiconductor field-effect transistors (FETs) in biological and chemical sensors due to their well-characterized behavior and ease of use of portable devices. In addition, the electrical measurement of bimolecular interactions is particularly desirable due to its adequacy for low-cost mobile sensors that could be used in the area by non-technical individuals . FET-basAn FET-based biosensor includes three electrodes—the source, the drain, and the gate—such that the section between the drain and the source functions as a biological detection component that interacts with the target analyte/biomolecules and senses their presence, concentration, and electrical activity. The biosensor then converts the biological information directly into a detectable signal . SubsequDue to the linear dispersion relation of the charge carriers in graphene, they have a very small effective mass and excellent carrier mobilities . FurtherAs a conductive channel substrate, many findings have been obtained through research on the contact resistance between graphene and metal to enhance the efficiency of the field-effect transistor. In addition, the need for high carrier mobility and luxurious functional groups has prompted a great deal of effort, such as the heating-assisted spray system for manufacturing mass production of RGO sheets and the interlayered quantum dots for elevating carrier mobility. Biocompatibility, ease of functionalization, and biocatalysis in the oxygen reduction reaction (ORR) are other outstanding features of graphene-based nanomaterials that are useful in the fabrication of GFETs for ultrasensitive and low-noise cancer detection ,119,120.v). Modified Cv FET with electrically activated antibodies showed good stability [It is important to maintain the stability of the antibody-modified surface during FET measurements. However, unlike other measurement methods, FET measurements use an electrical application on the gate area that can damage the surface. Electrical application may repel antibodies that are immobilized on the surface. Thus, preserving antibody-modified FET properties during measurements improves the reliability of FET antigen detection. Hidshima et al. evaluated three types of FET-modified antibodies to measure stability. The change in FET properties was determined three times in a row by calculating the change in the amount of threshold voltage change . In this study, anti-CEA was immobilized in a single-layer graphene channel using a PYR-NHS linker. The anti-CEA-modified GFET could achieve a limit of detection (LOD) of CEA less than 100 pg·mLproteins . In otheeins [−1 .In 2013, Kim et al. applied the R-GO FFET for label-free ultrasensitive diagnosis of a prostate cancer biomarker of α1-antichymotrypsin (PSA-ACT). When PSA-ACT complexes were placed on the R-GO channel substrate using monoclonal PSA, it caused a linear reaction of the gate voltage , caused by the protein–protein interactions on the R-GO FET sensor. They reported this detection of protein–protein interactions up to the femtomolar level with a dynamic range over six orders in the Vg, using minutes of the alteration as a sensitivity parameter. Finally, the R-GO FET biosensor successfully demonstrated high specificity in the diagnosis of SA-ACT of the femtomolar level .−9–1.0 U/mL) demonstrated a wide linear dynamic range with a low detection limit of 5.0 × 10−10 U/mL. During the process, the proposed FET currents are present in the presence of various interfering species commonly found in biological fluids, including CEA (1.0 × 10−3 U/mL), AFP (1.0 × 10−3 U/mL), and CA15-3 (1.0 × 10−3 U/mL). Comparison with CA125 (1.0 × 10−5 U/mL) was measured. According to the values measured in the bar graph of To increase the electrochemical reactivity of the FET biosensor system, Majd et al. designed an ultrasensitive aptasensor system based on multi-walled carbon nanotubes and RGO for label-free detection of ovarian cancer antigen 125 (CA125). The sensor has a simple design based on the noncovalent bonding of MWCNTs/conjugated aptamers on multilayer Graphene Oxide nanosheets and its integration with polymethyl methacrylate (PMMA) as a suitable substrate for designing flexible field-effect transistors. The presence of MWCNTs caused the immobilization of molecules due to the high surface-to-volume ratio. The designed aptasensor for CA125  of the dielectric medium. Interaction between the immobilized receptors on the metal surface and the analyte molecules causes a variation in the sensed medium’s refractive index (dielectric), which leads to an alteration in the propagation constant of the surface plasmons. This phenomenon affects the resonance condition of surface plasmons with specific surface plasmon waves (SPW) that have interactions with the incident p-polarized light of the same propagation constant. The energy of light photon transfers to the surface plasmons at the resonance angle, and the reflectance of the light significantly decreasing, results in forming a sharp dip in the SPR curve (reflectance with respect to the incident angle) ,134. ThePhase detection based on coherence is usually very sensitive to mechanical movements in optical components and ambient noise, which leads to phase measurement errors. The stability of the SPR biosensor in the time axis is also required to monitor the biological response over time.−7 RIU/RU was reported during the 80 min measurement, which is similar to several high-sensitivity-phase SPR systems [Wang et al. evaluated an efficient SPR phase biosensor for targeted drug delivery screening. The sensitivity of the sensor was determined by the SPR signal of a set of concentrations of sodium chloride solution, and its stability was estimated by oscillating the SPR signal of the system during the 80 min measurement process with a sample containing 0.9% sodium chloride. A significant reduction in ideal stability noise of 6 × 10 systems .Graphene-based nanomaterials have a very high surface-to-volume ratio, which enhances the biosensor sensitivity due to the efficient light absorbing on the sensing layer by p–p stacking interaction between biomolecules’ carbon-based ring structure hexagonal units of graphene sheets . Other s2 layered surface plasmon resonance (SPR) biosensor with a graphene-coated SPR biosensor. The numerical values obtained showed that the SPR biosensor coated with graphene-MoS2 compared to a single layer of graphene SPR biosensor is 175% more sensitive [−1 with the LOD of 15 ng·mL−1, which was 15.3 times lower than the conventional SPR biosensor for detection of the same biomarker [Biplob Hossain et al. conferred numerical modeling of a graphene-coated fiber-optic surface plasmon resonance (SPR) biosensor for detection of genetic biomarkers involved in early breast cancer by means of DNA hybridization. The technique used in this sensor is based on the attenuated total reflection (ATR) method to detect the hybridization of deoxyribonucleic acid (DNA) with individual point mutations in the BRCA1 and BRCA2 genes. To detect breast cancer biomarkers (916delTT and 6174delT), SPR and SRF angles can be used, which are insignificant for uncoordinated DNA strands, while being a significant complement to DNA strands. As shown in ensitive . In otheiomarker .Biomolecular imaging and biomarker detection can be performed using fluorescent nanomaterials and labels, a highly sensitive and selective method with adequate spatiotemporal resolution and low cost for application . NumerouFor removing the technical obstacles of conventional organic labels, many fluorescence-emitting nanomaterials such as dye-doped silica nanoparticles (DDSNs), fluorescent gold/silver metal nanoclusters, lanthanide-doped nanomaterials, upconversion nanoparticles (UCNPs), carbon nanomaterials, and fluorescent semiconductor quantum dots (QDs) have been utilized in the fabrication of fluorescent biosensors ,156. GraEven though the complete fluorescence mechanism of carbon nanomaterials is not understood, various types of them such as carbon nanotubes, GO, RGO, GQD, CQD, etc., have been used in fluorescent biosensors ,160,161.In 2015, Zhang et al. showed that with a nanofiber membrane with electrospinning of Graphene Quantum Dots for dual-purpose fluorescent and electrochemical biosensors, the fluorescent intensity is almost uniform, which determines the excellent stability of PVA/GQD nanofiber membranes .Xu et al. designed a fluorescent aptasensor to detect four biomarkers of AFP, HER2, CEA, and VEGF165 simultaneously in the living cell. GO sheets were modified by four fluorophore-labeled aptamers, including FAM, Cy5, and AF405, and after the formation of the aptamer–protein complex, these fluorophores detached from the GO sheet and biomarkers were detectable by four different colors. This procedure proved to be rapid, biocompatible, and highly specific for the diagnosis of related cancers . In otheIn 2021, MA et al. reported that they used DNA nanomaterial attached to the GO surface to detect liver tumors. To analyze the selectivity of target and non-target cells, these cells were stained with and without GO by nanomaterial DNA. In the absence of GO, the comparison between target and non-target cells was insufficient. However, in the presence of GO, diffusion in non-target cells was very weak compared to target cells, indicating the role of GO in the detection of target cells. In in vivo studies, the liver tumor was stained well with the GO-based DNA nanomaterial. The studied system showed that it is highly able to target liver cancer cells. It also works well in the detection of fluorescence imaging of liver tumors and chemotherapy .Electrochemical sensing essentially needs a reference electrode, a counter (auxiliary electrode), and a working electrode, sometimes termed as the sensing or redox electrode. The ref2 and H2 density on the edge-plane defect spots, they bring about a great deal of electron transfer for biospecies [A wide range of molecular identification elements can be used by electrochemical biosensors to detect biomarkers. A number of advantageous electrochemical strategies for detecting cancer biomarkers include techniques such as voltammetry (CV) . The useospecies .Modification of electrodes with graphene-based nanomaterials also causes electrochemical species to distribute on the surface uniformly, and their 2D structure provides a high surface-to-volume ratio, which results in a suitable substrate for detecting adsorbed analytes. The electron transfer rate directly correlated with the exponential distance between the electrode surface and electrochemically active center of the bioreceptor, and graphene-based modification can dramatically reduce this distance to facilitate the electron exchange. This phenomena takes place on the edge of graphene sheets and/or at defects in the basal plane by nanowiring between active electrochemical sites and electrode surfaces to transfer electrons directly that promote sensing applications ,184.−1 [Akbari et al. designed an electrochemical biosensor for prostate cancer biomarker detection using GO/Au nanostructures, and this article showed that the GO/AuNP composite improves the electrical conductivity and biocompatibility . Heydari−1 . A DNA b−1 .−14 to 10−8 M with a correlation coefficient of 0.972 and a detection limit of 3 fM. The results show that the electrochemical biosensors of the graphene-modified glass electrode can be used as an alternative to conventional methods for detecting the early stages of cancer [In 2020, Asadi et al. developed a graphene-modified glassy carbon electrode electrochemical biosensor to detect miRNA-21, a biomarker in early prostate cancer. A molecular linkage agent was used to immobilize DNA on the surface of a graphene-modified glass carbon electrode. Charge transfer resistance (Rct) was measured by electrochemical impedance spectroscopy before and after hybridization. The electrochemical biosensor demonstrated a linear impedimetric response between ΔRct and logarithm of miRNA-21 concentration ranging from 10f cancer .−1 and in the range of +0.8 to −0.4 V. The redox peak currents shown in In 2021, Jozghorbani et al. investigated the diagnosis of carcinoembryonic antigen (CEA) using a label-free electrochemical immunosensor to tether the antibody to the electrode surface. In this study, CV and EIS techniques were used. They first coated the GC electrode surface with RGO. The CV measurements were performed in a 0.1 M solution of PBS (pH = 7.4) containing a 5 mM ferro-ferricyanide probe against the Ag/AgCl reference electrode at a scan speed of 25 mV sIn Raman scattering, photons inelastically lose (Stokes) or gain (anti-Stokes) energy because of molecular vibrational events and represent information about the molecular structure enabling in situ and real-time detection . SurfaceIn bioanalysis, SERS-based biosensors have the following advantages: (1) ability to represent biomolecules’ intrinsic fingerprint molecular information and ultra-high sensitivity down to the single-molecule level; (2) narrow peak bandwidth relative to fluorescence spectroscopy with strong tolerance to photobleaching and photodegradation; (3) many ways to appeal to specific applications for signal enhancement substrates of varying sizes and shapes; (4) a greater depth of laser penetration enables both diagnosis and imaging in vitro and in vivo . One key downside of traditional silver-based SERS substrates is their low physical stability due to oxidation, which has a strong effect on their sensitivity and quality of efficiency. SERS-active metallic nanostructures are typically protected by a stable protective coating or shield constructed from inert materials, such as metal oxides and carbon materials, to overcome this restriction ,196. AmoPractical applications of SERS nanotags require SERS signal stability when nanotags are exposed to the constant light of relatively high-power laser light. These SERS-related problems limit the quantitative analysis of reading signals in sensor applications and prevent the development of a reliable, quantitative SERS-based detection platform. Therefore, the protection and stabilization of SERS nanotags are key factors in the development of reliable diagnostic assays. Smolski et al. showed that following the formation of an encapsulating layer on Raman molecules at the surface of gold nanoparticles improves signal stability in the long-term exposure to intense laser light. Modified molecules have higher SERS intensities and therefore show their stability over time .In addition, several intriguing properties, such as high optical transparency, high carrier mobility, chemical inertness, and biological compatibility, are often combined with graphene and its derivatives . A varie−1 wave number as well as spectral narrowing. The chemical interaction between RGO and tumor cells can be seen from the distortion at the characteristic peaks of graphene. In connection with the chemical mechanism of RGO, the Ag-RGO-Au system has also been used to identify tumor cells without the use of biomarkers. The results showed that free-labeled tumor cells and normal cells, due to interaction with RGO located on the Ag-RGO-Au substrate, could be characterized by Raman changes at specific peaks). The results are shown in Yi et al. developed an SERS-based biosensor to detect tumor cells. In this system, RGO was sandwiched between Ag and Au nanostructures. The Ag-RGO-Au system has a strong chemical mechanism due to the charge transfer between RGO and the target molecules. The SERS sensor based on Ag-RGO-Au was tested for sensitive detection of tumor cells in the presence of human tumor cells, liver cancer cells (BEL-7402), and normal human liver cells (202 liver cells). Tumor cells do not show a characteristic signal in the range of 1000–3200 cm-1 in the Raman spectrum on the Si slide. Placing labeled tumor cells on the Ag-RGO-Au substrate causes significant differences in the Raman spectrum that may indicate tumor cells. In addition to the changes in the width and shape of peaks D and G compared to the peaks for Ag-RGO-Au, the intensity of the peaks also increases. Especially for peak D, there is a clear shift in the 25 cmIn 2018, Liang et al. used a label-free graphene/gold nanopyramid-based SERS biosensor to detect colon cancer p53 −/− cells from p53 +/+ colon cancer cells. The hybrid SERS biosensor is capable of detecting p53 −/− cells from the p53 +/+ cells of three different cell states, live, dead, and burst, with an average 81% sensitivity and 97% specificity. These results show the potential of this SERS graphene hybrid for cancer detection .Electroluminescence or electrogenerated chemiluminescence (ECL) is the shared area of electrochemistry and chemiluminescence (CL). In this mechanism, by applying a potential on the electrode’s surface, the electrochemical energy is being converted to radiative energy ,205,206.Due to the convergence of electrochemical and spectroscopic approaches, ECL has many benefits over chemiluminescence and photoluminescence, such as improved temporal and spatial modulation of light emission, high sensitivity due to lack of light excitation and a wide dynamic range, and fast detection in a low volume of sample. Furthermore, ECL has been successfully used for diagnostic purposes in real samples and complex matrices such as blood, urine, and cell lysates because of its remarkable signal-to-noise ratio (S/N) .Nanoparticles possess outstanding electrochemical, photonic, and magnetic properties that make it possible for ECL sensing designs to be superior transducers ,211. Due2O82− system, which could promote both C•− and SO4•− production in the ECL system to produce more C*+, resulting in an almost 48-fold amplification of the ECL [Although the exact PL process of CQDs and GQDs is still argumentative, π-plasmon and surface or edge defects are related to the broad optical absorption and photoluminescence emissions of carbon-based QDs. The few PL colors and poor quantum yield are still the main shortcomings of CQDs and GQDs in sensing applications. In addition, surface passivation may also impact CQDs’ ECL behaviors. The oxidized CQDs have outstanding ECL behaviors without any surface passivation from a top-down technique. However, the CQDs passivated via a bottom-up route with some capping reagents, displaying erratic ECL signals with elevated background interference . CDs wer the ECL .After reviewing the types of graphene-based biosensors, Recent advances in the development of biosensors were discussed in this study. Recent research on biomarkers over the past decade has shown that biosensors can detect cancer in the early stages with minimal amounts of biological material and various diagnostic methods. Despite the invention of various biosensors, all of these sensors still have advantages and disadvantages. Powerful portable and user-friendly biosensors still have challenges as long as they become a reliable diagnostic method for cancer diagnosis. Challenges and future prospects in this field are rapidly being explored and developed. The issue is, what has led to the rapid progress in this area? Most research refers to nanomaterials or nanoscale measurements, or in many cases both, as the cause of rapid progress. Research and developments of current nanobiosensors are focused on reducing the mentioned problems such as high sensitivity and selectivity, accuracy and precision, and cost reduction. Hence, various methods have been used to minimize the challenges in biosensors. The most important solution proposed is the integration of different mechanical, electrical, chemical, and biological systems using special nanomaterials. If this sequential process occurs, the new diagnostic nanobiosensors could be extended to highly sensitive cells for cancer diagnosis and treatment without serious side effects .Limit of detection (LOD) is a critical performance characteristic that requires careful evaluation during method validation. LOD is generally expressed as the amount of analyte that the analytical method detects in at least 95% of cases. Cancer dosage markers may be very low, so the use of high-LOD methods puts the researcher at risk of negative diagnosis. Very low LODs allow the detection of target biomarkers without enzymatic amplification. Therefore, the development of new techniques with selectivity, sensitivity, and low limit of detection (LOD) is very important in routine diagnosis . For thiIn this regard, Hussein et al. reported in 2019 that the limit of detection (LOD) of the biosensor was about 2 ng/mL, which is 50 or 60 times lower than the detection of antigen by ELISA treated with gold nanoparticles (LOD = 100 ng/mL) and conventional ELISA .Circulation of tumor cells (CTCs), found in the peripheral blood of cancer patients, can be used to detect cancer early. With CTCs, 1 cell per 10 mL can be important, while for pathogens, it can be one microbial species per liter in water quality analysis. Larger volumes pose more challenges for biosensors because these low concentrations require multiples of the sample to be analyzed. Such large volumes can be challenging even for microfluidics, although the separation of CTC by Warkiani et al. solves the higher volume challenge . In this regard, Pudineh et al. examined the heterogeneous phenotypes of circulating tumor cells (CTCs) in whole blood to discover the complex and dynamic features of these potentially vital clinical markers. This task is challenging because these cells are present at parts per billion levels among normal blood cells. To characterize CTCs, they developed a new method based on nanoparticle activation called cytometry. Accordingly, it indexes CTCs based on their superficial expression phenotype. They used a microfluidic chip to process whole blood samples . It is tIn addition, to solve the mass transfer problem using dispersed electrodes for PSA detection, Wu et al. compared the performance of finite dispersible electrodes with a similar sensor prepared in the same way on a normal flat electrode. The dispersible electrodes were used to solve the problem of mass transportation with very low detection sensors. This features the detection limit from the flat sensor to the dispersible electrodes reduced from 7.05 pM to 3.0 fM. Similarly, detection limits of up to 15fM or even lower have been reported for a signal increase .Stability of nanobiosensors in electrodes.High sensitivity of biosensors to changes in environmental and medical conditions.Ability to reuse nanobiosensors.Problems caused by biocompatibility or non-toxicity to the environment.Complex technology for making electrochemical nanobiosensors.The complexity of the interaction method of nanomaterials and biomolecules.Processing, creating special features, and connection problems.Access to high-quality nanomaterials and the nature of these nanoscale compounds on the electrode plate .Ideally, for graphene-based biosensors, we need sensors with high response to the target analyte or molecule that have little or no response interference species. To achieve this goal, more working probes must be used to improve the detection of the target analyte or molecule . DespiteReproducibility, controllability, and scalability are other challenges that need to be overcome. Detecting cancer biomarkers in real samples such as blood plasma, etc., is also a challenge. Because real samples have different proteins, ions, and chemical species, they can be misdiagnosed. One possible way to solve this problem is to disable the sensor surface with an antifouling agent to focus on the target . Thus, aToday, biosensors are used as a sensitive and rapid diagnostic tool in the early detection of cancers. In addition to making early diagnosis and treatment options more accessible to patients and improving their quality of life, biosensors can develop multiple sensors, differential and semi-selective, that can measure multiple markers in a single unit. The biosensors are able to read direct responses as positive and negative responses of biological fluid, including biomarkers, and also lack limitations such as throughput limitations and small sample volumes. Because circulating cancer biomarkers are generally found in small amounts in the background of non-target cells, EVs, and biomolecules, the combination of magnetic nanomaterials and electrochemical biosensors is appropriate to overcome this diagnostic challenge. The use of nanomaterials in the manufacture of biosensors for the detection of biomarkers is significant because these cases increase the range of detection and increase the sensitivity of the biosensor. Graphene and its derivatives  are used in the fabrication of biosensors because of their high surface area, optical properties, and high thermal and electrical conductivity. In this study, the methods of synthesis of graphene and its derivatives were investigated. Oxidative exfoliation-reduction, CVD, and LPE have a high potential for industrial implementation. Finally, in order to synthesize graphene, the environmental hazards, cost, and quality of synthesized graphene should be considered. The use of graphene-based materials for biosensing has achieved great success in a short time. Recent studies in this literature review indicate that, among various detection methods, electrochemical graphene-based cancer biosensors are the best option, due to their high sensitivity in a rapid assay. Graphene-based biosensors show high sensitivity and can be employed for the detection of cancer, and graphene is a super new material for biosensing. The future development direction of graphene-based biosensors should be more portable, reproducible, miniaturized, and high-throughput in detection, and the future development of these 2D graphene nanomaterials will be further developed and tailored for the specificity of receptors.


Twisted van der Waals heterostructures were recently found to possess unique physical properties, such as superconductivity in magic angle bilayer graphene. Owing to the nonhomogeneous stacking, the energy of twisted van der Waals heterostructures are often higher than their AA or AB stacking counterpart, therefore, fabricating such structures remains a great challenge in experiments. On the other hand, one dimensional (1D) coaxial van der Waals structures has less freedom to undergo phase transition, thus offer opportunity for fabricating the 1D cousin of twisted bilayer graphene. In this work, we show by molecular dynamic simulations that graphene nanoribbons can self-assemble onto the surface of carbon nanotubes driven by van der Waals interactions. By modifying the size of the carbon nanotubes or graphene nanoribbons, the resultant configurations can be controlled. Of particular interest is the formation of twisted double walled carbon nanotubes whose chiral angle difference can be tuned, including the 1.1° magic angle. Upon the longitudinal unzipping of such structures, twisted bilayer graphene nanoribbons can be obtained. As the longitudinal unzipping of carbon nanotubes is a mature technique, we expect the strategy proposed in this study to stimulate experimental efforts and promote the fast growing research in twistronics. Carbon nanotubes (CNTs) and graphene are among the most exciting low dimensional materials that possess extraordinary properties, such as ultrahigh strength, unique configuration dependent electronical conductivity and magnetic properties, etc. Such unusual properties make CNTs and graphene promising for applications in a wide range of areas ,6,7,8,9.Recent progress shows that by stacking one layer of graphene on top of another, the formed bilayer graphene exhibits more distinct properties than its monolayer counterpart, e.g., the open up of an electronic band gap . SimilarOn the other hand, although twisted bilayer vdW heterostructures have shown great promise in electronic devices, their mechanical and thermal stability remains a pressing issue ,23,24. TInterestingly, as graphene’s 1D counterpart, CNTs are generally regarded as rolled up of graphene layers and multi-walled CNTs (MWCNTs), and can be composed of co-axial single-walled CNTs (SWCNT) of different chiralities. The different combination of chiralities among inner and outer CNTs can also result in rich electronic states. Furthermore, several groups have shown that MWCNTs can be unzipped or partially unzipped to 1D graphene nanoribbons (GNRs) or GNR-CNT junctions ,34,35,36Motivated by this scenario, in this work we propose a strategy to fabricate 1D vdW heterostructure via self-assembly of GNR onto the surface of SWCNT. The size of the GNR and SWCNT can be used to tune the resultant heterostructure with the desired orientation, i.e., with a particular twist angle between the inner and outer layer. Upon partially unzipping the 1D vdW heterostructure, the resultant GNR bilayer is shown to exhibit a twisted moire pattern. This strategy is expected to provide new alternatives to synthesizing magic angle bilayer graphene, or other twisted phases of 2D materials.The molecular dynamics (MD) simulations are performed using the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) . The gen2 is placed next to the SWCNT. After a few picoseconds (ps), the potential energy of the system displays a notable decreasing trend  SWCNT template as an example, three phases are observed, when the length of the GNR is as large as 70 nm, a longitudinal (L) wrapping is observed, forming multi-layered nanoscrolls. When the length is small, in-between 5 nm and 10 nm for example, helical (H) wrapping is achieved. Meanwhile, for an intermediate sized GNR, the edges of the helical structure attract to each other, and then make contact to form a tubular (T) structure. A widthwise (W) wrapping is also observed for the  SWCNT template, which has a similar size as the  SWCNT, the reason can be attributed to the fact that commensurate stacking between SWCNT and GNR can reduce the total potential energy of the system. Therefore, for chiral SWCNTs, H phases are preferred for small GNR, while for zigzag or armchair SWCNT templates, W phases are preferred. The observed phases are summarized in 2 nanotubes on SWCNT template; this process shares some similarities with ours, but the temperature is much higher in the experiment. More progress has also been recently made towards producing various 1D structures, such as the scrolls or ribbons in CNTs [One interesting feature observed in the T phase is that the size of the GNR does not have to be exactly 2πR, here R = r + 0.34 and r is the radius of the SWCNT, as depicted in  in CNTs ,45,46. S in CNTs ,48,49, bθ is the twist angle of the GNR after wrapping. We have run simulations with GNRs of different length to wrap a template SWCNT, the resultant twist angle is plotted as a function of L in For the chirality of the inner wall, the synthesis of SWCNT with the desired chirality has shown enormous progress, while for the orientation of the outer wall, according to the model presented in θ in Equation (1) equal to 1.1° are used to wrap the SWCNTs. The resultant twisting angles are found to agree well with predictions. Shown in This relationship means that the chirality of the outer wall can be controlled by the size of the GNR. For example, to obtain the magic angle between the inner and outer wall of the DWCNT, one can take a zigzag SWCNT as the template, then, according to the size of the SWCNT and the relationship in Equation (1), a particular sized GNR can be chosen to wrap around the SWCNT to form a 1.1° twist angle. We demonstrate this scenario in our simulations, as a representative example shown in We next proceed to study the stability of the as-formed structures under various temperature conditions, as representative results shown in As mentioned above, several groups have experimentally demonstrated the fabrications of layered GNRs via the unzipping of MWCNTs; a recent work by Chen et al.  showed t2. Future work towards fabricating multilayered vdW structures with precise twist should also be of high interest to this field, as recent experimental work has shown that such structures are also host interesting [In conclusion, we show by molecular dynamic simulations that graphene nanoribbons can be self-assembled to the surface of SWCNTs to form various heterostructures. If the size of the SWCNT and the GNR is well designed, the DWCNTs of particular twisting can be obtained; this offers a plausible platform for unzipping towards twisted bilayer graphene, including magic angle bilayer graphene. Upon unzipping, the twist angle for the resultant bilayer graphene remains almost unchanged. The obtained 1D vdW heterostructures is demonstrated to exhibit good structural stability under temperatures from 5 K to 300 K. This strategy can also be used to fabricate 1D vdW heterostructures composed of other elements, such as BN and MoSeresting . We expe


Foundation Doctors are exposed to a range of specialties within the Foundation Programme, with 20.9% completing a psychiatry rotation. Those who do not have a psychiatry rotation may have little experience other than what was acquired in undergraduate training, despite being expected to care for patients with mental health problems. According to Mind (2017), one in four people will experience a mental health problem each year thus essential that our medical workforce know and understand the basic principles of psychiatry to aid their management of core psychiatric conditions. The aim of this project was to improve mental health literacy among Foundation Doctors by improving their communication, formulation and risk management skills. Another objective was to encourage uptake to Psychiatry and help plug the high number of unfilled Consultant posts.The initial pilot was carried out between January and June 2021 over zoom and the sessions were optional. A survey was completed to find out which topics were most relevant and common themes included MCA/MHA interface, risk management and treatment of various conditions. These themes were incorporated into 90-minute sessions which included interactive case-based discussion in small breakout groups and some didactic teaching. The six session topics were EUPD, Dementia, Depression, Delirium, Substance Misuse and Alcohol Misuse. The sessions were facilitated by clinicians of mixed experience from Foundation Doctors to Consultants. Participant knowledge was tested using pre- and post-session quizzes and a working group reviewed feedback, making relevant changes subsequently.Feedback was majorly positive, and attendees valued the interactivity, breakout rooms, case studies and choice of topics. Suggested areas of improvement were having more time for discussion, technical difficulties, and less psychiatric ‘jargon’, but these tended to be isolated comments. Five out of six sessions showed an improvement in assessment scores afterwards, with an average improvement of 12.6% (average pre-session score of 70% and average post-session score of 82.6%). One session showed a decrease in the post-session quiz scores which on reflection showed that the questions in the assessment covered material not included in the session.The virtual programme was an effective way of improving knowledge and confidence in psychiatry. Whilst the sessions were positively received and showed improvements in post-session scores, there were some limitations which will be addressed and used to develop future training. There is now more mental health woven throughout the new Foundation curriculum and expected that much of this content will be covered during Foundation Training.