In this study, the friction and wear characteristics of graphene coatings were evaluated using lubricants with various ratios of graphene ink to deionized (DI) water. When dry graphene ink and pure DI water were used as lubricants, the graphene coating initially peeled off, and the friction coefficient rapidly increased to a large value. However, when a lubricant with graphene ink added to DI water was used, a lubricating film was formed on the graphene coating and the friction coefficient was reduced significantly. Under dry and pure DI water conditions, severe wear morphologies were formed on the graphene coating surface, whereas in the case of the lubricant with graphene inks added to DI water, insignificant wear morphologies were formed. When the mixing ratio between DI water and graphene ink was 100:10 and 100:5, the friction coefficient and wear rate were the lowest, respectively. As a result of a long-term experiment in which the sliding cycle was performed for up to 100,000 cycles under the same experimental conditions, the lubricant with a 100:10 mixing ratio showed excellent lubrication properties, confirming that the friction coefficient and wear rate were significantly reduced compared to that of the dry or pure DI water lubrication conditions. Lubrication has been used in various fields to improve the durability of mechanical parts by reducing friction at the contact surfaces in their contact sliding motion ,2,3,4. OWater has the advantages of being less expensive than oil lubricants, simple to purify after use, and not polluting the environment. However, because water has a lower viscosity than oil and corrodes metal surfaces, its application to mechanical devices made of metallic materials is limited. To solve this problem, studies on creating a water-based lubricant by adding nanoparticles to water, or applying a wear-resistant/corrosion-resistant coating on the surface of the metallic part, have been performed ,11,12. IThe possibility of clean lubrication by forming a graphene coating on a metallic material and using only water as a lubricant to improve the friction and wear properties has been suggested . HoweverIn this study, a corrosion-resistant coating was applied to the metal surface, and a lubricant in which nanoparticles of the same material as the coating were dispersed in water was simultaneously used to improve the water lubrication properties. Graphene, a low-friction/wear-resistant material, was used both as the lubricant additive and the coating material. By forming a graphene coating on the metal surface and using a lubricant in which graphene, which is the same component as the coating, is dispersed in water, a lubricating film was smoothly formed by the affinity between the graphene coating and the graphene particles to improve the water lubrication properties.The graphene solution was prepared by mixing graphene ink  with 1.5 wt% of graphene particles and deionized (DI) water in a 1:1 weight ratio and then uniformly dispersed through ultrasonication. A uniformly mixed graphene solution was coated to a thickness of approximately 2 μm using a spray device on the surface of a stainless steel substrate with dimensions of 35 mm × 35 mm and a thickness of 1 mm and heated to 200 °C. It was heated on a hot plate at 200 °C for approximately 30 min so that the graphene coating was firmly adhered on the substrate surface. Lubricants were prepared with various mixing ratios of DI water and graphene ink. The structure and defects of the graphene coating were analyzed using X-ray diffraction  and Raman spectroscopy . XRD analyzes the crystallinity and microstructure using the principle that diffraction angles differ according to the crystallinity. The measurement range of the diffraction angle was 5–50°, and the peak value was obtained according to the diffraction angle. Raman spectroscopy is an analytical method used to obtain the molecular structure of a substance by measuring the energy is lost through phonons emitted during light scattering that occurs when matter is irradiated by light. Raman analysis was performed after observing the nanoparticles on the substrate under a microscope. The laser wavelength of the Raman device was 532 nm, and the spectrum was measured in the range of 500–4000 nm with a power of 6 mW.A tribotester with a reciprocating sliding motion  was used to evaluate the friction and wear characteristics of the graphene coating according to the amount of graphene ink added to the lubricant. The tribotester converts the rotational motion of the motor into linear motion through a cam connected to the shaft to enable repeated sliding motion. The tribo-test conditions are listed in 2 bonding, so the Raman spectrum obtained is similar. Three distinct peaks were observed near 1350 cm−1, 1585 cm−1, and 2700 cm−1 corresponding to the D and G bands. The peak near 1585 cm−1 is the G band owing to sp2 bonding, and the peak near 2700 cm−1 is the 2D band owing to multiple scattering  is the wear volume (W.V.) [mm3] divided by the normal load (L) [N] and total sliding distance (D) [mm]. The wear rate of each lubricant is shown in −8 mm3/N·mm, and the wear rate in the lubricated condition was calculated as 9.47 × 10−8–4.1 × 10−8 mm3/N·mm. When pure DI water was used as a lubricant, the wear rate was the highest at 9.47 × 10−8 mm3/N·mm. Since water dispersion graphene exhibits a property of being well dispersed in water, we found that the graphene particles are easily peeled from the graphene coating due to their hydrophilicity under DI water lubrication conditions. Although the friction coefficient is reduced because the exfoliated graphene particles form a new lubricating film, it is considered that the wear rate is high because some of the particles already separated from the coating layer function as a lubricating film and the remaining particles are accumulated by repeated sliding motion. When the DI water lubricant mixed with graphene ink is applied, it seems that the friction coefficient and wear rate are reduced because the graphene particles present in the lubricant form a lubricating film on the surface to some extent from the beginning of the sliding motion. When a mixed lubricant with a 100:5 ratio was used, the wear rate was 4.1 × 10−8 mm3/N·mm, which decreased by 28% and 57% compared to that of the dry and DI water lubrication conditions, respectively, and it was the lowest. The wear rate when using a mixed lubricant with a 100:20 ratio, which had the highest mixing ratio of graphene ink, was higher than that in the case of dry lubrication. In the case of a lubricant in which more than a certain amount of graphene particles is mixed, the graphene particles tend to agglomerate and function as abrasive particles, which intensifies wear.The wear rate [mmAn additional friction test was performed for 100,000 cycles to evaluate the persistence of the lubricating properties for the mixed lubricant with a ratio of 100:10 with the lowest friction coefficient. Dry and DI water lubrication conditions were set as a comparison group, and friction tests were conducted under the same conditions for a long time, and the friction coefficients according to each lubrication condition were compared, as shown in −8 mm3/N·mm, which was more than double than that in the case of 10,000 cycles under the same conditions. This indicates that a larger amount of wear was generated by the long sliding motion, but the rate at which the amount of wear increased decreased as the number of sliding cycles increased. That is, the increase in the wear rate is reduced, so that the wear rate during the entire sliding cycle is greatly reduced. It is thought that this is because the graphene coating is worn and the wear rate increases as the graphene particles are separated from the coating surface, and the particles form a lubricating film on the surface and prevent the expansion of wear to some extent. It is expected that the process of forming the lubricating film again after the newly formed lubricating film breaks again and falls out as wear particles are repeated. However, when sliding motion was performed for a long time under pure DI water lubrication conditions, the wear rate showed the highest value of 3.49 × 10−8 mm3/N·mm, and it decreased by 2.7 times compared to that of the sliding motion of 10,000 cycles. The increase in the rate of wear decreased with an increase in the sliding cycles. This is because the graphene particles of the coating fell off after pure DI water was applied as a lubricant, causing continuous wear; however, the graphene particles that fell off were again partially formed into a lubricating film. Even in this case, the formation and destruction of the graphene lubricating film may have occurred repeatedly. In contrast, for the lubricant mixed at a ratio of 100:10, the wear rate for long-time sliding motion was 1.11 × 10−8 mm3/N·mm, showing the lowest value, and it was reduced by nearly five times compared with that of the sliding motion of 10,000 cycles. This is because the increase rate of wear owing to the increase in the sliding cycle is the lowest, and it is considered that the graphene particles mixed in the DI water form a lubricating film that protects the surface and reduces friction. The wear rates for the DI water-based graphene lubricant were reduced by approximately 56% and 68%, respectively, compared to those under dry and pure DI water lubrication conditions.As shown in EDS analysis was used to determine the degree of breakage of the worn surface, and the results are shown in In this study, DI water-based graphene lubricants were prepared by adding graphene ink to pure DI water to improve the water-based lubrication properties of the graphene coatings. In the friction test under dry conditions, the friction coefficient was high because wear debris was generated on the graphene-coated surface by repeated sliding motion. When pure DI water was used as a lubricant, the friction coefficient was reduced, but the graphene layers were easily peeled off, and a non-uniform lubricating film was formed, which intensified the wear. In contrast, in the case of the DI water-based graphene lubricants, both the friction coefficient and wear rate were significantly reduced. The friction coefficient and wear rate were the lowest when the mixing ratios were 100:10 and 100:5, respectively. Graphene particles dispersed in DI water formed a lubricating film that exhibits friction-reducing properties and surface protection. Even in the case of long-time sliding motion, only in the case of DI water-based graphene lubricants, the graphene coating was not completely damaged, and the slightest wear occurred. The results of this study will be helpful for studies to improve the water lubrication properties of metal substrates and may suggest the applicability of water-based eco-friendly lubricants by optimizing the amount of graphene added.


In this review, we highlight recent advancements in 3D graphene foam synthesis by template-assisted chemical vapor deposition, as well as their potential energy storage and conversion applications. This method offers good control of the number of graphene layers and porosity, as well as continuous connection of the graphene sheets. The review covers all the substrate types, catalysts, and precursors used to synthesize 3D graphene by the CVD method, as well as their most viable energy-related applications. The theoretical specific surface area (SSA) of graphene is estimated to be about 2630 m2·g−1. Nevertheless, graphene tends to restack and form irreversible aggregates, which leads to a significant decrease in the surface area. To this end, different strategies for assembling 2D graphene in 3D architectures have been investigated, to preserve the large surface area of graphene ..2·g−1) a−3 and excellent rate performance. The reported energy density was 2.65 mWh·cm−3 and the power density was 20.8 W·cm−3, with 94% retention after 10,000 cycles.A symmetric supercapacitor PET/Au/hnp-G/PVA solid gel electrolyte/hnp-G/Au/PET allowed continuous and short electron/ion diffusion pathway in the entire device, yielding a capacitance of 38.2 F·cm−1 at a scan rate of 5 mVs−1, and stable cycling performance after 2000 cycles. The direct synthesis of graphene on the Ni current collector offered an effective electrical contact for fast charge transfer from the active material to the current collector through the graphene network, which covered the Ni foam and held the active material (NiO).In 2011, Cao  fabricat2 on the surface using a hydrothermal method ..77].2. The discharge polarization curves of the co-doped np-graphene-based Zn-air battery revealed a maximum power density of 83.8 mW∙cm−2, which is higher than that of an all-solid-state Zn-air battery based on the Pt/C + IrO2 catalyst (74.5 mW∙cm−2). The co-doped np-graphene-based battery exhibited the highest power density (83.8 mW∙cm−2) with the longest testing period (43 h). Those performances were ascribed to the high bifunctional activity, electronic conductivity, and facilitated mass transport provided by the bicontinuous 3D nanotubular graphene network. This structure also presents the advantage of being highly flexible and, thus, could be easily integrated into flexible devices. The bending of the battery at different angles did not affect its performance.Graphene, N-doped graphene, and co-doped graphene were deposited using CVD from benzene and benzene/pyridine on a nanoporous Ni substrate and tested as the cathode in a Zn–air battery. The synergistic effect of a bicontinuous porous structure and single Ni atomic doping on the nitrogen sites made the co-doped np-graphene-based catalyst achieve ORR and OER performances comparable to commercial Pt/C and IrOElectrical devices that employ the photovoltaic effect to convert solar energy into electricity, such as dye-sensitized solar cells (DSSC), are a high priority, with solar energy becoming a major component of sustainable energy. In addition to their low production cost, DSSC/organic photovoltaics (OPVs) possess the advantage of being lightweight, transparent, or semi-transparent; easily assembled; and scaled up on flexible substrates. The architecture of a DSSC comprises a photoanode (working electrode–WE), a dye that absorbs the solar energy (sensitizer), an electrolyte (a mediator comprising the redox couple of the liquid DSSC), and a counter electrode .2 photoanode and Pt counter electrode.A free-standing 3D graphene network was employed as both photoanode (WE) and counter electrode (CE) in dye-sensitized solar cells (DSSC), achieving similar, or even higher, performances to a standard DSSC containing TiOThe performance of the main 3D graphene-based DSSCs employing Ru-based light-sensitizing materials, and iodide/triiodide redox couple electrolyte, recorded under standard conditions, is summarized in 3/HCl is attached to the FTO substrate. A schematic flow diagram of graphene-based CE fabrication and integration into a liquid-state DSSC is presented in In a general approach, in order to be integrated as a CE in the DSSC, the free-standing GF prepared by the conventional CVD method on the Ni foam template with the subsequent nickel etching in the presence of FeClSC  hybrids , the eff2 photoanode in a DSSC -phenyl C71 butyric acid methyl ester (PCBM)-poly(3-hexylthiophene) (P3HT) bulk heterojunction (BHJ) layer; leading to a device with a total thickness of less than 250 nm. a,b.−2 and a bias of −1V. The responsivity achieved values of 5.8 × 105 AW−1 under 520 nm illumination at 0.4 nW·cm−2 irradiance and a bias voltage of −1 V, yielding a photocurrent gain as high as 1.4 × 106. The extended responsivity to the NIR region, mainly ascribed to the graphene sponge absorption, revealed a stable and reliable photoelectric response. Thus, under 1000 nm laser irradiation the device’s responsivity was 0.6 A·W−1, with an incident irradiance of 2.4 mW·cm−2; reaching a responsivity of 108 A·W−1 for an incident power of 4 µW·cm−2.The hybrid 3DG/organic photoconductor exhibited the typical photoconductive characteristics, providing high photocurrent gains and responsivity, due to the efficient electron trapping sites present in the PCBM and the functional groups in 3DG. As shown in In summary, the exploration of template-assisted CVD-grown graphene in energy storage and conversion has been highlighted. Although the CVD synthesis of graphene foams seems to have reached its maturity, with the 3D graphene networks being deposited on either metallic or non-metallic substrates from various carbon precursors, the precise control of pore size and porosity is a big challenge and requires attention, as there is a direct relationship between the properties of the graphene-based electrodes and their microstructure. According to the reported studies, most of the 3D graphene electrodes feature randomly distributed pores, with sizes between tens and hundreds of nanometers. The properties of 3D graphene networks, especially the high conductivity and specific surface area confer graphene significant potential to function as a binder-free current collector electrode in batteries and supercapacitors. To extend their functionality, considerable efforts have been dedicated to the development of 3D graphene-based hybrids with high active material loading. The process of GF decoration with different active materials allowed, not only the control of mass loading, but also a well-defined morphology of the active materials. Among the tested active materials were metal oxide, sulfides, silicon, and hydroxides of different shapes, from nanoparticles to nanowires, and nanosheets.−1, lead-acid 50–90 W h L−1 and Li-ion batteries 250–850 Wh L−1). There has also been significant progress in their cyclability, which has reached the lower limit of commercial standards for LIBs, between 400 and 1200 cycles, or supercapacitors, which should be not less than 10,000 cycles. For instance, the cyclability of most of the reported supercapacitors is in the range of 500 to 5000 cycles, with a few examples of 10,000 cycles 2/G-NG/Ni(OH)2). On the other hand, the cycling ability of the reported LIBs was in the range of 300 to 500 cycles. Although the specific capacitance and energy density, the rate capability, and cycling life of the hybrid electrodes were superior to those of the active materials alone, they were not at the level of commercially available energy storage devices (supercapacitors 5–8 W h L2 photoanode and Pt counter electrode.With regard to solar cells and photodetection applications, they are at an incipient stage, with the best performances approaching those of standard DSSC containing a TiO


Contact resistance between electrically connected parts of electronic elements can negatively affect their resulting properties and parameters. The contact resistance is influenced by the physicochemical properties of the connected elements and, in most cases, the lowest possible value is required. The issue of contact resistance is also addressed in connection with the increasingly frequently used carbon allotropes. This work aimed to determine the factors that influence contact resistance between graphene prepared by chemical vapour deposition and pre-patterned Cu and Au electrodes onto which graphene is subsequently transferred. It was found that electrode surface treatment methods affect the resistance between Cu and graphene, where contact resistance varied greatly, with an average of 1.25 ± 1.54 kΩ, whereas for the Au electrodes, the deposition techniques did not influence the resulting contact resistance, which decreased by almost two orders of magnitude compared with the Cu electrodes, to 0.03 ± 0.01 kΩ. RC) between the metallic contact and the graphene can substantially affect the performance of the transistor realised in this way [RC significantly affects the high-frequency characteristics of the transistors [RC, the sensor’s sensitivity is lost. The arrangement of these sensors is usually reversed compared with that of the GFET. The electrode structures are first created using photolithography, and then the active layer of graphene is applied on top. Graphene-sensitive layers are used to realise sensors intended for a wide range of applications, such as sensors for monitoring environmental parameters and biosensors [Carbon allotropes, such as graphene or carbon nanotubes, are advantageously employed in many applications , such asthis way . It was nsistors . A numbensistors , mild oxnsistors , selectinsistors , or postnsistors . Howeverosensors ,13,14,15osensors , where tosensors  and temposensors . The higosensors  or epitaosensors , but theosensors , which aosensors . As an iosensors . The CVDosensors .RC values below 100 Ωµm [The transfer length method (TLM) in various modifications is the most frequently used technique to evaluate the contact resistance between graphene and the electrode structure ,9,24. ThThe aim of this work was thus to verify whether it was possible to achieve reasonably similar values of contact resistance for copper as for gold electrodes when pre-patterned before the deposition of single-layer (1L) CVD graphene. We also investigated how various surface treatment protocols of such an oxidising electrode system would affect the contact resistance and, finally, whether the contact resistance between gold and graphene could be further improved by its structural modifications, as suggested previously for better-defined combinations of higher quality graphene and metal electrodes deposited on top ,9,10.An electrode structure was designed for the experimental part, where the gaps between individual electrodes were set to 25, 40, 60, 80, 100, 200, and 300 µm .2 layer. The sputtering deposition machine Balzers BAS 450 PM  was used to deposit the Cu or Au/NiCr conductive layer. The thickness of the NiCr layer was 80 nm and the Au or Cu layer thickness was 150 nm. The exposition device Karl Suss MA 56  was used for the photolithography process, where the deposition of the resist ma-P1225 by Microresist Technology  was carried out by spin coating. The thickness of the coated resist was 2 µm. As a carrier substrate, the 3-inch boron-doped silicon wafer from Nanografi  was used, with a thickness of 380 µm, a resistivity of 1–10 Ωcm, and a SiO2 thickness of 300 nm. At the end of the electrode structure realisation, the wet etching process was used for 30 s in the solution of HNO3, Ce(SO4)2, and H2O. The actual gaps between individual electrodes were measured using an Olympus confocal scanning microscope, LEXT OLS5000 . The measurement of the gap between the electrodes was carried out at three different places for a given gap, and the arithmetic mean was used for the precise evaluation of RC. The standard deviation of the measurement was ±0.96 µm for a gap of 25 µm and ±1.0 µm for a gap of 300 µm, which shows a negligible influence of the gap dimension heterogeneity on the evaluation of the resistance measurement results.The metal electrode structure based on a Cu or Au/NiCr layer was realised using photolithography on silicon carrier substrates (Si wafers) with a SiO3 , or electrochemical polishing using H3PO4 . The mechanical cleaning (MC) of the electrodes consisted of fine grinding and polishing with the use of a rubber made from synthetic polymers (bis(2-ethylhexyl)hexahydrophthalate), during which oxides were removed from the surface of the electrodes. Chemical cleaning (CC) was performed using nitric acid in three different concentrations . Electrochemical polishing (ECP) of the electrode structure was carried out in a phosphoric acid solution (50 wt.%) upon applying 1 V vs. Ag/AgCl for 5 or 8 s [Before the actual deposition of the graphene, various methods of Cu electrode surface treatment were applied . This in5 or 8 s . The ele2 atmosphere and annealed for 40 min. Afterward, the foil was exposed to a mixture of CH4 (1 sccm) and H2 (50 sccm) for 30 min. Additional annealing in H2 for 5 min was performed after exposure to the methane/hydrogen mixture. Total pressure during the entire CVD process was maintained at 350 mTorr. Finally, the substrate was cooled down under H2. The 1L graphene was transferred onto electrode structures using the nitrocellulose-assisted method [2  using an in-built flow controller, after which the pressure was stabilised and the plasma was turned on at 35 W or 52.5 W RF generator for a specific treatment time of 10, 12, or 20 s . Ions extracted from an ion source with a 20 kV voltage were analysed with a 90° magnet and accelerated with an 80 kV voltage. The samples were kept at room temperature during irradiation and the beam current was 50 nAcm−2. The beam current and total fluence were measured using a shoot-through Faraday cup with an aperture of 1.4 cm in diameter and a Faraday cup area of 1.5 cm2. For electron suppression, the Faraday cup had suppression electrodes at −500 V in front of and behind the Faraday cup. The defects in Ar+-bombarded samples were created only within the graphene in contact with the Au electrodes using direct-write photolithography patterning  with an AZ® ECI 3007 chemical resist  which, after illumination, was developed with an AZ® Developer .Graphene was grown by CVD in accordance with a previously established protocol ,27,28. Bd method . Lists o20 s see . They wecm−2 see . The samRT) is a combination of the contact resistance of graphene with the two neighbouring contacts (RC), the resistance of the graphene layer (RG) between the contacts, and the intrinsic resistance of the metallic layer (Rm):The TLM was used to determine the contact resistance values between graphene and metal electrodes. This technique consists of creating metal electrodes with different gaps on which the graphene layer is deposited. The resistance value is then measured between the individual contacts. The measured total resistance value . From the slope of the linear fit, the sheet resistance (RS) of graphene can be obtained, because RG = RS(L/W), where L is the distance between the electrodes and W is the contact width. However, in this work, RS was determined from the particular slope of measurement between contacts spaced at 100 and 200 µm, where the total resistance is dominated by the RG and less so by the RC.Because RC values. The measurement of contact resistance was carried out in DC mode concerning the TLM evaluation. Contact with individual samples was made using a homemade contact needle array prepared for the used electrode structure, for which the spring probes S1 Series from Smiths Interconnect  were used. Samples 14 to 16 were measured in darkness using a Keithley 4200A-SCS parameter analyser  in a two-point arrangement with gold probes , and RT values were extracted from I-V characteristics. The electrical resistance was gradually measured between the individual electrodes . The effect of light on the contact resistance values for samples that were measured under laboratory lighting but kept in the dark, and for samples measured directly in the dark, was not observed. The measured values of the electrical resistance were plotted in a graph as a function of the gap between the individual electrodes.After the graphene was deposited on the electrodes, the resistance between the individual electrodes was gradually measured. The measurement of electrical resistance was carried out at room temperature and pressure, under standard illumination, using the four-point test method via the multimeter system Keithley DAQ6510  to eliminate the possible influence of supply wire resistance. The measurement accuracy of the resistance for the Keithley equipment was 0.01% of the reading and 0.001% of the range. This high measurement accuracy cannot cause a significant deviation in the measured total resistance values and, therefore, neither in the resulting −1. The spectrometer was calibrated using the F1g mode of Si at 520.2 cm−1. The defect density, nD, and the average distance between defects, LD, in the 1L graphene were calculated using Equations (2) and (3) by using the intensity (amplitude) ratio of the D and G Raman bands (ID/IG) and the laser excitation wavelength (Lλ) [Raman spectra of the transferred graphene (both pristine and with post-transfer defects) were measured using either a 532 nm laser excitation wavelength  or a 633 nm laser excitation wavelength  under a 100× objective (0.85 N.A.). The laser power was 1 mW under the objective. A grating of 600 lines/mm was used, giving a pixel-to-pixel resolution of ~1.8 cmy-axis determines twice the RC value between the electrode structure and the graphene layer.The core result of all individual experiments is the dependence of the resistance between every two neighbouring electrodes on the distance between these electrodes. The measured values for each sample were subsequently extrapolated by a least-squares linear fit, where the intersection with the In a copper electrode system, copper oxidation has a significant effect on contact resistance. For this reason, it is necessary to remove the oxide layer before graphene deposition. Samples with an applied graphene layer without any surface treatment of the copper electrodes were not realised and measured because the oxidised surface of the copper electrodes will significantly increase the contact resistance, thereby preventing any meaningful electrical measurement. Different methods of cleaning the copper electrode structures were compared to evaluate the efficacy of such treatments on the contact resistance between the copper electrodes and the graphene layer that was transferred on top of them immediately after the cleaning procedure. RC values for different methods of the surface treatment of copper electrodes. The lowest RC values were achieved for the MC and CC, reaching hundreds of ohms on average. For the ECP, the contact resistance was the highest, in the order of kΩ units. These higher RC values are probably due to the residual oxide layer that was not removed during this surface treatment. As can be seen in RC value was achieved for the longest electropolishing time—although it was still higher than for most of the other treatments. Hence, in contrast to the other treatment types, it would be necessary to set the conditions of the cleaning process according to the thickness of the oxide, making this method generally less viable for standardised utilisation. Concerning the other methods, the RC values of hundreds of ohms indicate that, despite the removal of most of the copper oxide, no good electrical contact with graphene was realised. Such high RC values are probably caused by a thin and heterogeneous layer of copper oxide forming instantaneously on the cleaned electrodes during the graphene transfer procedure. Furthermore, the oxide layer can grow even under the graphene layer due to the defects present in its structure, and the surface of the electrodes is thus still partially accessible to air. The air can also penetrate under the graphene layer from its edges. As shown previously, the graphene layer on the copper electrodes worsens the oxidation [RC values during repeated measurements after 14 days of aging when the samples were left in an ambient environment . xidation , therebyRC values between these two groups  of the graphene layer was also determined for the samples. As expected [RS can be observed for the plasma-treated samples. In conjunction with the Raman spectroscopy measurements, shown in −1 increases with the increasing treatment times and powers for the O2 plasma and with the total dose for Ar+ ion bombardment. At the same time, the intensity of the 2D band (at ~2700 cm−1) decreases, also corresponding with an increasing lattice disorder [ID/IG), and the ranges of defect densities (nD) and distances (LD) calculated using Equations (2) and (3), are summarised in ID/IG varies from 0.11 to 3.94, with the corresponding nD from the lower limit to the upper limit. We note that due to different laser excitation wavelengths used for the Raman measurement, only the ranges of nD and LD values should be taken as quantitative, not the average value, due to the approximation in Equations (2) and (3) [−1. The intensity ratio of the D and D’ bands (ID/ID’) is related to the defect type [2 plasma and 14 for the Ar+ bombardment), the ID/ID’ ratios are ~20.6 and 5.5, respectively. These ratios indicate that most of the defects are sp3-like for the plasma-treated samples and vacancy- or boundary-like for Ar+ irradiated samples. Hence, regardless of the majority defect type, the contact resistance is still dominated by the graphene deposition on top of the electrodes. A visual comparison of the effect of the defect density is shown in RC is plotted against nD. The squares with colours corresponding to the spectra in RC values for the non-modified samples, confirming that in our graphene–electrode system, defects with areal density up to ~6 × 1011 cm−2 do not influence contact resistance more than the differences caused by imperfect control of the graphene transfer process. We also note that regardless of the defect presence, the RC values are stable over time, in contrast to the case of Cu electrodes  . Additioect type ,41. In oodes cf. .RC reached an average value of 1.25 ± 1.54 kΩ for Cu and only 0.03 ± 0.01 kΩ for Au, with the same electrode dimensions. In previous studies, where the metal electrodes were fabricated on top of the graphene samples, several reasons for the differences in RC between graphene and various metals were suggested. The work function difference between graphene and the metal can cause differences in the charge doping of graphene, thereby changing the density of its states and, consequently, sheet resistance [RC with graphene. Instead, they suggested that chemical cleaning and the microstructure of the deposited metal are the key factors influencing contact quality. In our case, however, the large difference between RC values for Cu and Au, the heterogeneity between individual treatment types and samples on Cu electrodes, and the obvious worsening of RC over time for Cu, all point to the major influence of the copper oxide removal and subsequent continuous growth. The Cu electrodes, in spite of the lower cost, are thus not favourable for the system in which they are pre-patterned under graphene.As can be seen from the results presented in the above sections, the contact resistance between CVD graphene and a pre-patterned electrode beneath it is highly dependent on the particular contact metal. The sistance . The worsistance . Therefosistance . In contsistance  reported11 cm−2, regardless of their type and origin. The achieved results can thus be the basis for the design of sensor elements based on graphene structures, as it is clear from these results which methods of modifying the electrode system and methods of graphene transfer have the greatest effect on the contact resistance and, thus, the resulting sensitivity of the sensor. Fabrication of pre-patterned electrodes can also be potentially utilised in other applications, where the graphene’s top surface is sensitive to post-processing, such as when manipulating its electrochemical or optical properties by molecules or nanoparticles.We investigated the quality of electrical contacts between 1L CVD graphene and Au and Cu electrodes pre-patterned on the substrate before the transfer of graphene on top. It is clear from the obtained results that in the case of the graphene layer on the Cu electrode system, the type of cleaning of the electrode structure before the graphene transfer strongly affects the resulting contact resistance. Nevertheless, the lowest achieved contact resistance values were still in the order of hundreds of ohms. Such high contact resistance values can dampen any desired changes in the resistance of the graphene layer when it is utilised as the active layer in chemoresistive sensing applications. In the case of Au electrode systems, contact resistance in the order of tens of ohms was achieved without the need to clean the electrode system surface before the transfer. However, the contact resistance was not further improved in the Au–graphene system by modification of the graphene structure by defects with densities reaching 6 × 10


This study presents an easy and quick method for the synthesis of graphene from graphite in a set of solvents, including n-Hexadecane (n-Hexa), dimethylsulfoxide (DMSO), sodium hydroxide (NaOH), 1-octanol (OCTA), perchloric acid (PA), N,N-Dimethylformamide (DMF), ethylene glycol (EG), and ethylene diamine (ED), via microwave (MW) energy. The properties of final products were determined by X-ray diffraction spectroscopy (XRD), Fourier transform infrared spectroscopy (FTIR), ultraviolet-visible (UV-Vis) spectroscopy, and the four-point probe technique. The XRD spectra of most of the MW-assisted graphene products showed peaks at 2θ = 26.5° and 54°. Layer numbers extend from 2 and 25, and the leading comes about were gotten by having two-layered products, named as graphene synthesized in dimethylsulfoxide (G-DMSO), graphene synthesized in ethylene glycol (G-EG), and graphene synthesized in 1-octanol (G-OCTA). G-DMF has the highest electrical conductivity with 22 S/m. The electrical conductivity is higher when the dipole moment of the used solvent is between 2 and 4 Debye (D). The FTIR spectra of most of the MW-assisted graphene products are in line with commercial graphene (CG). The UV-Vis spectra of all MW-assisted graphene products showed a peak at 223 nm referring to characteristic sp2 C=C bonds and 273 nm relating to the n → π * transition of C-O bonds. Graphene, a monolayer form of graphite, has gathered significant interest in the past few years due to its outstanding properties, such as high electrical conductivity, extraordinary mechanical strength, and large specific surface area . By meanLiquid phase exfoliation (LPE) of graphite presents more economical, easier and a scalable alternative for the graphene production in comparison to Hummers’ method ,16,17. L−2 which is an optimum range for the MW-assisted exfoliation of graphite. Moreover, Pal et al. found that expanded graphite content increased the dielectric properties of the composite with epoxy. The dielectric constant and dielectric loss factor of expanded graphite produced by chemical oxidation were decreased when the microwave treatment was applied to these composites [The solvents having near zero surface tension can penetrate the gap between the graphite layers. This results in an abrupt expansion, generating a force that facilitates exfoliation of graphene in a liquid . Microwamposites . In this study, a simple and rapid method was presented for graphene synthesis and the one-pot synthesis of graphene from graphite through a MW-assisted solvothermal technique was conducted by using various types of low-cost solvents, such as n-Hexadecane (n-Hexa), dimethyl sulfoxide (DMSO), sodium hydroxide (NaOH), 1-octanol (OCTA), perchloric acid (PA), ethylene glycol (EG), and ethylene diamine (ED). Then, the exfoliation capabilities of aforementioned solvents were compared by examining the properties of final products via X-ray diffraction  analysis, the four-point probe technique, Fourier transform infrared spectroscopy , ultraviolet-visible  spectroscopy, Raman spectroscopy , atomic force microscopy  analysis, Brunnauer–Emmett–Teller  analysis, and scanning electron microscopy  analysis.The pristine graphite powder, grade 3061, was purchased from Asbury Graphite Mills, Inc., New Jersey and labelled as commercial graphite (CGr). Commercial graphene (CG) was acquired from Ningbo Yuanshi New Material Technology Co., Ltd., Ningbo, China and used as the reference. N-Hexadecane , dimethyl sulfoxide , sodium hydroxide , 1-octanol , perchloric acid , ethylene glycol , and ethylene diamine  were of analytical grade.®, Berlin, Germany) under 200 W, 35 kHz, mode 5, and 50% power for 10 min. Afterwards, MW irradiation was applied to the reaction mixture in a 80 mL volume of Teflon vent-and-reseal vessel under the conditions of 180 °C, 150 W for 30 min by a multimode Start-S model  MW furnace shown in It is worth to be mentioning that the graphene synthesis protocol that is presented in this study is a totally novel method. Firstly, the graphite was dispersed in various solvents at a concentration of 100 mg/mL. The mixture was then sonicated just prior to MW to prevent agglomeration via HD 2200 SonoPuls Homogenizer  spectroscopy. For UV-vis analysis, the dried samples were dispersed in distilled water by agitating via a magnetic stirrer. Afterwards, some of the dispersion was taken into the 10 × 10 mm vial and spectra were recorded at a range of 200–700 nm. The spectrum has an operation range of 200–700 nm. Brunnauer–Emmett–Teller (BET) surface area values of the products were measured by N2 physisorption at 77K using a Quantachrome Autosorb Automated Gas Sorption System. The samples were degassed under vacuum at 380 °C for 20 h prior to measurement. The thickness of the samples was determined via a Universal Scanning Probe Microscope (USPM) model Atomic Force Microscopy (AFM) that was built in 250× video microscope, easy change cantilever, a 40 µm × 40 µm × 4 µm scan tube assembly and Acoustic/Vibration Isolation Chamber (AVIC). The microstructures of the synthesized graphene were determined using a scanning electron microscope (FEI Philips XL30 SFEG SEM).The graphene samples were characterized by analyzing the thickness, the layer number, and the electrical conductivity of the final products. In order to determine the layer number, X-ray diffraction (XRD) analysis was performed via Rigaku D-Max 2200 Series equipped with Cu-Kα radiation (λ = 1.54 Å) at a scanning rate of 3° per minute. The tube voltage and the current were 40 kV and 40 mA, respectively. The intensity was determined over a 2θ° angular range of 2–90°. Electrical conductivities of synthesized products were measured using a Keithley 2400 Sourcemeter following the manufacturer’s instructions. Firstly, a graphene sample was placed in a cylindrical copper container equipped with a copper cap, thereafter, compressed by a hydraulic press at 50 bars (5 MPa) for 30 min. The electrical resistivities of final products were evaluated by the 4-point probe method. Graphene powder samples were compressed in a copper container with the help of a joiner’s clamp during the electrical conductivity measurements. The conductivity, σ, was then estimated as in Equation (1) shown at below:In this study, graphite was converted to graphene using various solvents with the help of MW energy. The MW-assisted LPE technique was applied to a set of graphite-solvent dispersions in order to exfoliate the graphite to graphene. It is possible to measure the affinity of solvent molecules to graphene layers by physical parameters such as dipole moment (μ), dielectric constant (ε), and surface tension (σ). Moreover, the effect of the surface tension, dipole moment, and dielectric constant of the solvents on layer number and electrical conductivity results were examined in order to increase the product quality. The solvents having larger dipole moment, dielectric constant and surface tensions show better solvent properties than those having poor solvent characteristics for a few exceptions . The cheIn the literature, it is understood that N-methyl-pyrrolidone (NMP) and dimethylformamide (DMF) provide better efficiency in regard to the exfoliation of graphite ,28,29. TThe thickness of graphene products and CGr was calculated by substituting the results of XRD analyses into Scherrer’s equation, which is denoted as Equation (2) shown below: ng angle ,33. The = 26.69° ,34. As s= 26.69° ,22,35,36Moreover, XRD analyses of final products were performed in order to prove the graphene crystal structure. The XRD spectra of the samples were presented in D-spacing values of graphene products were estimated by using Bragg’s equation (Equation (4)), which is shown at below:According to the The relation between the surface tension and layer number is presented in The relation between layer numbers and dielectric constant is given in The relation between electrical conductivity and layer number is given in The structure of the graphene products was also evaluated by FTIR analyses of CG, G-OCTA, G-DMSO, G-NaOH, G-n-Hexa, G-EG, G-ED, and G-PA, as presented in −1 due to the remaining carbonyl groups after the sonication and MW irradiation process [−1, corresponding to C-O-C bonds, is from remaining epoxides and other ether groups [−1 indicates the residual of solvent coming from filtration process [2 C=C bonds was found at 1618 cm−1 which have been assigned to skeletal vibrations of graphitic domains [−1 is characteristic of -COOH (-C=O in carboxylic acid), which is known to appear after purification steps following reaction and exposure to air [−1 correspond to the asymmetric stretch mode of atmospheric carbon dioxide, which is routinely observed in the background scan on an FTIR measurement, while the peak is caused by the absorption of CO2 from the air [−1 was due to O-H stretching vibrations [The FT-IR spectra of G-PA indicates graphene oxide (GO) formation and reveals the presence of various functional groups, including C-O-C and C-O stretching vibrations observed as a very low intensity band at 1083 cm process . The pear groups . The pea process . The sp2 domains ,46. The e to air . The ban the air . The brobrations . The FTIThe UV-vis spectroscopy is one of the most efficient techniques showing the presence of the bonds and the functional groups in the graphene structure. The UV-vis spectra of commercial product (CG) and the MW assisted synthesized graphene products are presented in The absorbance peak at 223 nm is related to the π → π * electron transition of C-C ring. The absorbance peak at 273 nm is related to n → π * transition of C-O bonds . PreviouIn order to approve the graphene structure, Raman spectroscopy was performed using final graphene samples. The Raman spectra consisting of CG, G-PA, G-EG, G-DMSO, and G-OCTA are given in −1, G band at 1566 cm−1, and 2D band at 2689 cm−1. The average value of IG/I2D for all samples is 1.72. This indicates that all samples have single layer. A noise peak around 2315 cm−1, which is attributed to C-O structure, was seen for all the samples [−1 is characteristic to sp2 C=C bonds. In the Raman graph, the D band, also known as the disorder band, occurs due to lattice motion away from the middle of the Brillouin zone and its presence between 1270 and 1450 cm−1. Thus, this means that G-OCTA does not demonstrate a good graphene structure.The Raman spectra of all graphene products showed a D band at 1350 cm samples . These rBrunnauer–Emmett–Teller (BET) surface area values of products labeled as CG, G-OCTA, G-EG, G-DMSO, G-PA, and G-n-Hexa are given in 2/g for CG, G-OCTA, G-EG, G-DMSO, G-PA, and G-n-Hexa, respectively. It can be concluded that the graphene products named as G-EG, G-DMSO, and G-PA having the most promising layer number results also have large BET surface area values. CG has a smaller BET surface area than MW-assisted synthesized graphene products. Kamelduski et al. proposed that graphene with a high surface area can also be used in energy storage devices, especially Zn–air batteries [According to atteries . The thickness and layer numbers were also estimated by AFM analyses. The AFM results of final graphene products are given in The thickness of G-DMSO, G-EG, G-OCTA, G-PA, and CG is determined as 2056 nm, 418.85 nm, 1435 nm, 587 nm, and 9.87 nm and the layer numbers of G-DMSO, G-EG, G-OCTA, and CG are calculated as 6137.313, 1250.298, 4283.58, 1752.23, and 29.02, respectively. When comparing with CG, produced graphene samples showed multilayered structures. While comparing the layer numbers obtained by AFM with the layer number values that were calculated by XRD and presented in SEM results show that the obtained graphene products have a wrinkled and agglomerated structure. As seen in −1, G band at 1566 cm−1, and 2D band at 2689 cm−1 which are characteristic to graphene. AFM analyses demonstrated the multilayered structure of some of the samples. Moreover, SEM results proved that the obtained products have a wrinkled and agglomerated structure as is usually encountered in the graphene images.This study presents a quick and easy way for synthesizing graphene from graphite via microwave irradiation. This method depends on LPE of graphite in different solvents whose surface tensions are in the range of 25–75 mN/m. LPE is a top-down method which eliminates the disadvantages related to the traditional Hummers’ method, including using heavy and toxic chemicals and successive production steps. After the reaction and purification steps, obtained carbon products were characterized by using different characterization methods, such as XRD, four-point probe electrical conductivity measurement, FT-IR analysis, UV-vis spectroscopy, Raman spectroscopy, AFM spectroscopy, and SEM analysis techniques. The XRD spectra of the samples showed two peaks at 2θ = 26.5° and 54° which are characteristic of graphene. According to XRD results, obtained samples have 2–25 layers. Most of the products are few layered. Among these products, those with lowest graphene layers were determined as 2, 2, 5, and 2 for G-DMSO, G-OCTA, G-PA, and G-EG, respectively. When the dielectric constant of the used solvent increases, the electrical conductivity values increase too. The FTIR spectra of most of the graphene products showed no sharp peaks and were compatible with the FTIR spectrum of CG. The UV-Vis spectra of all the graphene products display three peaks 223, 240, and 273 nm which refer to the π → π * electron transition to C-C ring, red-shifting of 223 nm domain, and n → π * transition of the C-O bond. The Raman spectra of all products showed peaks at D band at 1350 cm


Predicting long-term student achievement is a critical task for teachers and for educational data mining. However, most of the models do not consider two typical situations in real-life classrooms. The first is that teachers develop their own questions for online formative assessment. Therefore, there are a huge number of possible questions, each of which is answered by only a few students. Second, online formative assessment often involves open-ended questions that students answer in writing. These types of questions in online formative assessment are highly valuable. However, analyzing the responses automatically can be a complex process. In this paper, we address these two challenges. We analyzed 621,575 answers to closed-ended questions and 16,618 answers to open-ended questions by 464 fourth-graders from 24 low socioeconomic status (SES) schools. Using regressors obtained from linguistic features of the answers and an automatic incoherent response classifier, we built a linear model that predicts the score on an end-of-year national standardized test. We found that despite answering 36.4 times fewer open-ended questions than closed questions, including features of the students’ open responses in our model improved our prediction of their end-of-year test scores. To the best of our knowledge, this is the first time that a predictor of end-of-year test scores has been improved by using automatically detected features of answers to open-ended questions on online formative assessments. One of the most recommended teaching and learning strategies is formative assessment . These aIn this paper, we use a database of questions and answers taken from ConectaIdeas . This isIn principle, open-ended questions allow teachers to visualize their students’ reasoning. Additionally, promoting written answers to open-ended questions helps not only to reason about calculations but also to acquire written communication skills. This is particularly true if the question asks for explanations. These are questions that require more effort from students, but which are posed much less frequently than closed questions. Moreover, these are teacher-adapted or teacher-designed questions, thus leading to a wide range of question types. It is therefore more challenging to estimate their long-term learning than when working with questions taken from a carefully crafted and small list of closed questions. In this paper, we examine the responses of fourth-grade students from at-risk schools. The answers are very short. The average number of words in the responses is eight to nine words. This average increases as the school year progresses . On the Research question: To what extent do students’ short, written answers to teacher-designed, open-ended questions in weekly online formative tests help improve predictions of performance on end-of-the-year national multiple-choice standardized assessments?In this section, we examine related papers that investigate the possibility of making predictions of long-term learning and, on the other hand, the effect of answering open-ended mathematics questions.From a sample of 23,000 learners in Grades 6, 7, and 8 over three academic years, p-values less than 0.05) when testing is immediate but not after there is a delay.These papers show that the score at the end-of-the-year tests can be predicted with a reasonable degree of error and that written answers to open-ended questions help the learning process. However, in the prediction literature, there are not yet regressors related to the written answers to open questions .Our data come from a virtual platform called ConectaIdeas. This platform has a series of mathematics exercises for elementary school students. Each student answers two types of online questions during weekly sessions. The questions are created by teachers and can be either closed-ended  or open-ended . Throughout the school year, students answer the 36 closed-ended questions and one or two open-ended questions during each session. The sessions are 90 min long, with two sessions held per week. Unlike the answers to closed-ended questions, the data for the answers to the open-ended questions are unstructured and provided in the form of a written text. This is a particularly challenging feature.Below, we outline the materials and methods used to predicting the scores on an end-of-year national standardized test.We focused on using data gathered from the ConectaIdeas platform during 2017 from both open-ended and closed-ended online exercises. Fourth grade students used this platform to exercise mathematics content. Additionally, in order to measure progress in terms of performance when using the platform, 24 schools participated in an RCT study. In this study, only one of the two classes in each school worked on the platform (treatment group), while the other class did not have any access to the platform (control group). We will only focus on the responses given by students in the treatment group, since they are the only ones who answered both open- and closed-ended questions during the year.Now, for simplicity, we only consider two important annual tests: one at the beginning of the year and the other at the end of the year. At the beginning of the year, a test was applied to measure the performance of students prior to participating in the study. This test is called the pre-test. The second test, called SIMCE, is a national standardized test that is conducted every year by an educational quality agency. We consider the pre-test per student as an estimator to predict the score on the end-of-year national standardized test (SIMCE). It is worth noting that at a national level, this standardized test has a mean of 260.95 and a standard deviation of 47.8, both of which are important reference values .Additionally, we include the average SIMCE score obtained by the school in 2016 as another estimator. Note that this estimator is at the school level and not per student. In addition, we automatically label responses to open-ended questions with two prediction models. The first is a question type detector, which consists of receiving a question and responding with one of six question types. These are: (1) Calculate without explaining, (2) Calculate with explaining, (3) Choice and/or affirmation, (4) Compare quantities, (5) Procedure and content knowledge and (0) Others. The second is a coherent answer detector, which receives both the answer and the question in order to detect whether or not the answer is incoherent. Answers that range from noisy text  to nonsensical responses  are considered to be incoherent answers.In the ConectaIdea platform, answers to open-ended questions do not receive a score according to the correctness of the answer to the open-ended question. However, for this study, years later, teachers labeled answers received in 2019 according to coherence, indicating whether the answer is incoherent or not. With this information, we fit a model capable of automatically predicting the coherence of written answers to open-ended questions. Then, we used that model to label the student answers received in 2017. In addition, coherence does not imply correctness, but the reverse is true. This information is used for two reasons: (1) Some of the incoherent answers  are not appropriate for extracting linguistic features such as part-of-speech and dependency-tags. In addition, separating coherent answers from incoherent ones allows us to make sure that the answers are appropriate to the question asked and (2) incoherent answers also reveal a student’s animic, attitudinal, and not necessarily cognitive state. If the student responds with a scribble or something disjointed, it reveals a disrespectful, negative attitude or protest behavior. If he or she responds with something completely inappropriate, it is very different from making a calculation error or misexplaining a result by making conceptual errors. As Below, we will perform a general analysis of the data for the prediction of 2017 SIMCE scores. In our case, we are interested in all those students on the platform who have answered at least one open-ended question (these are the written answers). In summary, only 58.29% of the students out of a universe of 796 students from across 24 schools have answered this type of question at least once. This corresponds to 464 students. Additionally, 44.59% of the 796 students are female. It can also be observed that neither the sex of the student nor the number of students with answers to open-ended questions are stable at the school level, since there are schools with more girls than boys and others with 78.04% of students with responses to open-ended questions.https://www.junaeb.cl/ive, accessed on 16 March 2022). The weighted average of IVE (according to the number of students per school) for our data is 90.2%. The average of the IVE in Chile is 80.5% and the standard deviation is 17.2%. Thus, the IVE of these 24 schools is 0.56 standard deviations above the national mean. On the other hand, the average SIMCE score of the 24 schools in 2017 is 0.381 standard deviations below the SIMCE national average of that year. Despite this, there are students in some schools that stand out with 2.45 standard deviations above the national average.Now, we filter by all students with at least one response to an open-ended question. When we reduce the sample to this universe of students, we obtain a more even distribution between male and female students . It should be added that the 24 schools we studied correspond in general to at-risk schools, with a high percentage of vulnerability. For this, we use the index of vulnerability (IVE). This is an index that the Chilean Ministry of Education officially calculates for each school .Dynamic. Unlike the previous ones, these variables can vary during the year, e.g., number of online exercises answered on the first attempt. In this context, these variables are usually called Process variables . The objective is to transform all this information into a few relevant variables. For this, attributes are extracted from each of them independently . In this way, each student’s answer is encoded with a vector representation where each component of the vector is an interpretable variable. As each student answers more than one question, a stack of vectorized answers will be obtained . However, since each student answers a different number of questions per year, if we concatenate the rows of this matrix, we will obtain vectors of different lengths for each student. This is a problem when considering these as regressors for the linear model. To solve this, we will reduce the matrix to a single vector with the same number of columns, where each component condenses only the information of that column on individual vectors of the matrix . Finally, as we wish to keep those components with the best predictions on the linear model, dimensionality reduction will be applied .Simple indicators. These are indicators that allow data to be aggregated: in our case, to aggregate the answers to open-ended questions. One indicator is the number, e.g., number of incoherent answers. Another one is the proportions, e.g., the proportion of coherent answers. In addition, there are double aggregate indicators, e.g., number of coherent answers to calculate with explaining questions.Traditional/Semantic/Contextual features. Different features can be extracted from any given answer. These are Traditional, Semantic and Contextual features. Traditional attributes are those that capture simple properties of the sequence of letters in words and the sequence of words in sentences, e.g., number of vowels and number of words. Semantic attributes, on the other hand, correspond to properties that have a conceptual charge in words, e.g., number of well-spelled words or curse words or emoticons. Meanwhile, Contextual attributes are those that depend on the environment of the sentence; in the case of answers, their context is the question, e.g., the number of similar words between the answer and its question. Given all the answers that are given by a student, the way in which these features are distributed is important, e.g., Average of number of words in coherent answers. These features can also be aggregated by question type, e.g., Standard deviation of number of numbers in answers to calculate without explaining questions.Linguistic features. Unlike the previous features, this time, we are interested in using linguistic knowledge to capture useful information from the answers. The most abstract features of an answer are (shape) the shape of the tokens and  indicator of whether the token is alphabetic. An example of this is the following:Some words are noted for their abundance; these are called stop-words, e.g., https://explosion.ai/, accessed on 16 March 2022).We will now consider two more attributes, but this time, they depend on the other words and need to be detected automatically. These are part-of-speech tagging (POS tags) and dependencies (dep), both of which are available in the Spanish version of the Spacy library . An example of a regressor using POS tags is the following: Median number of verbs (tag/VERB) in coherent answers.POS tags are detected for each token in a response and can be: (PRON) Pronoun, (ADJ) Adjective, (VERB) Verb, and (NUM) Numeral, among others (Available in candies from the response There are 25 candies then 25 will take the role of the root (dep/ROOT) of the sentence that had candies; Likewise, if we add candies then 25 obtains its characteristic of being a number with units, which is a dependency called a Numeric modifier (dep/nummod). An example of a regressor using dependency tags is the following: Average of number of dep/nummod in coherent answers. Other interesting dependency tags include: (obj) object, (nsubj) nominal subject, and (nmod) nominal modifier, among others .Dependency tags correspond to syntactic dependencies between the tokens of the answer; e.g., if we remove In the following, we will detail the types of regressors based on written responses that we consider for the Open-ended model:Filters. The simplest way to construct a regressor based on the answers is to consider all of them. Another way is to use a subset of the answers, e.g., only incoherent answers. This way of filtering is based on coherence. In addition, the type of question associated with the answer is automatically detected so that it can be grouped according to question type, e.g., only answers to questions of type Others. Likewise, both filters can be applied, i.e., both according to coherence and question type, e.g., only incoherent answers to questions of type Others. We will include several variables related to potentially incoherent written answers. Some attributes of these answers can reveal negative moods and attitudes of the student. However, negative affective states help improve the prediction of results in the end-of-year test  Mutation of the k individuals; (4) Cross-over between individuals and the current population; (5) Evaluation and selection of the fittest individual; and (6) Return to (1) and repeat the process. The algorithm starts with 14 regressors. These correspond to the regressors used by the Baseline model. For more details, go to Algorithm A1.Reduction. Two things happen: (a) Too many regressors tend to be selected in relation to the number of regressors in the baseline model, and (b) linear models with more regressors tend to predict the observed variable better. To address this, we reduce the number of regressors selected until we obtain a similar number of regressors as the baseline model. For the Open-ended model, we choose the same number of regressors as in the baseline model to correctly compare the linear models with the Validation. In order to avoid overfitting, we use 250 four-fold cross-validations.The following scheme is used for selecting regressors:Metrics. For regression models, we use two statistical estimators to measure the model fitting. The first is the coefficient of determination or Validation. To validate, the k-fold cross-validation technique is used. This consists of randomly dividing the data set into k chunks of similar sizes. Training and testing with the entire data set allows having an estimate with k samples of the performance of a linear model more representative than an estimate with only one sample when using the classical approach. For the same reason, N random repetitions of k-fold cross-validation are performed; this corresponds to Baseline. To compare the model based on open-ended questions, we propose a single baseline. As with the proposed model, the baseline is a linear regression model. This time, the regressors are the so-called traditional regressors, separated into historical and dynamic regressors. This model consists of 14 regressors: a pre-test, a double regressor corresponding to whether the student is male or female, a single socioeconomic regressor named school vulnerability index (IVE), 2016 SIMCE score for the establishment, and other regressors from the exercises completed during the year. In the work of The following evaluation scheme is proposed to evaluate the model:p-value were obtained using the python scipy.stats module using the pearsonr function .Before showing the Open-ended model with its selected regressors, we will first show other regressors that use the answers to open-ended questions and that were not selected despite having a significant correlation between SIMCE score. First, One of the most important regressors is probably the number of responses, with a correlation of We will now focus on the regressors that use the linguistic attributes of the responses to open-ended questions as an estimator of the SIMCE score. For simplicity, we will report only five of these in wers see . The samstatsmodels python library using the OLS function .Finally, the baseline model and the Open-ended model are subjected to the same evaluation criterion: 250 four-fold cross-validations stratified by establishment. For each model, the average value of the coefficient associated with the regressor and its relevance to the model at each training stage is determined. Both the correlation with the SIMCE score and these two values are reported in one table per model  Exercise accuracy (answers to closed questions), with a correlation of According to The Open-ended model has 14 regressors, just like the baseline model. Of these, only According to i and column j corresponds to the correlation between the i-th regressor and the j-th regressor.Finally, we report the correlation matrix of both models see  and the First, the correlation matrix of the Open-ended model is more homogeneous and null than the that of the baseline model. This means that the Open-ended model regressors correlate less with each other than the baseline model regressors. This is a good feature if we want regressors with different informative estimators per student.Next, the Open-ended model performs better in both training and testing than the baseline model, with an average We have built a set of predictors using written responses in addition to answers to closed questions. We use labels of predictions of question type and coherence type in order to filter the questions and obtain different regressors. Of all these regressors, the Open-ended model mostly uses only regressors based on linguistic attributes (9/14) and only three regressors of the baseline. The other two regressors of the Open-ended model correspond to a simple indicator and a regressor based on traditional attributes from the responses. In addition, all of the regressors selected for this model distinguish between response types according to coherence and/or question type. In particular, these regressors are better estimators for predicting a final score than regressors that do not distinguish between response types. In particular, five of these regressors only use responses that are detected as incoherent, with regressors for question types 1, 2 and 3 being predominant.The baseline model and the model based on open-ended questions only share three of the regressors. The remaining regressors from the baseline model use simple indicators of written answers and estimators constructed using the closed-ended questions. This is not like the model based on open-ended questions, which uses the written answers to these types of questions and linguistic attributes from the answers.Our results also show that the proposed model outperforms the baseline model. The Some of the regressors of the Open-ended model do not contribute significantly to the prediction compared to the rest of the regressors; however, we observed with several experiments that removing them reduces the test performance.We also found models based on open-ended questions with 30 regressors and an expected value of The first one uses the nominal subject dependency tag (nsubj) and the standard deviation as the aggregation function. In our case, the variable “Average: Ratio of dep/nsubj tokens in question-dependent incoherent answers to calculate with explaining questions” considers this type of token but using the average as the aggregation function. In the referenced work The second variable uses the object dependency tag (obj) and the standard deviation as an aggregation function. In our case, the variable “Sum: Number of dep/obj tokens in incoherent answers to choice and/or affirmation questions” considers this type of token but using the sum as the aggregation function. In the referenced work On the other hand, among the retained variables, there is also one that measures the “lexical sophistication” of the students’ writing , which iIn the first experiment, they use a linear model with five linguistic features; two of them are named “Phonographic Neighbors: Function words” and “Word-naming accuracy: Function words”. These variables use the function words (stop-words) from the students’ mails to estimate the self-reported mathematical identity. In the Open-ended model, the variable “Standard deviation: Ratio of stop-word tokens in coherent answers” considers this type of word but using the standard deviation as the aggregation function. In the second experiment, they use a linear model with five linguistic variables; three of them are lexical indices, where one of them is called “Polysemy (adverbs)”. This variable uses the adverbs found in the emails sent and written by the students to predict their math scores. In the Open-ended model, the variable “Maximum: Number of tag/ADV tokens in incoherent answers” considers this type of word but using the maximum as an aggregation function. p-value. Second, we will mention three other variables that are not significant enough for our criteria but deserve a brief mention. We study in order the variables in In the following, we will give an interpretation of a subset of the variables in the Open-ended model. First, the first four most significant variables will be described in detail: that is, those variables with the third quartile (Incoherent answers to type 3 (Choice and/or affirm) questions can be either question-dependent or question-independent incoherent. As the proportion of independent incoherence to this type of questions is significantly less than the dependent ones, without loss of generality, the answers of interest fulfill two properties: (1) they are well structured answers, but (2) they do not answer the question asked. Thus, determining dependency between labels on responses that satisfy (1) and (2) is a potentially important feature.https://universaldependencies.org/u/dep/obj.html, accessed on 16 March 2022). A token with this tag is the phrase that denotes the entity that undergoes the action given by the verb. An example of this type of tag is the following:where tens is the object that undergoes the movement of the verb asked.Now, the dependency tag object (obj) of a verb is the second most important argument of a verb after the subject  he/she answers several times with the obj dependency label or (B) he/she answers few times but in his/her sentences, there is repeatedly the presence of the obj dependency. Note that case (B) can be extended to case (A) by partitioning the long answers into short sub-answers. In any case, the answers have the characteristic of answering the request to “Explain”. They use a structure that attempts to justify a reasoning. However, they do not mention whether the reasoning is correct or not. This characteristic leaves them as incomplete answers that are therefore incoherent.In summary, the variable quantifies incomplete answers to type 3 questions that have an appropriate structure. Therefore, higher values will punish more of those students who do not follow the requested question. Of course, this measure is with respect to the mean value and only for type 3 questions. Since the mean value is https://universaldependencies.org/u/pos/ADV, accessed on 16 March 2022). They can even modify adjectives and other adverbs. An example of this type of tag is the following:where the word very repeatedly modifies the adjective bad.In general, incoherent answers, independent of the question, do not have an appropriate structure, since they are often silent words, sentences without logical meaning and/or non-words. For this reason, the presence of part-of-speech tokens should only be detected on incoherent and sophisticated answers, i.e., question-dependent incoherent answers. Now, the type of token of interest is the adverbial part of speech (ADV). These are words that usually modify verbs in the categories of time, place, direction or mood  and has a positive effect on the model. Therefore, if a student uses several tokens of the ADV type on one of his incoherent answers, he/she will have a directly proportional bonus in the estimation of his/her score. Since the presence of this type of label on sophisticated answers is often used as a discourse embellishment, the bonus is positive when trying to convince with his answer using a stylistic resource.https://universaldependencies.org/u/dep/root.html, accessed on 16 March 2022). Two examples of this type of tag are as follows:where in (1), the only token 23 will have the ROOT tag, while in (2), the token will be find.This time, the responses of interest are incoherent-dependent responses to questions that ask both to calculate a quantity and to justify their answer. For the same reasons as before, the detection of dependency labels in this type of answers is appropriate. Now, the dependency label is ROOT. As the name implies, the ROOT grammatical relation points to the root of the sentence  he/she responds several times to short sentences or (B) few times, but in his/her sentences, there is repeatedly the presence of the ROOT dependency label. Note that case (B) can be extended to case (A) by partitioning the long answers into short sub-answers. In any case, the short answers are characterized by answering the request to “Calculate” but not “Explain”. They answer with only a quantity. However, they do not provide an explanation or justification of how they obtained their result. This characteristic leaves them as incomplete answers that are therefore incoherent. Unlike the longer responses, incomplete responses are penalized more than those that do not provide an amount.Similar to the first variable described, this other variable quantifies incomplete answers to type 2 questions. Thus, higher values will penalize more those students who do not provide an explanation of how they obtained their result. Obviously, this measure is with respect to the mean value and only for type 2 questions. Since the mean value is 3.0, it is to be expected that students who have more than two short answers will further decrease the value of their score.https://universaldependencies.org/u/dep/amod.html, accessed on 16 March 2022). An example of this type of token follows:where drawn is an adjectival modifier of the noun sticks.Unlike the previously described variables, this time, the variable to be analyzed only considers coherent answers to questions of all types. These types of answers are characterized by being well structured and responding to the underlying question. Note that the latter does not mean that the answer is correct. The tags used are adjectival modifier dependency (amod) tags. A token with this tag is any adjectival phrase that serves to modify the noun (or pronoun) , and with a positive effect on the model. Thus, if a student uses several tokens of the amod type on several of his coherent answers, he/she will have a directly proportional bonus in the estimation of his/her score. Since the presence of such labels in sophisticated responses is often used as a discourse ornament, the bonus is positive when adjectives are used as a stylistic device.p-value of the coefficient is not significant. Under our criteria, they are not significant variables. However, it is worth describing them. Some of them have a clear interpretation. For others, their presence may be due to multiple causes that are outside the scope of the analysis in this paper.For the rest of the variables to be analyzed, the third quartile of the Number of coherent answers to choice and/or affirmation questions. This variable is interesting, since it has a positive correlation with the variable to be predicted but contributes a negative effect on the model. This phenomenon is not uncommon. In fact, when working with linear models with more than one variable, there is no reason to expect the sign of the correlation to be the same as that of the model effect. The number of coherent answers has a positive effect but not so much in the type of choice and affirmation questions. Therefore, this regressor corrects for a possible overshoot of the number of coherent responses as accounted in the variable Sum: Ratio of dep/amod tokens in coherent answers.Standard deviation: Ratio of stop-word tokens in coherent answers. Coherent answers are closer to being correct answers than answers that are incoherent. This is because, in particular, coherent responses answer the question appropriately, and incoherent answers do not. For this reason, determining linguistic features on these types of student responses captures structural properties that are not noise. Now, for the variable of interest, we determine the ratio of stop-words over all coherent responses. Stop-words are words that are abound in language. Some of these words are: the, if, what, not, of. Thus, (A) a high ratio of stop-words means that in the answer, there is an abundance of stop-words. Likewise, (B) a low ratio corresponds to a lack of stop-words. It is not clear which (A or B) is better. However, the contribution of this variable in the model suggests that if a student does not consistently follow either style A or B, then this weighs negatively on his/her score. Such consistency is measured by standard deviations. If the standard deviation is far from zero, then this explains an inconsistency of styles.Sum: Number of punctuation symbols in coherent answers to calculate without explaining question. As mentioned for the previous variable, coherent answers are usually correct answers. This time, only coherent answers to questions that ask for a calculation but do not require a justification of how the result was obtained are important. Answers to such questions will be coherent if they at least contain an appropriate mathematical representation, e.g., 3/4. Such mathematical expressions are accompanied by punctuation symbols, e.g., /. In other cases, they serve to explain a procedure to obtain a result, e.g., 1/2 + 2 = 3/2. In this last case, four punctuation symbols are used, twice /, once + and once =. Other students usually represent the same mathematical objects but written in words, e.g., three quarters. This last example does not use punctuation symbols. However, since the variable is a sum of the total number of these punctuation symbols, then all the punctuation symbols used by the student are considered. Thus, students who represent their mathematical objects with symbols will also earn a bonus on their score. In addition, some students often explain using these symbols even though the question does not require them.To the best of our knowledge, this is the first time a study has looked at the contribution of written responses to open-ended questions on weekly online formative assessments when predicting individual performance on end-of-year standardized mathematics tests by students in fourth grade.Students only answered one or two open-ended online questions each week. This is 36.4 times less than the number of answers given to closed questions. Despite this, we found that the written answers provided information that allows us to make better predictions than when only working with responses to close-ended questions. In addition to there being very few open-ended questions when compared to closed questions, the students also wrote very short answers. These low-SES school students are not used to communicating in writing in mathematics. On average, the students wrote only eight to nine words for each answer. This is because they were not used to this type of question, let alone having to explain what they did. Even so, these written answers contain features that allow us to improve our predictions of long-term achievement, as measured by scores on a national summative test. The incredible thing is that this improvement in prediction is achieved even when the summative national tests do not contain any open-ended questions. This just shows the huge potential of open-ended questions and their written responses.On the other hand, there is a growing interest in developing and implementing a dialogic pedagogy : that isIn the near future, it would be important to look at the impact of an implementation with a larger number and proportion of open-ended questions. It would not be surprising if having students write more frequently and justify their responses were to have an even greater effect than that the one reported here. It also remains as future work to investigate how an early and automated warning of incoherence could speed up the process of producing coherent explanations as well as better explanations. On the other hand, it would be interesting to introduce instructions and explicitly teach students different strategies on how to explain their solutions to problems. We could then determine the effect this has on their adoption as well as on the students’ long-term learning. It also remains as future work to study the extent to which this process of written reasoning can be transformed from these early stages to a more rigorous form of mathematical reasoning, leading gradually to the sort of reasoning typically required in mathematical proofs.