We investigate the intercalation process of oxygen in-between a PVD-grown graphene layer and different copper substrates as a methodology for reducing the substrate-layer interaction. This growth method leads to an extended defect-free graphene layer that strongly couples with the substrate. We have found, by means of X-ray photoelectron spectroscopy, that after oxygen exposure at different temperatures, ranging from 280 °C to 550 °C, oxygen intercalates at the interface of graphene grown on Cu foil at an optimal temperature of 500 °C. The low energy electron diffraction technique confirms the adsorption of an atomic oxygen adlayer on top of the Cu surface and below graphene after oxygen exposure at elevated temperature, but no oxidation of the substrate is induced. The emergence of the 2D Raman peak, quenched by the large interaction with the substrate, reveals that the intercalation process induces a structural undoing. As suggested by atomic force microscopy, the oxygen intercalation does not change significantly the surface morphology. Moreover, theoretical simulations provide further insights into the electronic and structural undoing process. This protocol opens the door to an efficient methodology to weaken the graphene-substrate interaction for a more efficient transfer to arbitrary surfaces. Although proven to be an effective process to obtain complete, high-quality monolayer graphene over polycrystalline copper foils [Over last decade, since the discovery of graphene , many meer foils ,4, the cOne of the most promising protocols for the transfer of metal-grown graphene is the so-called electrochemical delamination method , a simpl60 as a precursor is defect-free and therefore it highly interacts with the Cu substrate [Oxygen intercalation has been previously studied on many different metal surfaces, such as Gr/Ru(0001) ,14, Gr/IIn this work, we report on the oxygen intercalation process applied to PVD grown graphene on polycrystalline Cu substrates. The process has been carried out in a UHV environment in order to have a controlled and pure oxygen exposure, that allows fine tuning and optimization of the different parameters  The process has been studied in detail by means of Low Energy Electron Diffraction (LEED), X-Ray Photoelectron Spectroscopy (XPS) and Raman Spectroscopy. In addition, Atomic Force Microscopy (AFM) and Kelvin Probe Force Spectroscopy (KPFS) have been used for topographic and surface potential measurements, respectively.2-10 mbar on different types of Cu substrates, both polycrystalline and single crystals. Commercial 35 μm thick Cu foils were used as polycrystalline substrates. In order to observe differences between the as-grown and the oxygen-intercalated specimens after graphene growth, samples were cut into two pieces (twin samples). One was oxygen-intercalated while the other was used as a reference. Both were characterized by means of the aforementioned techniques. In addition to copper foils, single crystals of Cu(100) and Cu(111) were initially employed to assess the feasibility of the intercalation method with a more controlled system. The surfaces of these substrates were prepared by following a standard cleaning procedure in UHV consisting of successive cycles of argon sputtering and annealing at 800 °C. The substrate temperature was monitored with an optical pyrometer. Commercial fullerene C60 molecules (sigma Aldrich 98% purity) were used as the precursor species for graphene growth on the substrate. The fullerenes were purified for several hours at 500 °C. For graphene growth, the clean Cu substrate was heated to 800 °C and maintained at this temperature. Then, the C60 molecules were sublimated in UHV at a temperature of 450 °C and deposited onto the hot surface during typically 1 h [Graphene samples were grown in a UHV environment with a base pressure of 1 × 103 L , using an oxygen pressure of 1.1×10-5 mbar for the intercalation procedure during 30 min. In order to avoid oxygen desorption from the samples, after completing the intercalation procedure, the oxygen pressure was maintained until the sample was cooled to RT.The oxygen intercalation procedure was carried out in UHV by controlling the incoming oxygen (99.9999% purity) dose with a leak valve. The temperature of intercalation was optimized by testing various samples at different intercalation temperatures of 280 °C, 350 °C, 400 °C, 500 °C and 550 °C, monitored using a K-type thermocouple attached to the heater. In all cases the oxygen dose was set to 20×10in situ with an OMICRON LEED. Subsequently, they were transferred in air to a different UHV chamber, where oxygen intercalation and XPS characterization were carried out. Photoemission experiments were performed with a hemispherical analyzer SPECS Phoiboss 100 MCD-5 with an instrumental resolution of 0.02 eV. Al Kα radiation (1486.7 eV) was employed as X-ray source. The topographic characterization of the samples prior to and after oxygen exposure was performed ex situ with a commercial AFM from Nanotec. The images were acquired in dynamic mode to preserve the integrity of both the samples and the tip. Using the same equipment, Kelvin Probe Force Spectroscopy (KPFS) measurements were performed to determine the surface potential. The tips employed were specially manufactured for KPFS measurements coated with gold nanoparticles (2–3 nm) from Nextip [After oxygen intercalation, an exhaustive characterization was undertaken in order to elucidate the mechanism of the process. The samples were characterized m Nextip . HOPG wam Nextip . Raman mDensity Functional Theory (DFT)-based calculations were performed by using the plane-wave code QUANTUM ESPRESSO , includi3The main objective of this work is to study the oxygen intercalation process in the system formed by PVD graphene on Cu foil samples. With this aim, it was first necessary to determine the best conditions for the intercalation process, which are defined by the temperature range and the minimum oxygen dose. To address this problem, a fast and reliable feedback based on LEED patterns was used. As in polycrystalline samples, grains with different orientation contribute with different spots to the LEED pattern without possibility of precise identification , the use3 L of oxygen, and R45°surface reconstruction, exhibiting two different domains. As it is already known, oxygen exposure on Cu(100) at room temperature and low coverage induces the formation of a c(2×2) superstructure, while at high temperatures the surface reconstructs forming a . Nonetheless, for smaller length scales oxygen differences at the graphene/Cu interface could be found. Additionally, the reproducibility of the KPFS data  single crystal was also monitored by LEED. Thus, after graphene growth the sample was annealed at 400 °C while being dosed with 20×10tructure ,42. Thistructure . The obsPFS data  along thTo verify the surface morphology, the graphene/Cu samples were characterized by AFM prior to and after oxygen intercalation for the Cu (100) and Cu(111) single crystal substrates and the polycrystalline Cu foils. The top panel of Both, the presence of the wrinkles and the ring observed by the LEED patterns indicate that the graphene layer grown on the Cu substrates is polycrystalline (see 3 L) at different temperatures. A temperature increase should enhance oxygen diffusion, and hence promote the intercalation process, although it is important to consider that an excessive temperature could compromise the graphene layer through etching processes. The XPS Cu 2p spectra with exposure temperatures from 280 °C to 550 °C are shown in Once the feasibility of the intercalation method is proved  we focus on the PVD graphene on Cu foil, since this system is the most interesting for future applications. To understand the chemical processes involved in the intercalation approach and for a further optimization of the intercalation conditions, we performed XPS in the regions corresponding to the C 1s, O 1s and Cu 2p emissions. The graphene grown on Cu foil was exposed to the same oxygen dose  in contrast to our procedure based on controlled exposure to pure oxygen. Both differences lead to different doping and un-doping levels of the graphene layer for the as-grown and oxygen intercalated layer, respectively. Interestingly, a previous work in graphene on Cu single crystals reported that the electronic response after oxygen intercalation is different for different orientations of Cu [of 0.068 . A secon3 carbon ,46, whicgligible . As it ireported . This dereported ,47 as wereported ,24,48, ans of Cu . Therefons of Cu ,50. It hns of Cu , and tha3 L) for different temperatures. The curve fit, made by using Voigt profiles, found two components located at 529.8 eV and 530.7 eV. According to the literature, the main peak, at lower BE, corresponds to the contribution of an atomic phase state, i.e., a low-bound state [2O [tate [2O . The absWith the aim of determining the influence of oxygen intercalation on the interaction between graphene and the substrate, Raman spectroscopy was performed ex-situ. 2D) has been correlated with strain in graphene layers. However, its absence in the spectra corresponding to the as-grown sample prevents its comparison with the O-intercalated sample by the use of ω2D vs ωG vectors, often employed to evaluate both strain and charge density in graphene layers [2D appears at an average value of 2718 cm-1, corresponding to a blue-shift of > 28 cm-1 with respect to free-standing graphene [2D, between 15 and 40 cm-1, in CVD-grown graphene on Cu foil [D and ωG values of 1373 and 1608 cm-1, respectively observed in the as-grown sample, also represent a significant blue-shift with respect to free-standing graphene and are coherent with compressive strain. Regarding the G band, in the as-grown sample a broad band profile is observed with ωG ~ 1608 cm-1, albeit with some contribution due to the D’ band. Whereas, in the case of the oxygen intercalated sample, the G-band is much stronger and shifted to a lower average frequency of ωG ~ 1600 cm-1, although more asymmetric with a small shoulder corresponding to the D’ peak at around 1620 cm-1, as previously reported [G and ω2D cannot be explained solely by charge doping and strain [2Dω without affecting the ωG [D, ωG and 2Dω to strain release after oxidation, as mentioned previously, which augment with the growth of the oxide underlayer [2O at 148, 219 and 635 cm-1, and CuO at 273, 327 and 619 cm-1 [2O were found for the 400 °C sample, the oxygen atomic phase state being the main O 1 s contribution. Thus, the main conclusions from the Raman data are that the as-grown PVD graphene is highly strained and strongly interacts with the Cu surface and after oxygen intercalation the graphene layer exhibits characteristics closer to that of free-standing graphene, although retaining a certain level of strain according to the 2Dω values observed, which evidences partial electronic decoupling of the graphene layer from the metal. The data obtained from KPFS measurements -(12 × 4)-MR interfaces; a detailed description of these simulations is found in the supplementary information. The most important result is represented in R45°O@Cu(100)-(12 × 4)-MR interface ) as a function of the energy referred to the Fermi energy (in eV). The PDOS profile for the case of Gr/Cu(100) reproduces a well-formed canonical Dirac cone. Nevertheless, the profile morphology shows a slight asymmetry w.r.t. the center of the Dirac cone from that obtained for free-standing graphene (dashed-black line). The symmetry obtained for the free-standing layer is lost when the substrate is considered, due to several factors: firstly, the non-negligible graphene corrugation arising in this case (around 0.17 Å), with regions where the pristine sp2 hybridization is slightly broken; secondly, the effect introduced in the electronic properties by the cell strain of around a 2% in the graphene; and finally, the non-negligible chemical interaction arising at discrete points in the interface. Importantly, besides the emerging asymmetry, the PDOS profile for the Gr/Cu(100) case exhibits a significant n-doping level developed in the graphene layer due to the charge transferred from the substrate, in excellent agreement with our experimental evidence and literature [In order to endorse the un-doping process that occurs when oxygen intercalates in the graphene/Cu interface, and according to the experimental evidence, we have carried out (DFT)-based calculations of a clean Cu(100) with the missing row reconstruction, named as Cu (100)-12 × 4)-MR surface (MR meaning “missing row” structure), of Gr/Cu(100)-(12 × 4)-MR and of Gr/(terature . From th-MR surfa4In this work, efficient oxygen intercalation between graphene layers grown on Cu by PVD in UHV conditions was reported. The graphene layer preserves its morphology after intercalation. The experimental results evidence the adsorption of an atomic oxygen adlayer on top of the Cu surface with no substrate oxidation. The combination of Raman spectroscopy with the XPS measurements and the KPFS experiments suggest an electronic un-doping of the layer, corroborating the electronic decoupling after intercalation. Theoretical DFT studies show that the system is essentially decoupled before a complete monolayer of oxygen is reached. Therefore, the intercalation method employed was demonstrated efficient to promote electronic decoupling of the graphene layer while retaining the advantages of the growth method. Furthermore, as this decoupling route can be performed immediately after graphene growth, it has the advantage of being an end-to-end process in the same vacuum chamber.https://doi.org/10.1016/j.apsusc.2020.147100.Supplementary data to this article can be found online at Supplementary information


Paper diagnostics are of growing interest due to their low cost and easy accessibility. Conductive inks, necessary for manufacturing the next generation diagnostic devices, currently face challenges such as high cost, high sintering temperatures, or harsh conditions required to remove stabilizers. Here we report an effective, inexpensive, and environmentally friendly approach to graphene ink that is suitable for screen printing onto paper substrates. The ink formulation contains only pristine graphite, water, and non-toxic alkanes formed by an interfacial trapping method in which graphite spontaneously exfoliates to graphene. The result is a viscous graphene stabilized water-in-oil emulsion-based ink. This ink does not require sintering, but drying at 90 °C or brief microwaving can improve the conductivity. The production requires only 40 s of shaking to form the emulsion. The sheet resistance of the ink is approximately 600 Ω/sq at a thickness of less than 6 µm, and the ink can be stabilized by as little as 1 wt% graphite. They have drawn interest because paper is inexpensive, absorbs fluids, can be easily modified, disposed of, and scaled up2. Important applications include diagnosing often neglected tropical diseases, home-based cancer and liver diagnostics, food and water testing, and environmental quality monitoring in developing countries3. As the low cost of these devices is critical to their utility, future paper diagnostics will require inexpensive, non-toxic, and easily processed conductive inks. Currently conductive inks find applications in flexible energy storage materials6, sensors8, antennas9, and electronics11. Metal particle-based conductive inks dominate the market, and although these inks provide high electrical conductivity, they are expensive, often unstable and toxic, and require high sintering temperatures18. To address these challenges there has been a great deal of effort in developing carbon-based, and more specifically graphene-based, inks21. However, difficulties in exfoliating graphite and suspending graphene have made these inks expensive, as evidenced by the cost of commercial graphene-based ink marketed for screen printing exceeding $500 per 10 ml. .Paper diagnostics were first introduced in 2007 as a potential solution to high cost and often inaccessible medical diagnostics8. The second approach uses liquid-phase exfoliation with added stabilizer to keep the graphene suspened23. The GO reduction method addresses the challenge of suspending graphene by adding hydrophilic functional groups to the graphene sheets. This introduces defects but sacrifices the electrical conductivity, which is partially restored by the reduction step25. In addition, the method often used for making GO involves harsh chemical treatment in concentrated sulfuric acid26, and the substrates suitable for GO inks are limited due to the GO reduction process that can require post-print temperatures from 200 to 450 °C28.There are two basic approaches to the use of graphene for conductive inks. The first is to reduce graphene oxide (GO) after printing, either by thermal or chemical treatment. This approach requires harsh oxidation of the graphite, but provides water suspendable material30. This is then followed by further stabilization of the graphene by the addition of chemical binders such as polymethyl methacrylate (PMMA), polyvinyl alcohol (PVA), polyvinyl pyrrolidone (PVP), or ethyl cellulose (EC)32. However, this results in the need for annealing at temperature from 250 to 400 °C to remove the non-conductive binders after printing35.The alternative to the reduction of GO is liquid-phase exfoliation. In this approach graphite is tip sonicated, often at high power for several days in high boiling temperature solvents such as N-methyl-2-pyrrolidone (NMP), dimethylformamide (DMF), or surfactantsAn ideal ink would consist of unoxidized, exfoliated graphene sheets without hard-to-remove suspension agents. Here we report an approach to create such an ink using interfacial trapping. The final inks contain only water, graphene, and alkanes and are prepared by simple shaking for 40 s using a bubble tea shaker. The inks are thus inexpensive and promising for large scale production. Shaking the mixture of water, graphite, and alkane forms viscous water-in-oil emulsions stabilized by exfoliated graphene sheets at the water/oil interface. No toxic chemicals or additional stabilizers are needed. The solvents used can be removed either by drying at 90 °C or by microwaving for 40 s. The inks reported here have a graphite loading of 0.1 g/10 ml, but higher and lower concentrations are also stable.36. Other studies have shown that graphene alone can stabilize emulsions, such as the work of Large et al., where graphite was exfoliated by sonication and used to stabilize water-in-oil emulsions37, and the work of Ogilvie et al. who pre-exfoliated with sonication followed by size selection to produce stabilized emulsions with only few layer graphene38. These emulsions were shown to require low graphene loading. In addition, a recently published study demonstrates that graphene stabilizes water-in-oil emulsions due to the amphiphilic nature of pristine graphene, and that based on DFT calculations dispersive interactions between stacked plates reduces the amphiphilic nature of single flakes, meaning that graphite flakes by themselves cannot stabilize water/oil emulsions39. Other research also supported this idea by successfully forming water-in-hexane emulsions using liquid-phase exfoliated graphene38.Since the exfoliation of the graphite is driven by lowering the interfacial energy of the oil/water interface, once sufficient graphene has been produced to cover the interface, exfoliation does not continue. This results in both graphite and graphene being present at the interface. The presence of graphene, and not just graphite, has been shown by TEM images and Raman spectroscopy10. Values ranged from ~ 2 Ω/sq up to ~ 2 × 105 Ω/sq with a median value of 260 Ω/sq. The variation in these values is at least partially a result of a range of approaches, thicknesses, and conditions used for measuring sheet resistance. All of these inks also contained a stabilizer, often cellulose, that required removal by high temperature annealing, often higher than the autoignition temperature of paper. Our measured sheet resistance of ~ 600 Ω/sq is comfortably in the range of other reported inks, despite the lack of a high energy exfoliation method as was required for all the other reviewed inks. Additionally, the lack of an added stabilizer eliminates the need for high temperature annealing, making our inks an attractive option for paper based electronics.A recent review of graphene based conductive inks compared them in terms of their reported sheet resistance36. The exfoliation is spontaneous and driven by lowering the interfacial energy of oil and water as graphene spreads at the interface40. Shown in Fig. 43.Exfoliating graphite by interfacial trapping relies on graphite not being soluble in either water or oil44. Diluting the ink with additional continuous phase allowed the spheres to be observed under an optical microscope as shown in Fig. Emulsifying the system by shaking or stirring produces new oil/water interfaces. This in turn drives further exfoliation of the graphite as it covers the new interfaces, leading to a water-in-oil emulsion with graphene sheets stabilizing the interface. Figure 48. This would not be expected in the original graphite.Diluting the ink with excess dispersed phase broke the emulsion and resulted in a film of overlapping graphene sheets at the water surface. Transmission electron microscopy (TEM) was used to image this film. Figure Printing with this graphene ink does not involve any modification to the screen-printing stencil. This viscous graphene ink can print into complicated patterns with good resolution as shown in Fig. 36.The rate of emulsion formation in the graphene inks is different than with typical emulsions. Exfoliation of the graphite is not instantaneous, meaning that in some cases there initially is insufficient graphene to stabilize a full emulsion. To study this effect, different graphite loadings were investigated. As shown in Fig. As seen in Fig. At day twelve, all vials were shaken again for 40 s, and vial 1 formed a full emulsion Fig. d. This sIt is important to note, however, that the same emulsion volume could not be achieved without the time gaps between shaking. In Fig. 49. So, graphite exfoliation in the C16 ink is more energetically favorable compared to C7, driving the equilibrium towards exfoliated graphene in the C16 ink system.In addition to the initial loading of graphite, the type of alkane had a significant effect on the rate of emulsion formation. Figure 50. Since the restacking of graphene requires a sheet to move towards either the oil or water phase and away from its minimum energy location, the steeper rise in free energy associated with a sharper interface should inhibit restacking26.Another possible explanation involves the sharpness of the oil/water interface. Moiré patterns shown in Figure 51, meaning that graphene is expected to absorb more strongly C16 than C7. Likewise, computational studies have suggested that as the alkane number increases there is an increase in the desorbing energy between the alkane and graphene52. However, despite the difference in emulsion formation rate between the C7 and C16 inks, both had a similar emulsion droplet size, around 50 μm, and a similar droplet size distribution, as observed by optical microscopy  or profilometryTo address the challenge of surface penetration for our conductivity measurements, the C16 ink was drop cast on a glass slide. The sheet resistance of the ink was found to be approximately 600 Ω/sq. The conductivity was calculated using a thickness value based on the mass of graphene, size of the ink dot on the glass slide, and the density of graphite  or an increasing number of prints (N)That residual C16 was the cause of the high observed resistance was shown by further drying the printed lines. When the lines were dried for a week at 90 °C, the C16 ink resistance was decreased to roughly a third of the original resistance. In addition, the resistance of C16 ink lines was also reduced by roughly 70% after microwaving the printed lines for four cycles of ten second duration, with the results shown in Figure While the lack of conductivity in the C16 inks was a result of low vapor pressure, the reason for the lack of conductivity of the C7 inks at small values of X•N is not as obvious. Visual observation of lines made with C7 suggested that at low print numbers C7 inks were less uniform than C16 inks. Further, as mentioned previously, vials of inks made with C16 appeared to have smooth surfaces while inks made with C7 had uneven or rough surfaces. The presence of aggregates in the C7 ink may cause non-uniform application.2). It was observed that with increasing C7 percentage in the ink, there was a steady decrease in the value of R2, providing evidence that the C7 inks provide less uniform graphene application during screen printing.This non-uniform application was quantified by replotting the data derived from nearly 400 individual measurements in Fig. 2 values in the C16 rich inks and the very poor R2 values in the C7 rich inks was investigated by comparing 1/X and 1/N separately, as shown in Fig. The cause for the less than perfect RHere we demonstrated a graphene-based screen-printing ink prepared by interfacial trapping of untreated graphite using non-toxic chemicals. The ink can be prepared in as little as 40 s by simple shaking of a graphite/water/alkane mixture. In contrast to other approaches, no high energy step such as tip sonication or high shear mixing was involved, only a bubble tea shaker set at 320 rpm. Graphite loading in the ink was as low as 0.01 g/ml with no post-printing procedures requiring harsh chemical treatment or high temperatures as no high boiling point chemical binders or stabilizers were involved in the formulation. The solvents, water and an alkane, can be effectively removed at 90 °C or with 40 s of microwaving. The ink demonstrated excellent electrical conductivity of approximately 300 S/m, with good bending tolerance.The rate of ink formation was shown to be affected by the alkanes used, with the C16 based ink forming faster than the C7 based ink. A statistical approach for analyzing the correlation of the ink’s resistance with the number of prints and the width of the printed line was introduced, providing information about how the uniformity of the ink related to its mechanism of formation. The rheological properties of the ink indicated a wide range of suitable printing speeds, similar to typical commercially available screen-printing inks. This graphene stabilized emulsion ink demonstrates significant potential for enabling the next generation of paper based diagnostics to improve the medical and environmental diagnostics of underserved populations.2O followed by a forty-second bubble tea shaker shaking . The bubble tea shaker mimicked the process of hand shaking but allowed a more reproducible study of the effect of shaking time. An image of the bubble tea shaker can be found in Figure heptane (C7) and hexadecane (C16) ink used for screen printing are made by using 0.1 g graphite , 3 ml of heptane  or hexadecane , and 7 ml of DI HSamples for optical image of C7 and C16 ink are made by diluting the ink using the corresponding continuous phase to separate spheres, which means C7 ink was diluted using C7 and C16 ink was diluted with C16. Images were taken using Leica M125.The stencil used for screen printing are made using Speedball Diazo Ultimate Screen Printing Kit followed by the instruction. Paper used for screen printing is Heavyweight Drawing from Strathmore. When preparing samples for the electrical conductivity study, each ink was printed on paper from 1 to 10 times using a stencil with designed lines widths from 1 to 6 pt. See Figure −1 was performed, and the testing temperature is controlled at 30 °C.C7 and C16 ink used for viscosity measurements are used as it is once a full emulsion was obtained. Commercial screen-printing ink is a fabric screen printing ink purchased from Speedball. Viscosity of the inks are measured using the rheometer  with a sandpaper  modified parallel plate . A steady shear test from 0.1 to 200 sC16 and C7 ink sample were made using the bubble tea shaker to obtain a full emulsion and poured into an DT-1202 Acoustic and Electroacoustic spectrometer sample cell . The emulsion sphere size and its distribution were measured, and the attenuation spectra is analyzed using Dispersion Technology software.Conductivity of the printed ink line was measured using a two-point probe multimeter (Fluke 25 multimeter), and each measurement was repeated at least three times and an average was taken. Resistance value was reported as a function of the product of the line width and the number of prints. Samples for the ink conductivity measurement were prepared by drop casting it onto a glass slide, and dried at 90 °C in an oven for a week. Sheet resistance of the ink was measured using a four-point probe (Signatone S-302), and the thickness of the ink dot was calculated using the volume of the ink drop casted, loading of the graphite, area of the droplet, and graphene density. Conductivity of the ink is calculated based on the sheet resistance the thickness of the dry ink droplet.Exfoliated graphene sheet sample was obtained by dropping the ink into a large excess of the water. Emulsion droplets break and forms a graphene skin since the oil phase is no longer the continuous phase. A TEM grid  was used to lift the graphene skin. The graphene sample was characterized using TEM (FEI Lab6 20–120 kV transmission electron microscope) at 80 kV.Supplementary file 1


Recent studies suggest that attention samples space rhythmically through oscillatory interactions in the frontoparietal network. How these attentional fluctuations coincide with spatial exploration/displacement and exploitation/selection by a dynamic attentional spotlight under top-down control is unclear. Here, we show a direct contribution of prefrontal attention selection mechanisms to a continuous space exploration. Specifically, we provide a direct high spatio-temporal resolution prefrontal population decoding of the covert attentional spotlight. We show that it continuously explores space at a 7–12 Hz rhythm. Sensory encoding and behavioral reports are increased at a specific optimal phase w/ to this rhythm. We propose that this prefrontal neuronal rhythm reflects an alpha-clocked sampling of the visual environment in the absence of eye movements. These attentional explorations are highly flexible, how they spatially unfold depending both on within-trial and across-task contingencies. These results are discussed in the context of exploration-exploitation strategies and prefrontal top-down attentional control. The prefrontal attention spotlight dynamically explores space at 7–12 Hz, enhancing sensory encoding and behavior, in the absence of eye movements. This alpha-clocked sampling of space is under top-down control and implements an alternation in exploration and exploitation of the visual environment. Visual selective attention modulates both neuronal baselines7 and the strength of visual responses8, decreases neuronal response latencies9, modifies the spatial selectivity profiles of the neurons11 and decreases shared inter-neuronal noise variability12.The brain has limited processing capacities and cannot efficiently process the continuous flow of incoming sensory information. Selective attention allows the brain to overcome this limitation by filtering sensory information on the basis of its intrinsic salience (a child crossing the road in front of your car) or its extrinsic value (your old favorite coffee mug which you know is somewhere on your crowded desk). Visual selective attention speeds up reaction times13, the spotlight theory of attention assumes that attention is focused at one location of space at a time14. In this framework, the spotlight is moderately flexible. It is shifted from one location to another, independently from eye position, under the voluntary control of the subject, and its size is adjusted to the region of interest very much like a zoom lens. Converging evidence demonstrate that the prefrontal cortex (PFC) is at the origin of the attentional control signals underlying the behavioral attentional spotlight18. Supporting this idea, we recently demonstrated that this attentional spotlight can be reconstructed and tracked from PFC neuronal population activity with a very high spatial and temporal resolution20. However, recent experimental work provides a completely different perspective onto selective attention, suggesting that spatial attention samples the visual scene rhythmically27. These studies report that target-detection performance at an attended location fluctuates rhythmically very much like overt sampling processes, such as eye exploration in primates30 or whisking in rodents31. The neural processes at the origin of this rhythmic sampling of space by attention are still poorly understood. Recent works propose that neural oscillations in the fronto-parietal network organize alternating attentional states or shifts in attention that in turn modulate perceptual sensitivity33.Based on the early work of William James (1890)In the present study, we provide evidence reconciling these two seemingly contradictory views of spatial attention. Specifically, we demonstrate that the decoded PFC  attentional spotlight explores space continuously, through a sequence of attentional shifts that are generated at a specific alpha 7–12 Hz frequency. Crucially, we show that these oscillations of the attentional locus determine both neuronal sensory processing, defining how much information is available in the PFC about incoming sensory stimuli, and perception, determining whether these incoming sensory stimuli are prone to elicit an overt behavioral response or not. Using Markov chain probabilistic modeling, we further show that space exploration by alpha-clocked attentional shifts depends on both trial and task specific spatial contingencies, implementing an alternation between exploration and exploitation cycles.36 or at which  location attention is placed19. In these studies, neuronal signals were averaged over time intervals ranging from 150 to 400 ms (refs. 38). Larger averaging window sizes produce higher decoding accuracies  or changes in the FEF population code at a specific frequency . Figure l39 Fig. , and plol39 Fig. . This hol39 Fig.  as well l39 Fig. , but in l39 Fig. . This co23 as well as between LFP beta and spiking activity and behavior33. An important question is thus whether changes in super MUAs are linked to the phase of oscillatory activity in the local field potentials. A significant alpha component is present in the super MUAs . Neuronal activities were averaged between 50–100 ms post-target and used to quantify the accuracy of a four-class classifier at assigning target location to the actual quadrant it was presented in (see the Methods section). For each session, target-related PFC decoding accuracy was then computed for each independent bin of target-to-attentional oscillation phase-relationship. To increase the resolution of this analysis, this operation was repeated with successive phase bins shifted by 5% of their width. The lag that generated the highest discrimination between maximum and minimum decoding accuracy in the cycle was used to define optimal phase-shift between sensory processing and attention signal oscillations, c. Neur40 Fig. , phase l41. These two peaks coincide with those identified in the prefrontal attention-related information  depended on target presentation time relative to the PFC attention information oscillation cycle Fig. 4d, e). An average difference in peak and trough decoding accuracies of 10% can be noted when decoding accuracies are cumulated at optimal phase-shift between target detection and signal oscillations  or in terms of displacement of the attentional spotlight. In the following, we provide robust evidence in favor of attentional displacement.Here, we explore the incidence of the oscillations in PFC attention-related information onto the processing of uncued distractors and the production of false alarms Fig. , along tIn a second step, we quantified how responses to distractors  depended on distractor presentation time relative to the PFC attention information oscillation cycle. An average difference in peak and trough false alarm rate of more than 10% can be noted when false alarms are computed at optimal phase-shift between distractor detection and signal oscillations Fig. . This diOverall, we show a direct modulation of how the PFC represents distractors as well as the overt behavioral responses to distractors by the ongoing PFC attention information alpha oscillations. These observations support the hypothesis of a displacement of attention in space. In the following, we provide evidence for an explicit link between the above described oscillations in PFC attention information and exploration of space by a dynamic and rhythmic attentional spotlight operating in the alpha frequency range.19, we demonstrated that the continuous  readout of a linear classifier assigning neuronal activities to a spatial location of attention in the PFC is predictive of behavior, both in terms of hit and false alarm rates. In the following, we apply the same approach to extract  attentional spotlight trajectories in time before and after cue presentation, to the major difference that the readout is obtained at higher temporal resolution, from neuronal responses averaged over 50 ms rather than on 150 ms as presented in the Astrand et al.19. Supplementary Movie In a previous studyx- and y-dimensions are presented in Fig. x- and y-traces, on all trials and all sessions, for each monkey , in the same range as identified for the global attention population information. No statistical difference is observed between alpha oscillatory peaks in the x- and y- attentional traces and in the global attention information content (p = 0.49 and p = 0.87 respectively), confirming a strong link between these measures. These PFC attentional spotlight trajectories are exploring space homogenously. Interestingly, a significant difference was observed between the distributions of attentional displacement along the x- and y-axis , indicating a larger exploration of space along the vertical dimension. Overall, the PFC attentional spotlight explores space both rhythmically and continuously.Projections of an exemplar PFC attentional spotlight trajectory onto the p > 0.99). However, the spatial distribution of these shifts vary significantly between pre-cue and post-cue epochs. Specifically, Fig. During cued target detection tasks, the cue orient attention towards the spatial location where the target is expected to be presented. The absolute distance between two successive attentional shifts does not vary between the period before Fig. , black aThe next question is thus whether the attentional temporal dynamics and attentional exploration trajectories described up to now are also influenced by task structure. To address this question, we use Markov chain probabilistic modeling to describe how the attentional spotlight explores space in two different versions of a cued target detection task, that only differed in the number and localization of the task-relevant items: a first version , in which the cue could orient attention to one of the four possible quadrants (18 sessions), and a second version in which the cue oriented attention to only two possible quadrants, placed along the diagonal one with respect to the other (16 sessions). For both monkeys, oscillations in the PFC attention information did not depend on the task Fig. . In contOverall, we provide evidence that the PFC attentional spotlight explores space at an alpha that remains stable within trials and across tasks. However, we show that how this decoded spotlight explores space depends on both within-trial and across-task task contingencies.26. This rhythmic sampling of space is phase-reset and entrained by external events of interest. It can also be observed spontaneously43, and is proposed to organize the tracking of task-relevant spatial locations by attention in time45. It has been proposed that, when prior information is available, such a rhythmic sampling of information is more efficient than a continuous sampling of space46. These observations have led to reconsider the model of a continuously active attention spotlight in favor of a rhythmic sampling of attention at relevant spatial locations, including during sustained attention states26.Converging behavioral evidence indicates that attention and perception are not anchored at a specific location in space, but rather exhibit a temporal alpha rhythmicity25 or multiple objects at a given location47. Here, we argue that the decoded attentional spotlight provides a direct access to the intrinsic attentional rhythm, i.e., 8 Hz, though how this reflects onto behavior will fully depend on task design and on how the spotlight successively samples relevant task locations. In our task, cues have a 100% validity. Hence, the attentional spotlight is on average into the cued quadrant, sampling visual information at 8 Hz. We predict that if the cue was not fully valid, behavioral sampling frequency might be lower than 8 Hz, directly co-varying with cue validity. Attentional periodicity in monkey FEF has been suggested before, albeit with a faster frequency33 (18–25 Hz). This frequency difference may reflect disparities in experimental design or task difficulty. Alternatively, it could reflect specific differences between FEF LFP and MUA processes  these oscillations are ongoing and can be identified independently of the intervening task events, (2) they are reset by relevant external events such as spatial cues and (3) they occur in a well-defined functional alpha frequency range. However, even if attentional exploration targets task-relevant locations, as reflected by the rhythmic enhancement of neuronal sensory processing and behavioral performance at the cued target location, exploration is not restricted to these locations. Rather, space exploration by attention extends to uncued task irrelevant spatial locations, as reflected by the rhythmic enhancement of neuronal sensory processing and behavioral overt report at uncued unpredictable distractor locations. Several recent behavioral studies suggest that attention fluctuates at around 8 Hz. This sampling can be distributed across multiple spatial locations7. As a result, one predicts that this dependence of sensory processing onto attention spotlight oscillations will be found at all stages of the visual system. However, phase relationships between local neuronal and stimulus presentation is expected to vary, reflecting a top-down cascade of influences, in agreement with the role of the FEF in attentional control49.The phase between the attentional spotlight ongoing oscillations and a given stimulus presentation accounts from 10% (in the case of the target) to 30% (in the case of distractors) of the accuracy with which PFC neuronal populations encode the location of this stimulus. In other terms these oscillations—i.e., where the attention spotlight falls in space—critically impact the sensory processing of incoming stimuli. Tracing down this effect all throughout the visual system would be extremely relevant. Neuronal responses to low-salience task-relevant stimuli has been shown to arise earlier in the PFC than in the parietal cortex25, highlighting the high predictive power of these neuronal population oscillations.These oscillations also determine overt behavioral perceptual outcome, accounting from 10%  to 30% (in the case of correct target identifications) of stimulus detection. This is globally higher than the range of reported oscillatory changes in behavioral hit rates26 that organize as a rhythmic alternation between exploitation and exploration states of space sampling by attention50.Overall, this suggests the existence of perceptual cycles55 a parallel processing model, driven by bottom-up information, dominating when visual search is easy; and a serial processing model, driven by top-down mechanisms, dominating in difficult visual search41. In the context of this latter model, it has been hypothesized that the brain controls an attentional spotlight that scans space for relevant sensory information. In a previous study19, we assessed, based on the  decoding of the neuronal population activity of the FEF, the tracking of this attentional spotlight in time19. Here, we show that the attentional spotlight explores space serially both at relevant (cued) and irrelevant (un-cued) locations, alternating between the exploitation and the exploration of the visual scene26. The activity of the parietal10 and PFC56 cortical regions has been shown to change drastically between exploitation and exploration behavior. In particular, exploration is associated with faster though less accurate oculomotor behavior10 and a disruption of PFC control signals56. This is proposed to facilitate the processing of unexpected external events10, the expression of novel behavior and learning through trial and error56.Two models have been proposed to account for the spatial deployment of attention50 propose that theta rhythms organize neural activity into alternating attentional states associated with either sampling  at a behaviorally relevant location or shifting to another location . In this model, how much overt or covert attention is placed onto a given item of the visual scene depends on its behavioral relevance. Because in our task, exploitation is an unexpected low frequency event, we propose that exploration is the default mode of the system, while exploitation, requires effort or a top-down drive to be implemented. Whether this exploitation is implemented by an independent theta clock remains to be tested. This would reconcile the seemingly contradictory views of the sampling/shifting hypothesis and an alpha exploration/exploitation hypothesis . Interestingly, the rhythm at which thisexploration/exploitation alternation takes place coincides with the rhythm at which attention behaviorally explores the different part of a given object22. Overall, this leads us to postulate the existence of attentional saccades that can either be directed towards specific items for exploitation purposes, or deployed onto the entire visual field for exploration purposes.Our observations strongly indicate that exploration and exploitation dynamically alternate within trials. This alternation of exploration and exploitation of space by the attentional spotlight thus appears to optimize subject’s access to incoming information from the environment by a continuous exploration strategy, very much like is described for overt exploration behaviors such as saccadic eye movements, whisking or sniffing59. Since then, several studies have demonstrated a functional dissociation between these two processes, and rhythmic attentional sampling has been shown to be independent from micro saccade generation60. Our observations support a continuous exploration of space by the attentional spotlight organized thanks to a rhythmic re-orientation of the attentional spotlight taking place at an alpha rhythm. This framework leads to an interesting set of experimental predictions. For instance, attentional capture and distractibility by an intervening distracting item is expected to coincide with an ongoing attentional re-orienting towards this item19. Likewise, inhibition of return64, is expected to reflect as an under-exploration of previously visited locations with respect to unexplored locations. This covert saccade-like exploration is proposed to be an intrinsic property of attention, taking place irrespectively of the ongoing behavior and building onto a rhythmic alpha clock. Its spatial pattern, that is to say the portion of space that is being explored by these attentional shifts, as well as the frequency at which task-relevant items are explored are however expected to be under top-down control.Covert exploration of space by attention is more energy efficient than overt exploration by the eyes and the former serves to inform and guide the latter. In an initial “premotor theory of attention”, these two processes, namely attentional selection and saccadic eye movements, have been suggested to rely on identical cortical mechanisms. This theory hypothesizes that attentional displacements, mirror saccades of the eyes except for the recruitment of the extra-ocular muscles49. As a result, one expects that the exploration of space by the PFC attention spotlight be strongly biased by top-down voluntary control. Confirming this prediction, we show that task goals significantly affect attentional space exploration strategy. Specifically, the locations where the PFC attentional spotlight explores space are modulated both (1) within trials, by the expected position of the target after cue presentation, and (2) across tasks, by the general expectations about sensory events. In other words, relevant task items are more explored than irrelevant locations, where relevance concatenates information relative to the ongoing trial and task design. This is in agreement with prior behavioral observations reporting that the attentional sampling rate observed at the behavioral level decreases as the number of task-relevant items increases65. Overall, this indicates that the rhythmic exploration of space by attention, is an intrinsic, default-mode state of attention, that can be spatially modulated by task context and internal expectations. A strong prediction is that this rhythmicity in attentional spatial processing will directly impact attention selection processes in lower level cortical areas, through long-range feedback processes60, possibly mediated by NMDA receptors66.Numerous studies indicate that PFC and specifically the FEF play a crucial role in attention orientation and attention control32, revealing the neuronal population dynamics associated with rhythmic attentional sampling.Overall, our work describes for the first time the spatial and temporal properties of the population PFC attention spotlight. It demonstrates a continuous exploration of space, that is mediated by attentional saccades that unfold at an alpha 7–12 Hz rhythm and that accounts for both neuronal sensory processing reliability and overt behavioral variability. Importantly, it bridges the gap between behavioral evidence of attentional rhythmic space sampling and local field attention-related oscillatory mechanismshttps://www.neurobs.com/). To start a trial, the bar placed in front of the animal’s chair had to be held by the monkeys, thus interrupting an infrared beam. The onset of a central blue fixation cross (size 0.7° × 0.7°) instructed the monkeys to maintain eye position inside a 2° × 2° window, defined around the fixation cross. To avoid the abort of the ongoing trial, fixation had to be maintained throughout trial duration. Eye fixation was controlled thanks to a video eye tracker (Iscan™). Four gray square landmarks (LMs—size 0.5° × 0.5°) were displayed, all throughout the trial, at the four corners of a 20°x20° hypothetical square centered onto the fixation cross. Thus, the four LMs  were placed at the same distance from the center of the screen having an eccentricity of 14° (absolute x- and y-deviation from the center of the screen of 10°). After a variable delay from fixation onset, ranging between 700 and 1200 ms, a small green square (cue - size 0.2° × 0.2°) was presented, for 350 ms, close to the fixation cross (at 0.3°) in the direction of one of the LM. Monkeys were rewarded for detecting a subtle change in luminosity of this cued LM. The change in target luminosity occurred unpredictably between 350 and 3300 ms from the cue off time. In order to receive a reward (drop of juice), the monkeys were required to release the bar in a limited time window (150–750 ms) after the target onset . In order to make sure that the monkeys did use the cue instruction, on half of the trials, distractors were presented during the cue to target interval. Two types of distractors could be presented: (i) uncued landmark distractor trials ; these corresponded to a change in luminosity, identical to the awaited target luminosity change, and could take place equiprobably at any of the uncued LMs; (ii) workspace distractor trials ; these corresponded to a small square presented randomly in the workspace defined by the four landmarks. The contrast of the square with respect to the background was the same as the contrast of the target against the LM; when presented at the same radial eccentricity as the LMs, the workspace distractor had the same size as the landmarks; for smaller eccentricities, the size of the workspace distractor was adjusted for cortical magnification such that it activated an equivalent cortical surface at all eccentricities. The monkeys had to ignore all of these distractors. Responding to any of them interrupted the trial. If the response occurred in the same response window as for correct detection trials (150–750 ms), the trial was counted as a false alarm (FA) trial. Failing to respond to the target (Miss) similarly aborted the ongoing trial. Overall, data was collected for 19 sessions . The behavioral performance of each animal is presented in Fig. The task is a 100% validity endogenous cued target detection task Fig. . The aniBilateral simultaneous recordings in the two frontal eye fields (FEF) were carried out using two 24 contacts Plexon U-probes Fig. . The con35, a regularized linear decoder was used to associate, on correct trials, the neuronal activity estimated on a given interval in the cue to target interval and the cued location. The decoder was trained on a random set of 70% of the correct trials at a specific time in the cue to target interval, then tested on the 30% remaining at all time after cue presentation  coordinates of the cued landmark for each of these N training trials. During testing, the output of the classifier was estimated for a 48 element vector corresponding to the average neuronal response on each recording channel for the time interval of interest on a testing trial, new to the classifier. This output can be read as a continuous  estimate of attention location19 or as a class output, corresponding to one of the four possible visual quadrants35. When seeking for a continuous  readout of attention location, we performed the training using the neuronal activities of Hits averaged over 50 ms immediately before target presentation, then we tested the decoder on neuronal activities averaged over 50 ms all throughout the cue to target interval. When taking a classification perspective, we performed cross-temporal decoding analyses . As in Astrand et al.zes Fig. . The cor23, over the attentional period ranging from 500 ms post cue presentation to 2100 ms. Standard error in the power spectrum corresponds to spectral variability during this time interval. Global power spectrum 1/f component was removed from the dataset using a *f normalization , and merged together across the 19 recording sessions. Behavioral performance, defined as the proportion of (hits/(hits + misses)) was then computed at every millisecond over. The spectral analysis of this time series was performed on detrended data using a Morlet Wavelet transform as in Fiebelkorn et al.68. Specifically, for the time frequency analyses, Morlet wavelet transforms were independently applied to the original data time series . The significance of peak frequency distributions in the range of interest (7–12 Hz) was assessed against the frequency content of time series generated by the random permutation  and low-spiking (labeled as 0) epochs. The probability of individual channel firing as a function of the oscillatory cycles of the session’s Super MUA was then computed as follows. For each channel, for frequencies from 5 Hz to 15 Hz, the spiking probability was computed for the up (+/−π/2 around oscillation peak) and down (+/−π/2 around oscillation trough) oscillatory phases of the frequencies of interest over the entire time window. For each frequency, the analysis time window was adjusted to 1.5 oscillatory cycle length and computations were performed over a minimum of 50 time bins. All further analyses on this metric were performed onto an attentional epoch running from 500 ms post-cue to 2100 ms post-cue.In order to track whether the frequencies identified on the decoded attentional information causally reflected onto behavior, the following analysis was performed. For each session i, characteristic attention information oscillatory frequency F(i) and Phase P(i) determined using the above described wavelet transform analysis. The decoded classification attention information signal was modeled as a sinusoidal wave determined by the function MSi(t) = sin(2. π.F(i).t-P(i)). Using this modeled signal (MSi), and based on target time from cue presentation, trials were assigned to one of 10 possible phase intervals ranging from [–π + π] phase offset from the modeled sinusoidal wave For each of these subsets of trials, decoding accuracy of target location (resp. distractor location) and percentage of hit trials  was extracted . As sensory processing or behavioral outcome could be phase lagged with respect to signal oscillations, target time was progressively shifted using 5 ms steps, so that the phase interval associated with peak sensory processing or behavioral outcome coincided with phase 0. This procedure was applied independently for each of the 18 recording sessions and the outcome of this analysis was then averaged over all sessions, so as to account for variations of F(i) and Phase P(i) from one session to the next. For a precise estimation of phase difference between oscillations in attention information classification decoding and oscillations in sensory processing or behavioral outcome, a circular mean of the corresponding wavelet transform continuous phase difference between the two signals at frequency F(i) was extracted.x,y) time series corresponding to the decoded spatial location of attention during the cue to target interval was collapsed onto the four possible screen quadrants, thus representing how attention moved from one quadrant to the other in time. Based on these discrete time series across all trials of a given session. A Markov chain model was used to estimate the probability that attention stayed in a given quadrant as well as the probabilities that it moved from the current quadrant to one of the three others. This model was performed using the Hmmestimate Matlab function of the Statistics and Machine Learning Toolbox. To compensate for possible idiosyncratic exploration biases of each monkey, the post-cue transition probabilities were normalized with respect to pre-cue spatial attention exploration transition probabilities. Transition probabilities were then normalized for each session and averaged over all sessions and both monkeys. This Markov chain modeling of spatial attentional exploration strategy was independently performed for both tasks: the four cued-location and the two-cued location tasks.Markov probabilistic chain models were used to characterize the spatial attention exploration strategy of each monkey from cue to target presentation. For each trial, (Further information on research design is available in the Supplementary InformationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesSupplementary Movie 1


H2) and hydrogen solubility of the growth substrates. The role of PH2 was found to be crucial, with a low defect density monolayer graphene being obtained in low PH2 (90.4 mbar), while partial graphene coverage occurred at high PH2 (137.3 mbar). To further understand the role of substrate’s composition, binary alloy with compositions of 20, 30, 50, 60 and 80 wt.% tin in copper were made by arc-melting. Graphene quality was found to decrease with increasing the content of copper in the Cu–Sn alloys when grown using the conditions optimised for Sn substrates and this was related to the change in hydrogen solubility and the high catalytic activity of Cu compared to Sn. This shall provide a tool to help optimising CVD conditions for graphene growth based on the properties of the used catalytic substrate.The chemical vapor deposition (CVD) of graphene on liquid substrates produces high quality graphene films due to the defect-free and atomically flat surfaces of the liquids. Through the detailed study of graphene growth on liquid Sn using atmospheric pressure CVD (APCVD), the quality of graphene has been found to have a close relationship with hydrogen flow rate that reflects on hydrogen partial pressure inside the reactor (P Graphene can be synthesised by a variety of methods including mechanical exfoliation , liquid 4 and its effect on the substrate’s hydrogen solubility. This systematic study shed the light on the correlation between the properties of the substrate used and the effect of the various process conditions.In this report, we studied the effect of various process conditions on the growth of CVD graphene on liquid Sn, highlighting the role of hydrogen. In order to optimise the process conditions, a detailed investigation was conducted to study the effect of residence time of the reactive species. This was a starting point to optimise the total gas flow rate and carbon concentration. The catalytic activity of Cu and the change in physical/chemical properties of the growth substrate were explored by using different compositions of Cu–Sn alloys. Such a study has never been reported before; thus, this should provide new insight on the role of hydrogen solubility of the growth substrate in determining the optimum hydrogen flow rate/partial pressure and hydrocarbon concentration. Furthermore, process temperature was investigated showing its effect on the decomposition of CHPure Sn (99.995%) and Cu (99.9999%) metals were used  as growth substrates, held in tungsten boats . Cu–Sn alloys were prepared using an arc-melting technique; elemental pure Cu and Sn were selected as to give a total weight of 5 g at various nominal compositions  with their compositions and melting points given in In a typical APCVD run, a sample was loaded at the centre of the isothermic zone and the reaction tube was first evacuated to base pressure then backfilled with hydrogen/argon mixture until atmospheric pressure was reached. The furnace then was switched on to gradually heat and reach the desired temperature. Then, the sample was left for annealing at that temperature for 30 min. The growth step began by introducing the hydrocarbon feedstock (methane) for the desired growth time. Finally, methane and the furnace were switched off and the chamber was left to cool naturally to room temperature under flow of hydrogen/argon mixture. Each CVD run was typically repeated 3–5 times to ensure consistency. CVD reaction tube was purged and filled with nitrogen after each run to ensure clean environment.Field emission gun scanning electron microscopy (FEG-SEM)  was used for the characterisation of the as-grown graphene. An accelerating voltage of 8 kV was used with a maximum working distance of 10 mm.TM software . The software was used to calculate the band intensity ratio, I2D/IG and ID/IG.Raman spectroscopy was the predominant technique used to study the graphene grown on the different substrates. All Raman measurements were carried out using a Renishaw inVia system with an excitation laser wavelength of 633 nm . The main graphene characteristic peaks  Raman peaks were fitted using mixed Lorentzian–Gaussian functions in the Wire 4.2The CVD process requires careful tuning of the growth conditions based on the properties of the substrate used  as in the case of graphene deposition. The process-limiting phenomena can be either surface-reaction-rate or gas-phase limitations . The sysiso, by the mean flow velocity, <ν> [s) to that within the chamber (Fr). <ν> was then calculated by dividing the cross-sectional reaction tube. Thus, <ν> in terms of the reaction pressure Pr, and temperature Tr, was given byStudying the residence time, τ, of the reactive gases can elucidate the reaction equilibrium within the reactor and how it effects the subsequent graphene deposition. It could be described as the time that the reactants spend in the gas stream in the CVD reactor with a laminar flow rate, and it is inversely proportional to the total gaseous mass flow rate . Lewis eity, <ν> . The ides was measured at standard temperature and pressure, . Thus, τ was calculated by Equation (2):FCH4) in the reactor by reducing the H2 and Ar flow rates and keeping the CH4 flow constant at 5 sccm. Different H2 and Ar flow rates were used in order to change the total flow rate in the reaction chamber (The residence time (RT) was investigated in combination with increasing the methane ratio  of <36.2 cm−1 for samples grown using 25 H2:250 Ar:5 CH4 (sccm), giving CH4 ratio (RCH4) = 0.017 and RT = 12.8 s. These samples also had an average Raman intensity ratio I2D/IG of 1.6, which is characteristic of single layer graphene and a ID/IG of 0.34 which indicated low defect density in graphene with a low defect density and large graphene domain.Raman mapping were collected from 121 points  for each of the as-grown graphene films on the ex-situ, solidified Sn substrate. These spectra were then used to map the band intensity ratios ICH4 and RT. The average I2D/IG increases while ID/IG decreases with the increase of RT while the coverage is full at RT = 12.8  were obtained by adjusting the H2 flow rate during the APCVD run. This PH2 was kept constant throughout the experiment . All the remaining growth parameters  were kept constant throughout this experiment series  (2 flow rates (best at 20 sccm). The lowest H2 flow rate, 20 sccm, was considered as a critical point for the flow rate. This leads to a rich hydrocarbon environment causing saturation of reactive species and an increase in the number of deposited graphene nuclei, a smaller grain size and a more intense D band in the Raman spectrum, which indicates a decrease in graphene quality. These results showing the change in film morphology and defect density as a function of H2 flow rate indicates that the use of lower H2 flow rates  can improve the quality of graphene on liquid Sn probably as it can transform sp3 bonds into sp2. [2. [2 diffused out of the Cu when the H2 flow was shut-off, whereas no reversible response was measured in Ni, suggesting that H2 desorbed from the Ni surface. It was found that by optimising and minimising hydrogen content in Ni substrate, graphene without a Raman D-peak could be achieved. However, this role of H2 substrate solubility on the CVD of graphene has not been confirmed with other substrates such as Sn. Indeed, similar to Ni, the hydrogen in Sn is low compared to Cu [2 interactions are surface limited due to the poor bulk solubility. Therefore, lower hydrogen flow rate is more favourable in the case on Sn growth substrate as demonstrated and confirmed by the obtained results.Optical microscopy and Raman spectroscopy found that at the formation of graphene film was disrupted at the highest Heposited . This obgenation . Raman s35 sccm) a,b based35 sccm) c, where nto sp2.  This obssp2. [2.  They foued to Cu ; thus, hed to Cu . HoweverH2+CH4P) in the reactor can be controlled through either the total pressure in the reaction chamber tP or the flow rate of a diluent gas such as Ar(g) or N2(g). Thus, different Ar flow rates were introduced into the reaction chamber to tune the H2+CH4P . Lowering the Ar flow rate to 100 sccm, i.e., increasing the H2+CH4P to 233.76 mbar, caused the deposition of amorphous carbon. While reducing H2+CH4P via increasing the Ar flow rates (250–450 sccm) led to an increasing number of graphene layers to be deposited, as indicated by the average Raman intensity ratio I2D/IG  low Ar flow rates (100 sccm) when ID/IG begins to decrease due to the increasing amorphous nature of the deposited films [4, H2 and Ar. Thus, each axis goes from 0 to the reaction pressure, as the total pressure is constant for all systems. The Raman intensity ratios, I2D/IG and ID/IG are plotted as ternary contour plots for visualisation in It was found that the best quality graphene was grown at o I2D/IG b. In coned films . The inced films . Therefo2D/IG and low density of defects ID/IG, which are associated with considerably high-quality graphene, are found at the higher partial pressures of CH4 and H2. In the case of Sn, it was found that in order to reach full coverage of monolayer graphene with a low density of defects, a high carbon concentration was required, which is opposite to the observations for APCVD graphene grown on Cu as reported in the literature [In the ternary plots , it can terature ,43. ThisAnother crucial parameter investigated was growth temperature. Initial experiments of graphene growth on liquid Sn used a growth temperature of T = 1020 °C. This temperature was selected because it had been extensively reported in the literature as successful for the growth of graphene on Cu and had also been reported with work by other groups on liquid Sn substrates . The tem2D/IG (≈1.4–1.7) in agreement with monolayer graphene. Moreover, both have relatively low ID/IG (≈0.3–0.5) indicating low defect density, with the D’ band present as a right shoulder on the G peak. D’ is not widely considered due its small intensity in comparison to the D peak. However, it is clearly distinguished from the G peak in the case of the two growth temperatures used (1100 and 1120 °C), indicating a moderate density of defects [The graphene grown at T = 1100 °C and T = 1120 °C showed graphene films that exhibit high I defects . AlthougThe temperature effect on deposition can be explained by considering that as temperature increases, a greater proportion of the adsorbed carbon can surmount the energy barrier, achieving attachment and contributing to graphene formation . MoreoveD/IG ≈ 1, indicating that this is due to phonon scattering from in-plane defects (D band), rather than from stacking induced disorder [2D/IG ≈ 0.8–1 is characteristic of bilayer to few layer graphene deposition. Another observation is the changed appearance of the quartz reactor tube, which turned black towards the end of the exhaust end during the reaction  is the temperature [The increase in hydrogen solubility at elevated temperature can be explained by Sievert’s law (Equation (3)):perature . A growt4 decomposition, causing the gaseous composition to change due to greater thermodynamic stability of larger hydrocarbons at higher temperatures. [Generally, the results obtained using different growth temperatures confirm that elevated temperatures increase the rate of CHratures. .Although the produced graphene films exhibited varying D peak intensities, the demonstrated optimal conditions showed low defects density. The conducted experiments and results should provide an insight on the effect of the used substrates’ properties on determining the optimum CVD conditions in order to tune the characteristics of the produced graphene film. This should pave the way to explore possibilities for simple and direct graphene transfer from the molten metal.As aforementioned, the ideal growth conditions for graphene deposition between Cu and Sn are very different; Cu needs higher hydrogen flow rate, while Sn requires lower hydrogen flow rate. Moreover, a Cu–Sn alloy has a higher carbon solubility than bare Cu substrate ,55; ther2D/IG and ID/IG of the samples, demonstrated in Raman mapping was taken on the as grown graphene film on each substrate, and Raman spectra representing the resulted graphene on each Cu–Sn composition are shown in 80%–Sn20%. The proposed scenario of an increased number of graphene layers as the content of Cu in the alloy increases, is believed to be due to the catalytic nature of Cu. As Cu catalytically stimulates CH4 cracking, higher Cu wt.% leads to a faster and higher CH4 decomposition rate, causing accelerated graphene nucleation and deposition. [The number of graphene layers was found to increase with the high Cu content in the alloy composition. When Cu wt.% is in the range of 80 to 100%, an obvious degradation in graphene quality occurs under the ideal growth conditions for Sn, with the D band activated due to the high presence of defects in Cuosition. .20%–Sn80% resulted in a bumpy surface, while increasing Cu further to 40% resulted in the formation of big cracks causing partial graphene coverage. At 80% Cu, the SEM image showed a wavy surface, the propagation of this surface waves of the deposited graphitic film onto the Cu-rich alloy surface was most likely due to the wetting of the growing graphitic material with the liquid Cu layer. Voids are also seen within the propagating waves, leading to areas with no graphene deposition.SEM micrographs of the deposited graphene films found a uniform image contrast over the semi-flat surface for pure Sn , indicat2 solubility for each of the different compositions  creates cracks in the graphene film, leaving bare metal which may describe the breakage in the graphene film [Another possible scenario for the change in graphene layers with the various Cu content in the Cu–Sn alloys is due to the change in Hositions . As menteased it . Therefoncreased ; thus, tompounds . The secene film  when Cu-4 decomposition . This study not only provides an alternative method to grow large-area graphene films on liquid catalytic substrates, but also points out more important factors, such as hydrogen and carbon solubility and hydrogen partial pressure, to influence the quality of graphene.In conclusion, APCVD graphene grown on liquid Sn and liquid Cu–Sn alloys has been demonstrated as a potential avenue for large-area continuous graphene films. Using APCVD to grow graphene on liquid Sn, the experiments have shown the graphene quality and the growth kinetics are both sensitive to reactants’ residence time, hydrogen flow rate, hydrocarbon partial pressure and growth temperature. When considering CVD growth of graphene on liquid Cu–Sn alloy at different compositions, important factors have been suggested to include their relative catalytic abilities, carbon solubility and hydrogen solubility. It has been demonstrated that graphene quality decreases with increasing the content of Cu in the Cu–Sn alloys when grown using the conditions optimised for Sn substrates. This can be explained due to the change in hydrogen solubility in the Cu–Sn compositions and also because of the different catalytic nature of Cu, leading to faster CH


The World Health Organization recommends confirmatory diagnosis by microscopy or malaria rapid diagnostic test (RDT) in patients with suspected malaria. In recent years, mobile medical applications (MMAs), which can interpret RDT test results have entered the market. To evaluate the performance of commercially available MMAs, an evaluation was conducted by comparing RDT results read by MMAs to RDT results read by the human eye.Plasmodium falciparum blood samples at five dilutions ranging from 20 to 1000 parasites (p)/microlitre (µl) and malaria negative blood samples. The RDTs were performed in a controlled, laboratory setting by a trained operator who visually read the RDT results. A second trained operator then used the MMAs to read the RDT results. Sensitivity (Sn) and specificity (Sp) for the RDTs were calculated in a Bayesian framework using mixed models.Five different MMAs were evaluated on six different RDT products using cultured P. falciparum (Pf) test line, when read by the trained human eye was significantly higher compared to when read by MMAs (74% vs. average 47%) at samples of 20 p/µl. In higher density samples, the Sn was comparable to the human eye (97%) for three MMAs. The RDT Sn of test lines that detect all Plasmodium species (Pan line), when read by the trained human eye was significantly higher compared to when read by MMAs (79% vs. average 56%) across all densities. The RDT Sp, when read by the human eye or MMAs was 99% for both the Pf and Pan test lines across all densities.The RDT Sn of the The study results show that in a laboratory setting, most MMAs produced similar results interpreting the Pf test line of RDTs at parasite densities typically found in patients that experience malaria symptoms (> 100 p/µl) compared to the human eye. At low parasite densities for the Pf line and across all parasite densities for the Pan line, MMAs were less accurate than the human eye. Future efforts should focus on improving the band/line detection at lower band intensities and evaluating additional MMA functionalities like the ability to identify and classify RDT errors or anomalies. Plasmodium falciparum or Plasmodium vivax), or multiple (Pan) species . Malaria RDTs detecting both a P. falciparum and a pan-antigen are commonly called combination (‘combo’) RDTs. The World Health Organization (WHO) recommends use of quality-assured RDTs (or microscopy) in all patients suspected of malaria [Malaria rapid diagnostics tests (RDT) are lateral flow assays (LFAs) that detect malaria specific antigens produced by the parasites. Malaria RDTs can detect either a single species . Malaria RDTs are used at all levels of the healthcare system including community health worker networks . Studiesand take over and/or support the diagnosis function. Malaria RDT MMAs claim to accurately interpret RDT results, while microscopy MMAs claim to accurately interpret blood slides  [Mobile medical applications (MMAs) can transform a mobile platform  into a medical device. Some MMAs can transform a smart phone into a medical device roscope) , 15. MMAroscope) –20. MMAsThe use of RDT MMAs is not yet widespread. At the time of writing this publication, their use has been limited to research and evaluation –25, and The MMA evaluation was conducted at the Malaria Branch Laboratory, the Centers for Disease Control and Prevention (CDC), in Atlanta, USA, between October 2016 and February 2017. Two full-time and one-part time staff (hereby referred to as operators) carried out the evaluation. Two operators were trained on RDT performance and result interpretation by CDC staff directly working on the RDT evaluation programme  prior toPlasmodium lactate dehydrogenase (LDH) dual test line (‘combo’) RDTs were used. The ability of MMAs and human eye to interpret both the Pf and Pan test line results were evaluated using cultured P. falciparum strain 3D7 blood samples at dilutions at 20, 100, 200, 500 and a 1000 parasites (p)/µl and malaria negative blood samples. Before dilution to the target parasitaemias, the cultures were synchronized at the young trophozoïte stage with a standard protocol based on sorbitol treatment. After resuspension in a 40% haematocrit mixture of O + blood cells and AB + plasma, the parasite density was determined by two independent microscopists, based on a red cell count. Dilutions of 20, 100, 200, 500, 1000 parasites/µl were then prepared using venous donor blood uninfected by Plasmodium parasites (screened by microscopy and RDTs). Parasite negative whole blood samples were obtained from informed and consented volunteer donors from accredited blood banks . Blood donors were tested for malaria  and viral infections . Six different RDT products from three RDT manufacturers were included that consistently met or exceeded WHO recommended procurement criteria and account for the largest share of the global RDT market  Table 11. Both PAlthough most clinical malaria infections manifest themselves with much higher parasite loads, a sample of 20 p/µl to induce low band intensity reactivity was included to see how the human eye and the readers would compare when reading such low band intensities. Over sixteen hundred RDTs (1625) of each product were shipped from the manufacturers’ site to the US Centers for Disease Control via airfreight at ambient temperature and without temperature monitoring. Four RDTs of each RDT product were then shipped to the MMA manufacturers for calibration purposes via ground/air freight under the conditions described previously. No specific instructions were provided to the manufacturers on what or how to perform the calibration.MMAs were identified and selected based on snowball sampling. RDT MMAs were included if they met the following specifications: (a) produce qualitative test results ; (b) are small and light enough to be portable/handheld; (c) have a battery power option available; (d) come in dust-free packing; (e) require limited or no additional equipment; (f) require minimal training so that lay operators can be trained within 2 hours and (g) cost less than USD $3,000. Potential RDT MMA technologies were excluded when MMAs (a) required a cold chain and (b) were not available within the timeline set to perform the study. MMAs were shipped to CDC in Atlanta via routine mail delivery using FedEx. Table P. falciparum or negative) and parasite density. Operator 2 was blinded to the RDT result noted by operator 1 and vice versa. Samples used on any particular day were not re-used.To achieve the first objective, operator 1 performed the RDT according to manufacturers’ instructions, read and recorded the result as positive, negative, invalid, and scored the RDT control and result line(s) intensity. Intensities were scored using a scale from 0 to 4 using the band intensity template from the WHO malaria RDT Product Testing as a guide  For the second objective, repeatability was calculated. Ten RDTs of each of the six RDT types were performed using negative samples and samples with each of the five parasite densities. The MMAs then read each of the RDT type/sample combinations multiple (ten) times. Repeatability for each of the RDT type/sample combinations was defined as the number of true positive or negative results following the first true positive or true negative result out of the total of ten read outs performed.For the third objective, seven different types of RDT protocol errors were intentionally made while performing the RDT. Errors were induced by adding (1) excess, (2) insufficient, or (3) no blood to the sample well; adding (4) excess, (5) insufficient, or (6) no buffer to the sample well; and finally (7) dropping buffer or blood in the incorrect sample well. Samples containing 1000 parasites/µL were used for this purpose. The seven different errors were performed once on each of the six different RDT products. MMAs were assessed, for correctly categorizing and identifying the results as errors, compared to the human eye. Human operators were trained on identification of errors and had a pictorial reference of different errors at their disposal.Finally, an overview of the product characteristics of MMAs was provided by noting down observations about the use of the MMAs during and after the course of the evaluation and by reviewing MMA user manuals shared by the designers.The required sample size was calculated using a non-inferiority study protocol with 80% power, 5% significance and the joint hypotheses that the RDT Sn when read by the MMA is not decreased by more than 90% compared to when read by the human eye, and the false positive fraction (1-specificity) is not more than twice that of the human eye. It was assumed that the performance of the RDTs varies by parasite concentration in the sample. It was assumed that samples at 1000, 500, and 200 parasites/µl would have 95% Sn when read by the human eye , while samples at 100 and 20 parasites/µl would have a RDT Sn of 90% when read by the human eye. RDT Sp when read by the human eye was assumed to be 95%. Based on these assumptions, it was estimated that a total of 1610 samples were required for each RDT, with a target profile of 49.7% negative samples, and 18.6%, 13.0%, 6.2%, 6.2%, and 6.2% of samples at 20, 100, 200, 500, and 1000 parasites/µl, respectively. Data was recorded on paper data collection sheets and later entered in MS Excel spreadsheets . Double data entry was performed. Data analysis was performed using R (version 3.4.1).P. falciparum result for a Pf/Pan combo test includes either or both the Pf and Pan test line to be positive). A Bayesian framework was used to estimate Sn/Sp and agreement (Cohen’s kappa), and to provide 95% credible intervals (CI). The probability of the sample to be truly positive was derived and used to compute sensitivity and specificity with a mixed model that accounts for fixed effects, i.e. concentration & readers (including human eye) and random effects, i.e. RDT types, using a logistic regression.The primary analysis was conducted using sample reactivity (positivity) as the reference method to determine diagnostic performance characteristics  of either the Pf or Pan test line interpretation, by the human eye and the MMA. Diagnostic performance characteristics  of both Pf and Pan test lines were also analysed for the two ‘combo’ RDTs included in the evaluation  line across all Pf only and Pf/Pan RDTs, when read by the human eye ranged from 77%  at 20 p/µl to 99% (95% CI 99–100%) at 1000 p/µl. Malaria RDT Sn when read by the best performing MMA (MMA4) ranged from 47% (95% CI 31–64%) at 20 p/µl to 99% (95% CI 98–99%) at 1000 p/µl. Malaria RDT Sn when read by other MMAs ranged from 4%  at a density of 20 p/µl to 98% (95% CI 97–99%) at a density of 1000 p/µl. Malaria RDT Sp when read by the human eye was 99% (95% CI 98–100%). Malaria RDT Sp read by the MMAs ranged from 86% (95% CI 70–94%) for MMA1 to 99% (95% CI 97–100%) for MMA2 and MMA4  at 20 p/µl to 99% (95% CI 100–100%) at 500 p/µl. The Sn when read by the best performing MMAs  ranged from 26% (95% CI 19–71%) at a density of 20 p/µl to 100% (95% CI 93–100%) at a density of 500 p/µl. Malaria RDT Sn when read by other MMAs ranged from 1% (95% CI 1–10%) at a density of 20 p/µl to 99% (95% CI 94–100%) at a density of 500 p/µl. , the agreement (median kappa) of the MMAs compared to the human eye ranged from 0.46 (95% CI 0.44–0.47) for MMA2 to 0.85 (95% CI 0.84–0.86) for MMA4 across all sample densities. In other words, MMA2 showed moderate agreement and MMA4 nearly complete agreement with the human eye . For theThe test line intensity (as rated by the human eye using the band intensity template)) was found to have increased with the sample concentration. Overall, the average Pf line intensity across RDT types was higher than the average Pan line intensity for identical sample concentrations: For Pf and Pan lines, with negative samples, the average line intensity was 0. For the Pf line, the average line intensity was 1 with samples at 20 p/µl, increasing to an average intensity of 4 at 1000 p/µl. For the pan line, lower average line intensities were observed; with a maximum average line intensity of 2 observed at 1000 p/µl. Figure Repeatability of MMAs reading the Pf line varied between 0%–100% depending on RDT type/sample combinations. In negative samples, repeatability was 100% or close to 100% for all MMAs evaluated . Repeatability at 20 p/µl was 0% for all MMA types across all RDT types , except for MM3 (100% for the First Response Pf RDT) and MMA1 . Repeatability of all MMAs increased at higher sample densities irrespective of RDT type: at a density of 200 p/µl and above repeatability was 90% or 100% for all MMAs evaluated.Repeatability of MMAs reading the pan line of the two combo RDTs , was 0% across all sample densities, except for MMA1. For MMA1, repeatability ranged from 0% in a density of 200 p/µl for the Carestart Pf/Pan RDT to 100% across densities for the SD Bioline Pf/Pan RDT. Repeatability of MMA2, MMA3 and MMA5 for the Pan only RDT (Carestart Pan RDT) ranged from 0% for samples at 20 p/µl to 100% at the higher dilutions. Repeatability scores for MMA1 ranged from 10% at 100 p/µl to 100% at 500 p/µl Fig. .Fig. 4Reinvalid or no control line detected when anomalies were observed. In adding too much (error #1) or too little blood (error #2) of a sample of 1000 p/µl to the RDTs, neither the MMAs nor the human eye detected any errors, but generally interpreted the line as positive. Blood of a positive P. falciparum sample (1000 p/µl) was added in the buffer well instead of the blood well and vice versa (error #3 and #4). In both instances, again, neither the MMAs nor the human eye were able to detect the errors and interpret the line as positive. Similar results were obtained when too little buffer was applied (error #5). However, when excess buffer (error #6) or no buffer was used (error #7), all MMAs interpreted the result as negative, rather than showing an error classification  like the human eye did. Figure The ability of MMAs to detect and categorize RDTs that were performed with errors was also measured. All MMAs had a classification system for errors that classified results as either d error #. HoweverTable To evaluate the performance of commercially available MMAs, an evaluation was conducted by comparing RDT results read by MMAs to RDT results read by the human eye. At parasite densities of 100 p/µl and higher, the RDT Sn when read by both the human eye and MMAs was high (close to 100%) for the Pf line, with the exception of MMA1 (Sn of 72%) and MMA2 (Sn of 37%). These two MMAs were the only ‘open’ MMAs included in the evaluation where the amount of light falling on the RDT is not controlled. At a lower parasite density (20 p/µl), the RDT Sn was low but the value was higher when read by the human eye compared to when read by the MMAs. Specificity was close to 100% for RDTs, irrespective of whether it was read by the human eye or MMA, except for MMA1 (82%). In identifying the Pan line, the RDT Sn using both the human eye and the MMAs was generally poor, with lower Sn when read by the MMA.The MMAs scored poorly on repeatability at low densities for the Pf line and across all densities on the pan line, but 100% at high densities for the Pf line  and with negative samples. Kappa agreement between human eye and MMAs varied across all samples ranging from 0.459 for MMA2 (low) to 0.850 (high) for MMA4. Further analysis showed that agreement and repeatability results were lower across all MMAs for low density examples (20 p/µl).The ability of MMAs to read and identify RDTs conducted with operating errors was also assessed resulting in a suboptimal presentation of the test strip. Anomalies like incomplete clearing or red background frequently occur. The Round 8 WHO malaria RDT product evaluation found at least one anomaly in over half (19/35) of all RDT products evaluated . The evaOperators also provided an overview of the product characteristics of the MMAs. The operators found the MMAs easy to use and experienced few problems. During the evaluation, there were some instances of hardware and software challenges, which were resolved in a couple of days, due to ease of communication and relative proximity to the service centers of the designers, as the study took place in a reference lab and not in a field setting. However, resolving technical issues may be more difficult in field settings where international communication is limited or there are differences in time zones.The results from this study demonstrate that the ability of MMAs to correctly identify positive samples is closely correlated with observed line intensity: the lower the test line intensity (as scored by the human eye), the lower the level of accuracy of the MMAs or agreement with the human eye. The relatively poor performance of the MMAs in recognizing low intensity lines could be secondary to a number of factors. A lack of calibration of MMAs at various line intensities or setting the intensity cut off point for positive tests too high (by design) could have caused the MMAs to report false negatives. Other reasons that may have affected the result interpretation include the amount of light falling on the RDT cassette ; or RDT surface properties (matte vs. shiny finish) that may affect the quality of images captured.falciparum malaria read by the human eye and the Deki reader to that of a reference method . In Tanzania, the human eye (trained lay workers) and the Deki reader achieved a similar Sn of around 94% in interpreting 1293 RDT results compared to microscopy. At parasite levels below 200 p/µl, both the human eye and the Deki reader achieved a lower but similar Sn of 69% [Results from our evaluation are similar to those found in other recent evaluations of the Deki reader, MMA4 in this study , 31, 32.n of 69% . In UganThis study suggests that the potential benefit of using MMAs in malaria case management may not be in improving overall accuracy in diagnosing malaria but rather in collecting images and other test information that may help in identifying and classifying RDT anomalies or errors in specific cases that may otherwise go unnoticed in resource constrained settings where post-market surveillance for RDTs as well as general quality assurance occurs infrequently or not at all. MMAs could capture, monitor, and report this information in real time, allowing for immediate follow up if required, serving as a quality assurance tool. However, this benefit should be weight against the lower sensitivity of RDTs when read by MMAs at lower parasite densities, or on pan-only RDTs.P. falciparum samples were used, rather than clinical P. falciparum and P. vivax samples to evaluate the Pf and Pan lines. However, because RDTs are designed to detect P. falciparum with either HRP2 or pLDH the results are not believed to be influenced to a great extent by the choice of culture-derived samples. Second, the evaluation was limited to the result interpretation capability and did not evaluate any other functionalities that MMAs may have had (or could be programmed to have). The study only used 93.4–97.5% of the target number of samples per RDT, although the profile of the samples was maintained. The higher than assumed Sp for the human eye reduces the power to detect significant differences in Sp. The higher than assumed Sn of the human eye for samples with ≥ 100 p/µl compensates for the lower number of samples to maintain statistical power at higher concentrations, while the lower than assumed Sn for 20 p/µl and actual samples size means decreased Sn among the MMAs of more than 85% compared to the human eye should be detected with 80% power at 5% significance.The evaluation had a number of limitations. First, only cultured P. falciparum densities typically expected with clinical malaria (i.e. above 100p/µl), some MMAs can perform as well as the trained human eye in detecting the Pf line of an RDT. At low band intensities, and with non- falciparum malaria, the trained human eye outperforms the results interpretation ability of MMAs. Future development and research efforts should focus on improving the band/line detection for low band intensities and conducting field evaluations that include other MMA functionalities, including error classifications, to inform whether or not MMAs can serve as a quality assurance tool in malaria case management.The results of the study show that at