We introduce high-performance metal mesh/graphene hybrid transparent conductive layers (TCLs) using prime-location and metal-doped graphene in near-ultraviolet light-emitting diodes (NUV LEDs). Despite the transparency and sheet resistance values being similar for hybrid TCLs, there were huge differences in the NUV LEDs’ electrical and optical properties depending on the location of the graphene layer. We achieved better physical stability and current spreading when the graphene layer was located beneath the metal mesh, in direct contact with the p-GaN layer. We further improved the contact properties by adding a very thin Au mesh between the thick Ag mesh and the graphene layer to produce a dual-layered metal mesh. The Au mesh effectively doped the graphene layer to create a p-type electrode. Using Raman spectra, work function variations, and the transfer length method (TLM), we verified the effect of doping the graphene layer after depositing a very thin metal layer on the graphene layers. From our results, we suggest that the nature of the contact is an important criterion for improving the electrical and optical performance of hybrid TCLs, and the method of doping graphene layers provides new opportunities for solving contact issues in other semiconductor devices. Graphene itself has a high value of sheet resistance (300–1500 Ω/□) resulting from imperfect growth and is therefore inappropriate for use in TCLs5. There have been multiple efforts to modify graphene using metal nanoparticles, metal nanowires, and a metal mesh to achieve the necessary performance characteristics and make graphene a suitable replacement for the oxide-based TCLs in UV LEDs and flexible devices12. However, despite reports of excellent performance for modified graphene, graphene/metal nanoparticle hybrid TCLs still exhibit unacceptable levels of sheet resistance caused by the discrete nanoparticles’ structures6. Moreover, even when the graphene/metal nanowire hybrid TCLs exhibit proper values for their sheet resistance (10–100 Ω/□) and transmittance (~85%), it has been difficult to control the uniformity of the thin nanowire films precisely and ensure their reliable contact characteristics in optoelectronic devices12. These drawbacks have led to the degradation of electrical contact properties in attempts to fabricate practical optoelectronic devices.Graphene-based transparent conductive layers (TCLs) have been attracting attention as alternatives to conventional TCLs in optoelectronic devices, such as solar cells and light-emitting diodes (LEDs). They offer several advantages over existing oxide-based TCLs, including (i) high transparency without wavelength dependency, (ii) high conductivity, and iii) good flexibilityAmong the various candidates, graphene/metal mesh hybrid TCLs are noteworthy for their use in practical applications because they have a low value of sheet resistance (1–100 Ω/□) and high transparency (~90%). Furthermore, the electrical and optical properties of hybrid TCLs can be sufficiently controlled by rationally designing the features of the metal mesh, such as the gaps between lines, line widths, and thicknesses.10. We assume that the undesirable results for the hybrid TCLs in optoelectronic devices can be attributed to several issues: (i) the structure of the metal mesh, (ii) the optimum position of each material due to its hybridization, and (iii) the contact resistance of TCLs in devices.Despite these advantages, there are presently few research results reporting the development of graphene/metal mesh hybrid TCLs for optoelectronic devices, and performance reported for both the electrical and optical properties of hybrid TCLs have fallen short of expectations13. However, achieving high-performance conditions for actual optoelectronic devices still requires determining the optimum position of each material and improving its contact properties with the p-GaN layer.We have previously reported the successful design of a metal mesh, and solved the structural issue in GaN-based near ultra-violet light-emitting diodes (NUV LEDs) by adding a supporting layer in the metal mesh to guarantee its stable contact properties13. We observed huge differences in the electrical and optical properties depending on the location of the graphene in the NUV LEDs even if each TCL showed similar sheet resistance and transmittance values, regardless of the graphene’s location. A main factor in these differences was the change made in the contact with the p-GaN layer, accomplished by changing the location of the graphene. Furthermore, we adopted a dual-layered metal mesh  to dope the graphene layer. Giovannetti et al. first reported the effect of doping a graphene layer by depositing metal, and several groups have since reported on the principles involved in the doping effects, both theoretically and experimentally15. However, to date, there have been few results involving practical devices. In our work, we have obtained improvements for the contact properties in the NUV LEDs by doping the graphene layer with a 5 nm-thick Au/150 nm-thick Ag mesh, and we have verified the doping effect through Raman spectra, work function variations, and transfer length methods (TLMs). The superior changes in the graphene layer using this simple metal deposition technique provide many opportunities to control contact properties in other semiconductor devices.In this work, we demonstrate performance when a graphene layer is located either above or below the metal mesh, the doping of the graphene layer by incorporating a dual-layered metal mesh, and their effect on the performance of GaN-based NUV LEDs. Thanks to the high transparency of the hybrid films in the UV region, we were able to obtain remarkable optical properties in GaN-based NUV LEDs compared to conventional ITO. We used the metal mesh structure as reported in our previous work Even when the metal mesh and/or hybrid films exhibited similar sheet resistances and transmittance values, significant differences were observed in the electrical and optical performances of the NUV LEDs depending on the existence and location of the graphene (prime-location graphene).We were able to dope the graphene layers by depositing a dual-layered metal mesh on them; this improved the contact resistance for the p-GaN layer . Since NUV LEDs fabricated with a 150 nm-thick Ag mesh combined with graphene could act as both prime-location graphene and metal-doped graphene, we designated them “Mesh on GR/Ag doping”.Figure As can be seen in Fig. Furthermore, the light output power of the NUV LEDs exhibited huge variations depending on the type of very thin metal mesh layer on the graphene layer. Specifically, while NUV LEDs with “Ni doping” exhibited the lowest light output power among all samples, we obtained the highest light output power in the NUV LEDs with “Au doping”. Figure 16. Even though the sheet resistance values of the “ITO” and “GR” TCLs were high , those of the other TCLs with the Ag mesh structures were very low: 4.88 Ω/□ for “Mesh”, 5.39 Ω/□ for “GR on Mesh”, and 4.54 Ω/□ for “Mesh on GR”. We were able to observe differences in the sheet resistance values based on the existence and location of the graphene layer. The lower sheet resistance values of “Mesh on GR” than “Mesh” were attributed to the graphene layer bridging the gaps of the Ag mesh. However, the higher sheet resistance values of “GR on Mesh” than “Mesh” were due mostly to the torn and rolled-up condition of the graphene layers, even though the graphene layers connected the Ag mesh, as shown in Fig. 17. Next, we investigated the Raman spectra  to identify the graphene layers above/below the Ag mesh. In Fig. −1 and 1586 cm−1) and 2D peaks (2694 cm−1 and 2683 cm−1), respectively. Notably, there were peak shifts in the G and 2D peaks in “4” on the graphene layer on the Ag mesh. These shifts are further discussed in the section on metal-doped graphene. In contrast, the G peak (1591 cm−1) and 2D peak (2693 cm−1) appeared only in a gap region in the “Mesh on GR” TCL. To investigate the contact properties between the TCLs and p-GaN layers depending on the location of the graphene layers, we fabricated TLM samples using pure p-GaN layers. The process of preparing the TLM samples is shown in the inset of Fig. 2 for “ITO”, 0.53 Ω·cm2 for “Ag”, 0.81 Ω·cm2 for “GR on Ag”, and 1.23 Ω·cm2 for “Ag on GR” , a graphene layer (“GR”), a 150 nm-thick Ag mesh (“Mesh”), a graphene layer on a 150 nm-thick Ag mesh (“GR on Mesh”), and a 150 nm-thick Ag mesh on a graphene layer (“Mesh on GR”) on double-side-polished sapphire (DSPS) substrates to investigate the transparency and sheet resistance. We measured the transmittance values in the range 350–600 nm with a UV-VIS-IR spectrometer , as shown in Fig. 21. However, the chemical doping of graphene has several disadvantages, such as instability in external environments. For that reason, in this work we doped the graphene by simply depositing a very thin metal layer. A number of researchers have previously reported the potential doping of graphene using thin metal layers22. By investigating with Raman analysis, the measurement of work function, and TLMs, we determined the effect of doping the graphene using very thin metal deposition, and we describe how to incorporate their advantages in our devices below.Even though the remarkable transparency and sheet resistance of the TCLs was established, some uncertainty remains regarding device performance. For example, we previously observed that there were huge variations depending on the location of the graphene layer, even when they had similar transparency and sheet resistance values. Accordingly, the contact issues require careful consideration to obtain high-performance TCLs. To improve contact properties when using graphene-based TCLs, the graphene is typically doped using chemical methods24. In order to verify the doping effect of the very thin metal layer on the graphene layer, we prepared samples for Raman analysis, as can be seen in Fig. Raman spectroscopy is one of the most powerful tools for investigating changes in the graphene layer. Many research groups have used Raman spectroscopy to characterize chemically doped graphenepz states with metal d states24. This overlap of the band structure results in electron diffusion from the Ni layer to the graphene layer by repulsive force of the electrons caused by Pauli exclusion principle and causes “Ni doping” n-type doping. Thus, there are two types of adsorption for metal-graphene: physisorption  and chemisorption . It is necessary to use materials with large work functions for the p-GaN layer to form better contact properties. In Fig. In addition to using Raman spectra to verify doping type, we checked the doping effect of graphene by measuring the work function of the graphene with the very thin metal layer using ultraviolet photoelectron spectroscopy. Figure 2 for “Ag doping”, 1.1 Ω·cm2 for “Ni doping”, and 0.08 Ω·cm2 for “Au doping”  for mesa masks. The mesa structures were formed through photolithography with positive photoresist (PR) (AZ 4330) and inductively coupled plasma (ICP) etching, as can be seen in Fig. 2 layers using acetone and an HF solution sequentially. Using the mesa structures of the NUV LEDs, we then fabricated six types of TCLs on the p-GaN layers: 150-nm-thick ITO (“ITO”), 150-nm-thick Ag mesh (“Mesh”), a graphene layer on 150-nm-thick Ag mesh (“GR on Mesh”), 150-nm-thick Ag mesh on a graphene layer (“Mesh on GR/Ag doping”), 150-nm-thick Ag mesh and 5 nm-thick Ni mesh on a graphene layer (“Ni doping”), and 150-nm-thick Ag mesh and 5-nm-thick Au mesh on a graphene layer (“Au doping”). For “ITO” samples, we made patterns using negative PR (AZ 2035) and deposited the 150-nm-thick ITO layers on the patterned p-GaN layers by e-beam evaporation (E-beam). After the PR lift-off, the ITO layers were annealed by rapid thermal annealing at 600 °C for 5 minutes under ambient air. We subsequently completed the metal mesh in all of the TCLs  by metal deposition process and graphene transfer process, as can be seen in Fig. Figure 17/cm3 and 7 × 1017/cm3 for the two samples. We employed lower hole concentration for investigating the different locations of the graphene and higher hole concentration for verifying the doping of the graphene in the TLM samples.We grew two TLM samples with a structure of 1-µm-thick pure p-GaN layers/2-µm-thick un-doped GaN layers on sapphire substrates. We measured the hole concentrations of the pure p-GaN layers using Hall measurements (model: HL 5500 supplied by BIO-RAD) and obtained values of 3.7 × 102 on the pure GaN layers to make mesa masks using PECVD and made mesa patterns on the SiO2 layers using photolithography with AZ 4330. We formed the mesa structures by ICP etching and stripping out the PR. We removed the rest of the SiO2 layers with HF solution. Using the mesa structures, we then fabricated TLM samples with 100-μm width and 200-μm length with a range of gaps from 5-μm to 40-μm with a 5-μm interval. We prepared ITO and Ag samples for the TLM samples (“ITO” and “Ag”) using the same methods described in method section 1.We deposited 300-nm-thick SiOIn order to verify how the contact properties differed depending on the graphene locations, we made two different types of TLM samples: one with a graphene layer on 150-nm-thick Ag (“GR on Ag”) and one with 150-nm-thick Ag on a graphene layer (“Ag on GR”). For “GR on Ag”, we deposited a 150-nm-thick Ag layer on the pure p-GaN layer after creating the TLM patterns and then transferred a graphene layer onto the Ag layer. For “Ag on GR”, we transferred a graphene layer onto the mesa structures of the pure p-GaN layers and deposited a 150-nm-thick Ag layer on the graphene layer. We completed the TLM samples by isolating the graphene layers using reactive ion etching (RIE) with TLM PR mask patterns and removing the rest of the PR.Using the same methods introduced in Sec. 2. 2-1, we made mesa structures on the pure p-GaN layers. To verify how doping the graphene affected the contact properties through thin metal interlayers, we prepared three types of thin metal interlayer on the graphene layer: 150-nm-thick Ag/5-nm thick Ag/graphene (“Ag doping”), 150-nm-thick Ag/5-nm-thick Ni/graphene (“Ni doping”), and 150-nm-thick Ag/5-nm-thick Au/graphene (“Au doping”).For all TLM samples, we transferred the graphene layers onto the mesa structures of the pure p-GaN layers and deposited the thin metal layers by E-beam. Furthermore, 150-nm-thick Ag layers were equally deposited on the thin metal layers. The graphene layers were separated by RIE etching with TLM PR mask patterns, and the rest of the PR was removed.To investigate the doping effect of graphene based on Raman shift, we prepared three types of graphene layers located on different thin metal layers, graphene on 5-nm-thick Ag (“Ag doping”), graphene on 5-nm-thick Ni (“Ni doping”), and graphene on 5-nm-thick Au (“Au doping”), as shown in Fig. Supplementary Information


We review our recent time- and angle-resolved photoemission spectroscopy experiments, which measure the transient electronic structure of optically driven graphene. For pump photon energies in the near infrared ( Dynamical engineering of the electronic structure of solids with light is emerging as an important new area of research, which complements more traditional routes of control using chemistry, pressure, or magnetic fields. Especially interesting are new techniques that can drive materials close to lattice and other resonances at mid-infrared and THz frequencies. Indeed, in these driven solids, new properties emerge away from equilibrium. These result naturally from modulation with a strong, periodic field that creates new effective Hamiltonians with new eigenstates.3C60,2Se3.Resonant excitation of the crystal lattice has, for example, been used to induce superconductivity far above the equilibrium critical temperature in cuprates1u phonon in bilayer graphene at Eph = 200 meV, in addition to carrier heating via free carrier absorption, the electronic band structure is modulated by the coherent oscillation of the carbon atoms. In this case, we observe anomalous carrier dynamics that we attribute to a transient enhancement of the electron-phonon coupling constant  For pump photon energies bigger than twice the chemical potential, electron-hole pairs are generated. Photo-excited electrons are found to rapidly accumulate above the Dirac point at the bottom of the conduction band where they form a short-lived population-inverted state  substrates were hydrogen-etched and annealed in an Argon atmosphere, resulting in carbon coverages of one and two monolayers, respectively. These carbon layers were then decoupled from the underlying substrate by hydrogen intercalation, giving quasi-freestanding monolayer and bilayer graphene.μm wavelength are typically used to excite the sample. At a variable delay from this excitation, the sample is illuminated with a second extreme ultra-violet (XUV) probe pulse that ejects photoelectrons from the sample. These photoelectrons are measured with a hemispherical analyzer, yielding the photocurrent as a function of kinetic energy, Ekin, and emission angle, θ. From the kinetic energy, the binding energy, EB, of the electrons in the solid can be obtained, and the measured emission angle can be converted into in-plane momentum, Tr-ARPES measurements have been performed at the Artemis user facility at Harwell, United Kingdom. Femtosecond laser pulses tunable between 800 nm and 15 μm) with subsequent difference frequency generation (4 to 15 μm). About 1 mJ of laser energy is used for high-order harmonics generation in an Argon gas jet, generating XUV photons in the energy range between 10 and 40 eV. A single-grating time-preserving monochromator is used to select a single harmonic at μm.The setup is based on a 30 fs/780 nm/1 kHz Titanium:Sapphire (Ti:Sa) laser. Pump pulses are generated with a commercial optical parametric amplifier  is too bad to allow access to the primary thermalization events.The measurements displayed in Fig. The ultrafast <30 fs dynamics after photo-excitation are predicted to be dominated by different Auger processes as sketched in Figs. CB, as well as their average kinetic energy, ECB/NCB. In the case of Auger recombination, NCB decreases while ECB/NCB increases and vice versa for impact ionization. These numbers are directly accessible in a tr-ARPES experiment, as the photocurrent is proportional to the number of electrons at any given energy.These two processes can be easily identified by measuring the number of carriers inside the conduction band, Nσ = 9 fs (FWHM = 21 fs) using a pump photon energy of CB as well as ECB/NCB as a function of pump-probe delay. We find that, during the first ∼25 fs after photo-excitation, NCB keeps increasing while ECB/NCB already decreases, clearly indicating impact ionization.In order to resolve these primary scattering events, we repeated the experiment with a temporal resolution of For experiments in which the pump photon energy was below the threshold for interband excitation, metallic carriers in our hole-doped samples absorb energy from the pump by phonon-assisted intraband absorption Fig. . This ex1u phonon in bilayer graphene at Eph = 200 meV. (Monolayer graphene does not possess any infrared-active modes and cannot be used for the present purpose.) In this case, in addition to carrier heating via free carrier absorption  the observation of a transient population inversion, (2) pre-thermal scattering events dominated by impact ionization, and (3) anomalous dynamics due to resonant excitation of the crystal lattice.a priori clear whether such a state might exist in graphene. Ryzhii et al.et al.(1) and (2) are related to the conical band structure of graphene and the vanishing density of states at the Dirac point. While the occurrence of a population-inverted state after strong optical excitation is commonly observed in conventional semiconductors with a sizable band gap, it is not In our tr-ARPES experiments performed in different excitation regimes and with different temporal resolutions, we are able to observe the complete dynamics during the buildup and decay of the population inversion.1u phonon is resonantly excited can be understood in terms of a transiently enhanced electron-phonon coupling constant.1u mode.The observed anomalous carrier dynamics that occur when the EThis can also be understood from intuitive arguments without the need to invoke theoretical models. An increase in electronic temperature increases the phase space for optical phonon emission and is expected to result in faster cooling of the electronic system.1u mode in bilayer graphene leads to faster relaxation at lower peak electronic temperatures that we attribute to a transient enhancement of the electron-phonon coupling constant.We have reviewed the electronic-structural dynamics of optically excited graphene at different pump wavelengths throughout the near- and mid-infrared. These dynamics were tracked with time- and angle-resolved photoemission spectroscopy. We find that free-carrier absorption efficiently heats the electrons to high temperatures but maintains a thermal electronic distribution at all times. After interband excitation, carriers rapidly accumulate at the bottom of the conduction band, resulting in a short-lived population-inverted state. Using sub 10 fs pulses, we were able to show that the predominant scattering channel during the first 25 fs after interband excitation is impact ionization. Finally, resonant excitation of the EWhile the observed short-lived population inversion may in principle be used to amplify TeraHertz pulses, the presence of impact ionization most likely cannot be exploited for efficient light harvesting as the absence of a band gap in graphene makes electron-hole separation difficult.3C60.The observed transient enhancement of the electron-phonon coupling strength presents a significant step towards dynamical band structure engineering with important consequences for the occurrence and the understanding of light-induced superconductivity in graphene and other carbon compounds, such as K


To develop effective question understanding tools, question corpora semantically annotated for relevant question elements are needed. In this paper, we present a two-part consumer health question corpus annotated with several semantic categories: named entities, question triggers/types, question frames, and question topic. The first part (CHQA-email) consists of relatively long email requests received by the U.S. National Library of Medicine (NLM) customer service, while the second part (CHQA-web) consists of shorter questions posed to MedlinePlus search engine as queries. Each question has been annotated by two annotators. The annotation methodology is largely the same between the two parts of the corpus; however, we also explain and justify the differences between them. Additionally, we provide information about corpus characteristics, inter-annotator agreement, and our attempts to measure annotation confidence in the absence of adjudication of annotations.Consumers increasingly use online resources for their health information needs. While current search engines can address these needs to some extent, they generally do not take into account that most health information needs are complex and can only fully be expressed in natural language. Consumer health question answering (QA) systems aim to fill this gap. A major challenge in developing consumer health QA systems is extracting relevant semantic content from the natural language questions . Problems are the most frequent named entities, while treatment and general information questions are the most common question types. Inter-annotator agreement was generally modest: question types and topics yielded highest agreement, while the agreement for more complex frame annotations was lower. Agreement in CHQA-web was consistently higher than that in CHQA-email. Pairwise inter-annotator agreement proved most useful in estimating annotation confidence.To our knowledge, our corpus is the first focusing on annotation of uncurated consumer health questions. It is currently used to develop machine learning-based methods for question understanding. We make the corpus publicly available to stimulate further research on consumer health QA. Consumers increasingly rely on the Internet for their health information needs . A recenquestion understanding for information retrieval systems. Question understanding can broadly be defined as the task of extracting relevant elements or generating a formal representation of a natural language question, which can then be used to formulate a query to a search engine and retrieve relevant answers  ,they found repot was PROGRESSIVE DISEASE,ACTIVE LUNG TBINTHE RIGHT B2 AND B4 SEGMENS,THE EXUDATIVE LESIONS IS INCREASING WITH SMALL CAVITY.so what we have to do for her is this contages,who is the pople staying with her need to do test ?pls guide me thank u my contact [CONTACT]Most research in biomedical QA has focused on clinical questions asked by healthcare professionals , which acontagious is misspelled, making both sub-questions very difficult to parse automatically. Based on such characteristics, it has been suggested that consumer health QA systems should be designed with considerations different from those underlying QA systems targeting healthcare professionals  in the Raf gene are associated with cancer?). More recently, BioASQ challenge also focused on biomedical QA and presented a corpus of 311 questions, categorized by the type of answers they require: yes/no, factoid, list, and summary ,they found repot was PROGRESSIVE DISEASE,ACTIVE LUNG TBINTHE RIGHT B2 AND B4 SEGMENS,THE EXUDATIVE LESIONS IS INCREASING WITH SMALL CAVITY.so what we have to do for her is this contages,who is the pople staying with her need to do test ?pls guide me thank u my contact [CONTACT]We briefly discussed several approaches to biomedical question representation above: empirical, domain-based, and formal. Our approach to representing consumer health questions can be viewed as a hybrid method. Like empirical approaches, we begin with existing questions and categorize them into a limited number of generic question templates, and, like formal approaches, we create structured representations of the information in these generic questions that we call pecified . A questdo in what we have to do for her. Note that while a THEME argument is required for each frame, we only specify other arguments if they are likely to be relevant for answer retrieval. For example, in the first frame, one could specify woman as an argument with PATIENT role ; however, since gender is unlikely to be significant in answering this particular question, its explicit representation is not required. In this sense, our representation is driven more by pragmatic concerns for question answering than by completeness.The first frame indicates that the information request contains a question about the treatment of active lung tuberculosis, triggered by the verb THEME of a sub-question) and the question types in an information request are known, authoritative answers can be found in 60% of the cases [ACTIVE LUNG TB is marked as the question topic. The annotation of this question is illustrated in Fig Our prior research suggested that when the question topicOur corpus consists of two parts: a set of 1740 questions harvested from consumer health emails received by the NLM customer service between 2013 and 2015 (CHQA-email) and another set of 874 questions harvested from MedlinePlus search query logs (CHQA-web).CHQA-email is an extension of an earlier corpus (CHQ-NER), reported in , and supAs part of the present study, we extended CHQ-NER with 195 additional questions. Furthermore, 2 duplicate questions and 1 non-question request from the original corpus were dropped, bringing the total number of questions to 1740. One hundred fourty six of the new questions come from the set of 300 questions that were considered in a study that investigated whether authoritative answers to consumer health questions can be found in MedlinePlus and other online resources . In thatTHEME, KEYWORD, and EXCLUDE_KEYWORD), and prepared annotation guidelines. We did not calculate inter-annotator agreement for these practice questions and only make available the final, adjudicated annotations.The rest of annotation of this part of the corpus proceeded as follows: first, all annotators annotated 20 questions for the elements under consideration . As the question type categories, we used those that were used in annotating GARD questions . As thisTHEME argument, except when the question type is one that allows multiple THEME arguments, such as COMPARISON.Next, we sliced the rest of corpus (1720 questions) such that each annotator was allocated roughly the same number of questions, and each shared with all the other annotators approximately the same number of questions. Seven annotators were involved, although the additional annotator (Ann7) annotated significantly fewer than the others due to scheduling conflicts. The questions initially allocated to her but could not be annotated were redistributed among four annotators in such a way to ensure that each question was still double-annotated and the questions were fairly distributed. The resulting number of questions annotated by each annotator is given in Table 1 score when one set of annotations is considered the gold standard [We calculated inter-annotator agreement for question triggers/types, frames with and without optional elements, and question topic. Inter-annotator agreement is calculated as the micro-average Fstandard .Considering the length of questions, the average number of annotations per question, and the number of annotators involved, adjudicating this set is a daunting taskwh-words to identify these questions in the query logs: how, what, when, where, which, who, why. We also included queries ending with question marks and those that start with an auxiliary . For each category, we selected 70 most frequent questions as well as 70 random questions. After removing duplicates and near-duplicates using string matching heuristics, we were left with 1180 questions. In contrast to CHQA-email, we did not assess whether these were all legitimate questions or were answerable; instead, we used two labels, NOT_ANSWERABLE and NOT_QUESTION, during the annotation process to mark such questions. Recognizing that our heuristics for removing duplicates may not be adequate, we used another label, DUPLICATE, to indicate such cases.In contrast to CHQA-email, the second part of the corpus, CHQA-web, has been fully annotated from scratch in this study. MedlinePlus provides search capabilities that are geared towards traditional keyword searches; however, a significant number of searches are posed as natural language questions. Such searches often fail. To investigate whether we can address such questions automatically and to create a counterpoint to customer service information requests, we harvested a set of short questions from MedlinePlus search query logs. These questions were asked in the period from May 2015 to April 2016. We looked for In a practice step, the same six annotators annotated and adjudicated 100 of these questions using the guidelines for CHQA-email. During this process, the guidelines were updated: some question types were consolidated, a couple of new types were added, and the types of semantic roles were expanded.1 score. Fourty eight questions were adjudicated as NOT_QUESTION, 203 were deemed NOT_ANSWERABLE and 55 were found to be DUPLICATE, bringing the total number of annotated questions in this part down to 874.Next, four of the annotators double-annotated the rest of the questions . Instead of pairing all annotators, we only paired those with clinical expertise (n=2) with those without (n=2), leading to four annotator combinations. In the end, each annotator labeled 540 questions. Since these questions were relatively short and there were fewer annotator pairs, we chose to adjudicate these annotations. Each pair adjudicated the questions that they both annotated. As in CHQA-email, we calculated inter-annotator agreement using micro-average Fbrat annotation tool [brat. Question topic was specified as an attribute of the relevant named entity.Seven annotators participated in various stages of the annotation, depending on their availability. All annotators had experience in working with biomedical text. Two annotators  have clinical expertise, two are trained as medical librarians , and four have experience in natural language processing . We used ion tool  for all In the rest of this subsection, we provide details about annotation of these semantic layers and highlight the differences in their annotation between the two parts of the corpus.Named entity annotation in CHQA-email essentially follows the principles of CHQ-NER .ANATOMY, CELLULAR_ENTITY, DIAGNOSTIC_PROCEDURE, DRUG_SUPPLEMENT, FOOD, GENE_PROTEIN, GEOGRAPHIC_LOCATION, LIFESTYLE, MEASUREMENT, ORGANIZATION, PERSON_POPULATION, PROBLEM, PROCEDURE_DEVICE, PROFESSION, SUBSTANCE, and OTHER). Nested entities and multi-part, non-contiguous annotations were allowed. Annotation was performed both manually and with assistance from several named entity recognizers [In CHQ-NER, we annotated 16 broad categories of named entities , we added a new named entity type ORGANISM_FUNCTION. We use the UMLS [OTHER in CHQ-NER and identified those that can be considered organism functions. We then automatically relabeled these mentions, originally labeled OTHER, as ORGANISM_FUNCTION in CHQA-email.For the present study, recognizing that many of the entities with the UMLS  definitiORGANISM_FUNCTION and RESEARCH_CUE. The latter, though not common, is a category that was devised to indicate that the consumer is interested in information that goes beyond typical consumer health information.For CHQA-web, we considered 18 categories: 16 categories above in addition to RESEARCH_CUE mentions include new and latest information. In contrast to CHQA-email, we did not consider nested entities for CHQA-web, as the annotators generally found the annotation of nested entities challenging, although such entities can provide a more precise evaluation for named entity recognizers, as illustrated in [Example rated in .The full list of named entity categories used in annotation, with brief definitions, examples, and relevant UMLS semantic types, is provided in Table ACTIVE LUNG TB is the question topic, while there are two topics in the following question in bold: (4)I’d like to learn more aboutmegalocytic interstitial nephritiswithmalakoplakia.Question topic is the central theme  of a question. Each legitimate question is expected to have at least one topic, but multiple topic elements can be annotated, especially in questions about relationships between two diseases or drugs. In the earlier example, anosmia and loss of smell are both legitimate topics. (5)ANOSMIA.This site tells me that ongoing research may solve my problem some day. Please, could I be informed if such result does happen? I live alone, aged 78, and this completeloss of smellhas been my great problem for quite a while.All mentions of the topic term, including synonyms and misspellings, were expected to be annotated. For example, in the following question, synonyms ropinirole HCl as well as repinirole HCl 0.25 mg and ropinirole HCl 0.5 mg were annotated as named entities. With this instruction, annotators were expected to label mentions of ropinirole HCl as the topic, since the dosage/form of this drug is not relevant to the primary question. (6)Ropinirole HCl 0.25 and ropinirole HCl 0.5 mg. Please tell me if these meds are gluten-free.When considering nested entities for question topics, annotators were instructed to label the term as topic at the nesting level that is appropriate for the question. For example, in the following question, both mentions of THEME argument of the question frame.These principles applied to question topic annotation in CHQA-email. For short questions in CHQA-web, based on the practice annotation, we chose not to explicitly annotate question topic, since this almost always corresponded to the General question types: These categories can apply to any type of entity .Problem question types: These categories apply to diseases/conditions, and other problems . A subset of these categories also apply to entities of procedure and medical device types, since problems can arise from their usage .Intervention question types: These categories apply to drugs, supplements, as well as procedures and medical devices .We based our practice annotation of CHQA-email on the 14 question type categories proposed for disease questions in . DiscussWe provide some further details in Table SUSCEPTIBILITY as defined in Roberts et al. [FREQUENCY, INHERITANCE, and SUSCEPTIBILITY. Considering that answers to such questions are likely to be found in the same passages, for annotation convenience and expediency, we merged these types back into SUSCEPTIBILITY in CHQA-web. The consolidated question types are the following: {complication, long_term_effect, overdose, side_effect} →complication{person_organization, support_group} →person_organization{frequency, inheritance, susceptibility} →susceptibility{dosage, storage_disposal, tapering, usage} →usageFor question type annotation in CHQA-web, this scheme was modified in several ways, resulting in a somewhat simplified categorization that consists of 26 classes. First, some question types that do not occur frequently were merged into other categories that can be considered their supertypes. For example, in CHQA-email annotation, we split the question type s et al.  into thrEFFECT to indicate underspecified relations between two or more named entities, and changed its name to ASSOCIATION for clarity. Finally, we added two new question types. DIAGNOSE_ME identifies questions asking for diagnosis from a set of symptoms. For example, this question type was annotated for the following question (the trigger is in bold). (7)my waist turn red like i have hives but it’s not and its not achy or itchy,whatdo u thinkit is?We generalized the question type TIME, identifies questions asking for time/duration of an event. An example is given below. (8)who gets vaccines andwhen?While we specifically avoided annotating such questions in CHQA-email, since they were considered beyond the scope of a QA system provided by a medical library, we added such annotations in CHQA-web to allow further experiments. The other question type, take, rather than can take or if I can take. In the second question, since both reduce and remove can trigger the same question type, the question trigger is taken as reduce or remove. (9)I would like to ask if I cantakeglutathione supplement.fibroadenomas.…taking medicine is help toreduce or removethis?…The annotators were instructed to annotate the minimal trigger expression, except when two equally valid triggers are conjoined. In the first question in Example THEME argument, which corresponds to a named entity. The type of the question frame is inherited from the trigger. Most questions types allow a single THEME argument, with the exceptions of EFFECT, which allows multiple THEME arguments, and COMPARISON, which requires multiple THEME arguments. Restrictions on the type of named entities that can fill the THEME role are specified in Table In CHQA-email, question frames were annotated using the 17 named entity categories and 33 question types discussed above. A question frame in this context minimally consists of two elements: a trigger indicating the question type and a INTERACTION still required at least two THEME arguments. Two additional question types, DIAGNOSE_ME and TIME, were allowed multiple themes and a single theme, respectively.In CHQA-web, question frame annotation involved 26 question types and 18 named entity categories. We lifted the type restrictions for theme arguments for this part of the corpus, because the annotators found them too restrictive. However, the cardinality restrictions remained in place. For example, THEME semantic roles. In CHQA-email, two generic semantic roles were defined: KEYWORD, and EXCLUDE_KEYWORD. KEYWORD arguments indicate named entities that are expected to be helpful in finding more specific answers to a question. EXCLUDE_KEYWORD arguments are those that should be used to exclude some potential answers. While KEYWORD can be viewed as a generic semantic role and can correspond to more typical semantic roles, such as AGENT, PATIENT, PURPOSE, EXCLUDE_KEYWORD is unique and is meaningful in a question answering context. Examples in Table Two parts of the corpus also differ with respect to non-EXCLUDE_KEYWORD was not considered. The fine-grained roles used are as follows: agent indicates the entity that performs the action involving the theme. In the clinical domain, inanimate objects, such as drugs, procedures and devices could be agents. For example, in the first example in Table keyword arguments can be annotated as agents.locative indicates the location where the action involving the theme occurs . leg in the question parkinson’s disease best drugs for legs? can be annotated with this role in a treatment frame.purpose indicates the reason/goal for which the action is performed. eczema in the question how many mg. of zinc daily for eczema patients? can be annotated with this role in a usage frame.patient indicates the experiencer of the action. baby in the question what if a mother quits intravenous drug use at five months what side effect will baby have? (a complication frame).restrictor indicates the context in which the action occurs or a restriction on the type of information being sought. doctor in the question what kind of doctor would i see for calcification? (a person_organization frame).temporal indicates the time at which the action occurs or the duration of the action. 4 years ago in the question is zoster vaccine effective if one had shingles 4 years ago? (an indication frame)research indicates the role of research_cue entities. literature in the question review of literature of cancer stress (an information frame).In CHQA-web, we used fine-grained roles, more in line with typical roles used in semantic role labeling. On the other hand, PATIENT role is likely to be filled with an entity of PERSON_POPULATION type, and the RESEARCH role should be filled with a RESEARCH_CUE entity.A question frame can contain multiple arguments with such optional roles. We did not stipulate any semantic restrictions on the named entities that can fill these roles, even though for example, the RESEARCH to indicate that the person asking the question is interested in information that goes beyond consumer health information. In CHQA-web, as noted above, we specifically annotated terms indicating such information need as named entities (RESEARCH_CUE), in order to allow training of machine learning models that automatically learn such phrases.In CHQA-email, we associated a frame with the attribute When there are multiple mentions of a term that fills a specific role, the annotators were instructed to use the mention that is syntactically linked to the question type trigger, if possible. Otherwise, they select the closest mention that precedes the trigger. Similarly, if there are multiple potential triggers, annotators were expected to select the trigger that is syntactically related to the theme or is the trigger closest to it.(10)questions about the effect of L-Leucine.Hello,do you have any introduction about L-Leucine on the effect of treat cancer?If so,could you kindly tell me details?The same question is sometimes asked multiple times in the same information request, especially in long requests. Annotators were expected to annotate all instances of such questions. We also found that some questions can be represented from multiple perspectives. Consider the example below: treatment frame where cancer fills the theme role and l-leucine is an agent A indication frame where l-leucine is the theme and cancer is a purpose (corresponding to is l-leucine indicated for cancer?)An This can be formalized in one of two ways: l-leucine is the more salient entity  and therefore, the frame in which this entity appears as the THEME element  would be preferred. If both entities are equally likely as question topic, we asked the annotators to prefer specific question types over others .Annotators were instructed to annotate a single frame in such cases and to take the question topic into consideration in making a decision. In this question, It became clear early on that adjudicating annotations in CHQA-email would be significantly difficult, due to the number and length of questions, and the size of annotator pool (21 annotator pairs). This is in contrast to CHQA-web with the smaller number of questions, which are shorter on average, and annotators (4 pairs). Thus, we investigated whether we can estimate confidences for the annotations.For this purpose, we used two annotation confidence modeling methods: MACE (Multi-Annotator Competence Estimation)  and anotWe calculate the mean average confidence score for each semantic class .If the item under consideration is named entity or question trigger, it is always considered consistent, as they are atomic and independent.If it is a question topic, it is considered consistent as long as the underlying named entity has already been added.If it is a question frame, it is consistent as long as its trigger and all its arguments have already been added.If the confidence score associated with an individual item is higher than the mean average for its class, it is added to the new set of annotations as long as it is consistent with the annotations already added. Consistency is defined as follows: We considered annotations of named entities, topic, triggers, and frames for modeling. To simplify matters, we treated each annotation as an independent data instance for MACE and P&C. Once we modeled the annotation confidences using these methods, we used the confidence scores generated to create a new set of annotations. When incorporating these scores, we assumed that if two annotators agreed on a specific annotation, that annotation was correct. For the rest of the annotations on which two annotators did not agree, we followed the following procedure: We compared these two methods to two other simple methods: the first (AGREE) only considers the subset of annotations that annotators agreed upon for the new set. The second (IAA_RANK) uses inter-annotator agreement results. For this method, we rank annotators by their inter-annotator agreement with others. Then, for a given question, we prefer the annotations of the annotator ranked higher among the two who annotated the question. Note that none of the methods as used here can distinguish cases where both annotators agree but are both incorrect.To evaluate the performance of these methods, we ran experiments on CHQA-web, since an adjudicated set of annotations was available. Based on the results we obtained, we generated several sets of annotations for CHQA-email incorporating these methods. We make these sets publicly available in addition to raw annotations by each annotator.In this section, we present and discuss corpus statistics for each part of the corpus, provide an in-depth analysis of the semantic categories annotated, detail inter-annotator agreement and annotation confidence estimation results.Table Statistics regarding named entities in the corpus are provided in Table DRUG_SUPPLEMENT and DIAGNOSTIC_PROCEDURE terms appear more frequently in CHQA-web, while categories like PERSON_POPULATION, ORGANIZATION, PROFESSION, which often provide background information, appear less frequently than in CHQA-email. The distribution also confirms findings from earlier studies that consumer questions focus mostly on diseases and symptoms .As expected, the distribution of named entity categories and the average number of annotations per question in CHQA-email are similar to those reported for CHQ-NER, which CHQA-email supersedes. For the short questions in CHQA-web, the average number of entities per question is much lower (1.6). PROBLEM category had the highest number of additions (141), and ORGANIZATION had the highest number of deletions (12), though additions/deletions often amounted to a very small percentage of the entities in a given category. Additions included problems like pain in period and drug classes like allopathic medicines.A small number of named entity annotations were added to CHQA-email (Unadjudicated) or deleted by annotators, increasing the number of annotations in this set from 33,494 to 33,747. RESEARCH_CUE category was only annotated in CHQA-web. In CHQA-email, RESEARCH attribute assigned to question frames indicates the same information without explicitly marking the mention. The total number of RESEARCH attribute annotations are 2 and 109 in CHQA-email (Practice) and CHQA-email (Unadjudicated), corresponding to 0.8% and 0.3% of all the entities, respectively, indicating a distribution similar to that of RESEARCH_CUE in CHQA-web.TREATMENT triggers are most common in CHQA-email by a significant margin. In CHQA-web, INFORMATION triggers are most common, though its margin over the next most frequent type, CAUSE, is not as large. There are differences in the distribution of question types between the two parts: most significant are PERSON_ORGANIZATION and PROGNOSIS question types, which appear much more frequently in CHQA-email, and CAUSE, LOCATION, ACTION, and INDICATION question types, which occur more frequently in CHQA-web. Some of these differences can be explained by the question selection methodology. For example, in constructing CHQA-web, the same number of questions were sampled using different wh-words. where questions often contain LOCATION questions, which occurs in large quantity in CHQA-web, but is very rare in CHQA-email. The comparison of frequency of question trigger types in CHQA-email and CHQA-web is illustrated in Fig. Statistics regarding question triggers/types in the corpus are provided in Table TREATMENT, COST, and CAUSE question types have the least uniform distribution , while in CHQA-web, these question types are LOCATION and CAUSE. There is a slight preference for verbal triggers in CHQA-web as compared with CHQA-email. Light verbs, such as get, help, have, and do are the most heterogeneously annotated triggers, indicating 13, 11, 11, and 11 different question types, respectively, in CHQA-email. In CHQA-web, these triggers are have, why, when, and take, indicating a mix of light verbs and wh-words, to some extent reflecting the question selection process for this part of the corpus.We analyzed the distribution of trigger expressions for each question type. A subset of results is provided in Table OTHER_QUESTION and DRUG_QUESTION categories in CHQA-email. After the annotations were completed, we manually categorized the small number of proposed types the annotators had specified. These types are provided in Table DIAGNOSE_ME (which we adopted later for CHQA-web) were among the most frequent. In general, the annotators were not consistent in proposing new question types; therefore, these results should be interpreted with caution. Also note that the annotation guidelines sometimes indicate a particular question type for a specific kind of information request annotated with one of these two types ; however, these were missed by some annotators, indicating potential annotation errors.We asked annotators to indicate their proposed question types when annotating SIDE_EFFECT trigger was annotated with highest number of frames, indicating that consumers were often interested in side effects of multiple drugs in the same question. The comparison of frequency of question frame categories in CHQA-email and CHQA-web is illustrated in Fig Statistics regarding question frame types in the corpus are provided in Table THEME role was approximately 68% in CHQA-email and 46% in CHQA-web. This difference is to be expected, as longer CHQA-email questions often provide more context that may be relevant for answering questions. The most frequently used semantic role in CHQA-web was the unspecific RESTRICTOR. Considering that this role is largely similar to KEYWORD and not very informative, fine-grained annotation of semantic roles  may not provide much advantage over simply using coarse-grained KEYWORD when it comes to question understanding. EXCLUDE_KEYWORD role was rarely used in CHQA-email, suggesting that its utility for answering consumer health questions may be limited, and was later dropped for CHQA-web.Table We only annotated question topic in CHQA-email, as this element almost always corresponds to the theme element of the question in the short CHQA-web questions. On average, 1.8 question topics were annotated per question. The maximum number of topic mentions annotated for a single question was 17.1 score when one set of annotations is taken as the gold standard. Agreement was calculated for named entities, question triggers/types, question topics, and frames. Exact span match criterion was used to compare mentions (named entities and question triggers).We calculated pairwise inter-annotator agreement, using micro-average Ffull frame agreement calculation, we considered the question trigger/type and all semantic roles . For core frame agreement, we considered only the THEME role in addition to question trigger/type. In addition, frame agreement calculation can be based on question trigger mentions or normalized question types indicated by these mentions. Combinations of these options, consequently, led to the following frame agreement types: Full frame w/ trigger, Full frame w/ type, Core frame w/ trigger, and Core frame w/ type. We consider Core frame w/ type as the most relevant for QA, as identifying specific words used to express a question precisely is generally not needed for accurately answering it.We calculated frame agreement in several ways. For We did not consider agreement on practice sets for each part of the corpus. We also did not calculate named entity agreement on CHQA-email (Unadjudicated), since this largely overlaps with CHQ-NER and the inter-annotator agreement for named entities in that set were reported earlier . The resFull frame w/ trigger). Frame annotation in general is challenging as the results indicate, but the agreement is somewhat improved when the agreement only focuses on core frame elements, question type and theme (0.41 with Core frame w/ type). The low agreement for frames is not unexpected, as they can involve many parts, each potentially leading to disagreement. In particular, annotators had difficulty in agreeing on the exact boundaries of trigger mentions (0.37), one of the major components of the frames. When the matching criterion is changed to allow mention overlap (approximate match), average question trigger agreement increases from 0.37 to 0.45, full frame agreement from 0.22 to 0.28, and core frame with type agreement from 0.41 to 0.44. While these figures are still low, they suggest that more strict guidelines may be useful in annotating triggers. In contrast to named entities which are often expressed with specific part-of-speech elements, triggers are lexically and syntactically more heterogeneous and ambiguous, especially in consumer health questions; thus, developing such guidelines may be difficult. Analyzing inter-annotator agreement for triggers of each question type, we find that the agreement is generally higher for triggers of the frequent question types (Table CAUSE (0.60), TREATMENT (0.52), PREVENTION (0.44), and PERSON_ORGANIZATION (0.43), while for the rare types, the agreement is very low or even zero (for SUPPORT_GROUP and OVERDOSE).Inter-annotator agreement in CHQA-email is overall low. The highest agreement is for question topics (0.71) and the lowest is for full frame agreement (0.22), which considers named entity and trigger mentions, and all semantic roles  . This isCOST, DIAGNOSE_ME, and COMPARISON, which are relatively rare, as well as TREATMENT, the third most frequent trigger type in this subset (Table TREATMENT).Agreement in CHQA-web is consistently higher than that in CHQA-email by about 0.2 points. Trends are similar for different semantic classes, confirming our basic intuition that annotating these shorter questions is easier, compared to CHQA-email. Agreement for named entities (0.72) is consistent with that reported for CHQ-NER (0.71) , althouget Table . AgreemeThe evaluation results of annotation confidence estimation for CHQA-web are provided in Table The results show that the baseline (AGREE: taking simple agreement of annotators as indication of confidence) already achieves an agreement of 0.79 with the adjudicated results. The precision of 0.99 indicates that a small number of annotations that the annotators agreed upon were discarded later in adjudication. All other methods improve on the low recall of the baseline (0.66), while leading to some precision loss. IAA_RANK uses the ranking of annotators by their average agreement score, and can be based on any of the agreement categories described earlier. Because frame annotations incorporate other atomic annotations , we ran IAA_RANK with two frame agreement results. The first ranks the annotators by their average agreement for full frame with triggers, and the second by their agreement for core frame with types. The latter led to best confidence estimation for CHQA-web (0.844).The results obtained with MACE  and P&C Full frame w/trigger and Core frame w/ type), MACE, and P&C subsets. In addition, we provide MACE and P&C confidence estimations for each annotation. This can enable further research in developing more advanced methods that estimate annotation confidence. For example, the confidence scores generated by MACE and P&C and the output of other simpler methods can be used as features to learn to predict confidence. Confidence scores can also be used in an active learning framework, where the annotator is presented only with the annotations with moderate confidence, reducing adjudication load. Distinguishing high from low confidence items can also benefit training and evaluation: for example, a system can be penalized less for an incorrect response, when the annotation is labeled with low confidence [We ran the same methods on CHQA-email (Unadjudicated) and provide this part of the corpus in several versions: AGREE, IAA_RANK  for a long time, since they were considered outside the scope of our work. More recently, we began considering these questions but annotated them only in CHQA-web. A large portion of the corpus has not been adjudicated due to personnel/time constraints, which may limit its usefulness. To address this, we experimented with several methods of annotation confidence estimation and make these estimates available, which could stimulate research in better understanding annotator behavior and automatic adjudication.Our corpus has some limitations. Our conceptualization of question understanding and our goals have evolved over time and the corpus reflects this to some extent. Two parts of the corpus are largely similar, but there are also differences. In most cases, these differences can be reconciled with some automatic processing, as in question types that are merged in the other part of the corpus or non-In ongoing and future work, we plan to expand this corpus on several other semantic dimensions. For example, we are currently normalizing named entities in our corpus by mapping them to the corresponding UMLS Metathesaurus concepts. By addressing the lexical variability in named entities, this can improve question understanding. In earlier work, we found that coreference played a major role in consumer health question understanding , and we


Answering questions before a learning episode—“prequestions”—can enhance memory for that information. A number of studies have explored this effect in the laboratory; however, few studies have examined prequestions in a classroom setting. In the current study, the effects of prequestions were examined in an undergraduate course in chemical engineering. At the start of several class meetings, students were provided with a prequestion to answer about the upcoming lesson, and then were asked to provide ratings of confidence in their answers, familiarity with the content in the prequestion, and how much of the assigned reading they had completed. At the end of class, students were given the same question again (postquestion), along with a different question from the same lesson (new question). On a quiz at the end of each week, students were given the postquestions and new questions again, in addition to never-before-seen questions (quiz-only questions) from the same lessons. Performance on questions at the end of class revealed no difference in performance for postquestions vs. new questions. Although weekly quiz performance revealed an effect of retrieval practice—superior memory for material tested at the end of class (postquestions and new questions) compared to material not tested (quiz-only questions)—there was no difference in weekly quiz performance on postquestions vs. new questions. These results suggest that retrieval practice is beneficial to learning in the classroom. However, prequestions do not appear to enhance learning, nor to enhance the effects of retrieval practice.The online version of this article (doi:10.1186/s41235-017-0078-z) contains supplementary material, which is available to authorized users. An important question in education is how to improve students’ learning of course concepts. Asking students questions over what they are learning—i.e., retrieval practice—is an effective way to enhance retention of concepts. However, cognitive psychology research has not always provided clear and concrete recommendations for how instructors can incorporate this technique into their teaching. The current study shows that “clicker” questions administered at the beginning and end of class can provide a straightforward and effective way to enhance students’ learning in chemical engineering. In particular, material that was tested at the end of class was remembered 30% better on a later review quiz, relative to material from the same lesson that was not tested. Asking questions at the beginning of class—i.e., “prequestions”—and repeating them at the end of class, however, did not further this benefit. Thus, when instructors are faced with the question of when to incorporate clicker questions into a given day’s class—at the beginning, at the end, or both—the current results suggest that such questions would be best placed at the end of class. These results provide a simple yet effective way that instructors can incorporate retrieval practice into their teaching.testing effect, or retrieval practice , and the other half read each segment without receiving pre-questions . On the final test, the prequestion group typically outperforms the control group , and a modest but significant advantage in experiment 2b . Thus, contrary to the results of laboratory studies where the benefits of prequestioned information over non-prequestioned information have been quite large—e.g., 25% in the study by Richland et al. (d = 1.70)—these effects appear to be much smaller in classroom settings.Indeed, in the 10 years since the publication of the practice guide, the potential of prequestions to enhance learning in educational settings has remained largely unexplored. To date, only one known study has explored prequestions in a classroom setting. McDaniel, Agarwal, Huelser, McDermott, and Roediger   believed to be responsible for these effects are less likely to be engaged. Theoretical discussions of the prequestion effect have included the  possibilities that prequestions stimulate curiosity  class period, students may have difficulty sustaining attention and noticing information that is relevant to a prequestion that was asked at the beginning of class. Even if curiosity to know the answer is high, this rather lengthy duration of time may introduce interruptions in the processing of the prequestion and the ease with which students can remember it and connect it to the relevant information in the lesson.In the current study, we set out to provide additional data on the effectiveness of prequestions in classroom settings. In a college-level course on chemical engineering, students were asked a question at the beginning of several class meetings that pertained to that day’s lesson. At the end of class, they were asked the same question again, in addition to a different question from the same lesson. Consistent with laboratory studies on prequestions, we did not provide students with feedback of the correct answers at the time of the prequestions. The answer to a given prequestion was always contained in the class lesson that immediately followed, and to learn the answer students needed to discover it during class. This was somewhat different from the study by McDaniel et al. , in whicGiven the variability among students in classroom environments, we also explored the potential role of individual differences in the prequestion effect. We collected information about students’ academic achievement ), in addition to their confidence in their answers to the prequestions, familiarity with the information in the prequestions, and how much of the assigned reading they had completed prior to class. Such individual differences have not been examined in any of the known research on prequestions, but could be of theoretical and practical importance. In particular, the idea that prequestions provide a metacognitive reality check that reduces students’ overconfidence could be tested by examining the relationship between students’ confidence in their answers to the prequestions and their later accuracy on those questions at the end of class. A negative relationship between confidence and later accuracy (other factors controlled) might offer some support for this notion, in that students who are less certain about their knowledge of the concept in the prequestion are more likely to learn and retain the answer to that question when it is encountered in class.Abundant research on retrieval practice shows that testing students over material they have encountered boosts memory retention . The study protocol was reviewed and approved by ISU’s Institutional Review Board (IRB). At the beginning of the semester, a researcher visited class and informed students about the study. All students were invited to participate by providing informed consent that allowed their scores on the course-related activities to be analyzed and information about their grade point average (GPA) to be collected for research purposes. Throughout the semester, three students dropped the course and 12 students declined to have their data included in the study, leaving 62 students in the sample.The course was a Material and Energy Balances course for second-year chemical engineering majors. The course covered three content modules: (1) material balances, (2) volumetric properties and multiphase systems, and (3) energy balances. Following the content modules, there were two modules devoted to case studies. The course met three times per week for 50 minutes over a 15-week semester. There were ten class meetings dedicated to instructional activities for each of the three content modules. Other class meetings were dedicated to scheduled exams, review of information following exams, and case studies.The course was structured using the team-based learning instruction method  consisted primarily of hands-on problem-solving exercises or scheduled activities related to exams. Thus, the prequestions took place on days that were well-suited for the acquisition of new information that students needed for later stages of the course.For each of the 15 target class meetings, a set of three questions was selected to align with the content for that day. These questions assessed knowledge of concepts that were included in the assigned readings for each class meeting, although the exact questions themselves were not included in the readings. Each set of three questions contained a prequestion, a new question, and a quiz-only question. The prequestion was asked at the beginning of class and repeated at the end of class, whereas the new question was asked only at the end of class. This allowed us to measure retention at the end of class for prequestioned vs. non-prequestioned information. To explore whether prequestions enhance the effects of retrieval practice, an online quiz was given to students at the end of each week. This online quiz contained both the prequestion and the new question from that week, along with a third question from the same lesson that had not been asked before—i.e., the quiz-only question. This allowed us to explore the delayed effects of questions that had been asked at the beginning and end of class (prequestions), versus questions asked only once at the end of class (new questions), and to compare performance on these questions to performance on questions from the same lesson that had not been asked before (quiz-only questions). Better performance on new questions vs. quiz-only questions would demonstrate the benefits of retrieval practice. Better performance on prequestions vs. new questions would demonstrate that the effects of retrieval practice are bolstered by providing an opportunity to answer the questions at the beginning of class.In this real classroom environment, questions could not be randomly assigned or counterbalanced across question type. Therefore, we ensured that the relevant properties of the questions were controlled to the extent possible. All of the questions were selected from the AIChE Concept Warehouse . The time limit for responding was set so that once the response rate reached 90%, the remaining students had 5 s to input an answer. Most prequestion responses were completed in under one minute. No feedback was provided about the correct answer to the prequestion or the distribution of responses. Immediately after the prequestion, students were presented with individual slides assessing their confidence in their answers, familiarity with the concept in the prequestion, and completion of the reading assignment. The confidence question inquired “How confident are you in your answer?” with response options A) very confident, B) confident, C) somewhat confident, or D) not at all confident. The familiarity question inquired “Which statement best describes your familiarity with the question you just answered?” with response options A) very familiar: I knew this concept before class, B) somewhat familiar: I have seen this concept, but I do not remember the details, C) Somewhat unfamiliar: I think I have seen this concept, but I do not remember it, or D) very unfamiliar: this concept is new to me. The reading assignment question inquired “How much of the reading assignment did you complete prior to today’s class?” with response options A) all of it, B) more than half, C) less than half, or D) none of it. Students answered each of these questions one at a time with individual clickers.A schematic of the procedure is shown in Fig. Near the end of the team-based learning session, teams reported their answers to the class and the instructor provided closing remarks to summarize and conclude the team activities. During the last segment of the class period , the instructor presented the prequestion again, with the answer choices arranged in a new, unique order that was different from what students saw at the beginning of class. After students answered the question with their clickers, the confidence question was presented in the same format as at the beginning of class. After all confidence responses were collected, the instructor presented the correct answer to the prequestion and provided a brief explanation. Next, the new question was presented, followed by the confidence question. After all responses were collected, the instructor presented the correct answer to the new question and provided a brief explanation.The slides for the prequestion and new question sequence were kept in a separate file from the slides used for the lesson. Although students were provided access to the lesson slides outside of class, they were not directly provided with the questions that were used as prequestions and new questions. Aside from the class period in which these questions were administered, students were provided with access to these questions again only on the weekly quizzes.Each week except for those that contained exams, students were required to complete a weekly quiz on the online course management system (Blackboard Learn). This quiz contained the prequestion, the new question, and the quiz-only question, in that order, for any of the class periods during that week that implemented the prequestion routine. The answer choices for the prequestion and the new question were arranged in an order that was different from any previous order in which they appeared (when they were presented in class). Additional questions designed to be more challenging were included in the weekly quiz, at the instructor’s discretion, and these other questions  appeared after the prequestion, new question, and quiz-only questions. The quiz was released to students immediately after the last class meeting on Friday of each week, and was due by Sunday at 10 pm.The online quizzes were not graded for correctness. Students received one point per answer for the first attempt for every quiz question that they answered by the deadline. Once a student submitted the quiz, they received a performance report that showed each question, their response to each question, the correct response to the question, and feedback from the instructor to explain the reasoning behind the correct answer. Once a weekly online quiz was released, it remained available to students for the duration of the semester and students could return to the quizzes as often as they wished to review the content. For data analysis purposes, we assessed quiz performance on only the first attempt at answering the prequestions, new questions, and quiz-only questions.The first of the 15 target class meetings (during week 1) was used as a practice opportunity. During this meeting the instructor familiarized students with the in-class question procedure, ensured that clickers were working properly, and reminded students to complete the first weekly quiz before the deadline. Data from this practice session were not analyzed, leaving 14 class meetings (beginning in week 2) that were entered into the analyses.Students’ responses to the prequestions, new questions, and quiz-only questions were scored as either correct or incorrect. Students’ responses to the beginning-of-class questions assessing confidence, familiarity, and reading were re-coded  to numeric ordinal responses  such that higher numbers corresponded to greater degrees of confidence, familiarity, and reading. For each student we calculated the proportion of questions answered correctly on both the in-class questions and the online weekly quiz questions across the 14 target lessons. We also calculated students’ mean confidence, familiarity, and reading ratings across these lessons.01 is used to express the probability of the data given the null hypothesis (model 0) relative to the alternative hypothesis (model 1). Jefferys , we also report the Bayes factors for all nonsignificant comparisons. Unlike NHST that does not allow one to measure support that there is a true null effect, Bayes factors allow one to provide evidence for one model relative to another model. In this case, model 0 is the null hypothesis that there is no difference between conditions, and model 1 is the alternative hypothesis that there is a difference. The notation BFJefferys  providesFor in-class questions, data were included for a given lesson only if students completed all three questions for that lesson—the prequestion at the beginning of class, the same question repeated at the end of class (referred to here as the postquestion), and the new question appearing only at the end of class. Across students, the average number of lessons  that received responses on all three questions was 11.37 (standard deviation (SD) = 2.48). Three students did not provide enough responses to contribute data for the three-question set associated with at least one lesson, resulting in 59 students entered into the following analyses.For each student, we calculated the proportion of questions answered correctly out of the lessons for which that student responded to all three questions. Given differences in the overall mean difficulty (from the AIChE Concept Warehouse) across prequestions  and new questions  associated with these lessons, we entered question difficulty as a covariate in the analyses comparing question types.t(58) = 4.01, p < 0.001, d = 0.74), reflecting some prior knowledge of the material at the time of the prequestions.Figure t(58) = 4.50, p < 0.001, 95% confidence interval (CI) , d = 0.65). However, there was no significant difference in performance at the end of class on the postquestions vs. the new questions (t(58) = 1.52, p = 0.13, 95% CI , d = .35, BF01 = 3.20ts < 1.43, ps > 0.15, BFs01 > 3).Comparison of performance on prequestions vs. postquestions indicated that students improved significantly at answering the same question from the beginning of class to the end of class  receiving responses to all of these questions was 10.51 (SD = 2.65).t(58) = 7.04, p < 0.001, d = 0.66. At the end of class, confidence ratings were also higher for postquestions compared to new questions  = 8.92, p < 0.001, d = 0.88).On average, students found the prequestions to be somewhat familiar, with an average rating of 3.11 (SD = 0.43). The average rating for out-of-class reading was 3.06 (SD = 0.66), indicating that students completed just over half of the assigned reading per lesson. Confidence ratings increased significantly from prequestion  to postquestion : R2 = 0.20, F = 2.64, p = 0.034). The only predictor to emerge as significant was prequestion accuracy  = 0.14, p = 0.001), indicating that students were more likely to answer the postquestion correctly if they had answered the prequestion correctly. No evidence emerged that prequestion confidence, or any of the other predictors, was significantly related to postquestion accuracy. Bayesian analyses, however, revealed weak evidence in favor of these null effects (BFs01 < 3).A multiple regression analysis was conducted with postquestion accuracy as the dependent variable, and five predictors: accuracy on the prequestion, confidence on the prequestion, GPA, familiarity with the prequestion, and out-of-class reading. Table ts < 1.30, ps > 0.20), with Bayesian analyses again suggesting weak evidence in favor of the null effects (BFs01 < 3).The same regression analysis was conducted with new question accuracy as the dependent variable. None of the predictors accounted for a significant amount of variance in new question accuracy , had seen once before (new questions), or had never seen before (quiz-only questions). The benefits of retrieval practice would be reflected in an advantage of new questions over quiz-only questions. If prequestions add to this benefit, then weekly quiz performance would be greater for postquestions compared to new questions.Analysis of performance on the weekly quiz questions required that students complete all three questions on the weekly quiz, in addition to the previous in-class questions corresponding to those same lessons. For example, if a student completed the three online quiz questions for a given lesson , these data were only included if the student had also completed the three in-class questions for that same lesson . Across students, the average number of lessons  receiving responses on all six questions was 10.44 (SD = 2.82). Five students did not provide enough responses to contribute data for the six-question set associated with at least one lesson, resulting in 57 students entered into the following analyses.For each student, we calculated the proportion of questions answered correctly out of the lessons for which that student responded to all six questions. Given differences in the mean difficulty (from the AIChE Concept Warehouse) across postquestions , new questions , and quiz-only questions  associated with these lessons, we entered question difficulty as a covariate in all of the following comparisons.t(56) = 6.38, p < 0.001, 95% CI , d = 1.35) and better on postquestions compared to quiz-only questions (t(56) = 4.03, p < 0.001, 95% CI , d = 0.93). However, students did not perform better on postquestions compared to new questions (t(56) = 0.77, p = 0.44, 95% CI , d = 0.16, BF01 = 7.20), indicating that prequestions did not enhance the benefits of retrieval practice. Question difficulty did not significantly influence performance in any of these comparisons .Figure The current study contributes new data on the effects of prequestions in classroom settings. Asking a question at the beginning of class did not enhance learning of that information, relative to other information from class that was not prequestioned. These results are consistent with those of McDaniel et al.  showing Nor do they appear to enhance the effects of retrieval practice. On the weekly quizzes, the benefits of retrieval practice were apparent, with performance highest for material that was tested at the end of class compared to material that was not tested. Performance did not differ, however, for material that was tested at the beginning and end of class compared to material that was tested only at the end.Thus, information that was tested twice was not remembered better on a later quiz, compared to information that was tested only once. This could be due in part to the fact that feedback was not provided at the time of the prequestion. When students are uncertain about the correct answer, the most potent learning experience is likely to come from corrective feedback. Assuming that the learning gains from practice questions would be enhanced when feedback is provided


We report command electro-optical switching on photolithographically-patterned graphene into a high-density electrode pattern for a high-transmission in-plane-switching (IPS) liquid crystal device. A highly-effective liquid crystal photoalignment method is used to maximize the field-driven optical contrast of a prototyped device. A non-contact and low-temperature photoalignment allows delicate surface treatment required for successful processing of graphene layer into an IPS electrode structure. Electro-optic performance of the graphene-based single pixel laboratory IPS prototype demonstrates the application potential of graphene for liquid crystal electro-optic devices with complex and high-definition electrode patterns. Large Young’s modulus and elasticity of atomically thin graphene5 and graphene oxide6 sheets enable extraordinary flexibility7 and consideration of these materials for flexible applications with large deformations8.Development of transparent electronic and electro-optical (EO) devices is a topic of major interest in modern science and technology. Good electrical conductivity, excellent optical transmittance and mechanical flexibility generate continuous interest in graphene as a candidate transparent conductive material for applications in a large variety of optoelectronic and photonic devices including solar cells, light emitting devices, photodetectors, smart windows, and liquid crystal devices (LCD)14. Consideration of graphene and graphene oxide transparent electrodes has been extended for applications in voltage controlled liquid crystal (LC) terahertz phase shifters18, polymer dispersed liquid crystal (PDLC) devices on rigid glass and flexible plastic substrates20, and flexible plastic contact lenses21. These works have demonstrated suitability of transparent graphene electrodes for application in LC EO devices, and at the same time have indicated technical challenges related to mechanical processing and handling of graphene. Nearly all LC EO devices require accurate control of the average orientation of the LC molecules, known as LC director, which is typically defined by the alignment layer deposited above electrically conducting electrodes. Traditionally, the alignment layer is a thin polymeric film which is mechanically rubbed in order to define the direction of desired LC orientation23. Being an atomically thin carbon film, graphene by its nature is much more vulnerable to irreversible mechanical damage which may be caused during the rubbing process compared to much thicker and mechanically more robust films of solid inorganic transparent conductive oxides. Additionally, graphene can be delaminated from the substrate as result of a contact with n-methyl-2-pyrrolidone (NMP) organic solvent which is a major component of all solutions of commercial alignment layer materials such as polyimides (PI)17. A partial solution of the alignment issue for LC on graphene electrodes could be to use a water soluble poly (PVA) as alignment layer12. However, challenges to align the LC using rubbing of either PVA or PI alignment layers require to consider non-contact alignment for the best optical contrast ratio (CR) and EO performance. Such techniques are photoalignment in the case of planar alignment and polymer stabilization for vertical alignment (VA)26.Graphene-based electronic devices have stimulated a continuous interest for application of graphene as a transparent conductive electrode in LC EO devices28. The photoalignment is a well-known technique for LC alignment via an anisotropic surface interaction capable of a large area uniform alignment for display applications31. At the same time, photoalignment has a unique potential for creation of complex LC orientation patterns implementing lattices of topological features for active control of soft matter LC systems32. Until now an azo dye LC photoalignment has only been demonstrated on a non-patterned porous graphene layer for terahertz LC phase shifter16.The photoalignment layer is a thin film of a photosensitive material capable of creating a high-quality orientational ordering of LC director on its surface. The orientation direction of LC director is defined by irradiation treatment with linearly polarized light having a narrow wavelength spectrum and accurately controlled polarization axis with respect to the surface of the substrate34, “fishbone” VA26, or fringe field switching (FFS)36 LCD modes. We produce a high-quality graphene-based single pixel laboratory LCD prototype using commercially available photoalignment material. Direct comparison of prototypes with identical design demonstrates better optical transmittance and equivalent EO performance of IPS device with graphene electrodes compared to reference device with ITO electrodes.In this work we report in details on development of the fabrication process capable of producing a high-resolution patterns of interdigitated electrodes for modern in-plane switching (IPS)38. A single layer graphene film absorbs from 0.6% (at 780 nm) to 1.8% (at 380 nm) of incident light, as shown on Fig. 13.Graphene films were grown on Cu films using a chemical vapor deposition (CVD) process and transferred onto a quartz glass substrates using a thin PMMA film as the supporting substrateThe graphene-based LC device was fabricated from glass substrates deposited with a single layer graphene film in the center, as shown on Fig. In order to be able to apply electric field to the graphene layer the substrate was additionally deposited with ITO conductive layer covering the right and left edges of the graphene film. The center part of the graphene film and the substrate was protected by a shadow mask splitting the ITO layer into right and left parts which are electrically connected only through the graphene layer in the center of the substrate. Afterwards, both ITO and graphene films on the substrate were patterned using a photolithographic process with a positive photoresist and photomask to produce a single graphene IPS pixel with ITO contact bus lines, as illustrated on Fig. Durability of ITO allows to define the alignment direction of LC easily by mechanically rubbing the surface of alignment layer. However, a high sensitivity of graphene to direct mechanical impacts such as rubbing makes it nearly impossible to apply reliably industry’s standard PI alignment layer materials for a high-quality planar alignment of LC in a cell with graphene electrodes. Even a minor mechanical buffing can create substantial irreversible damage of the integrity of graphene layer. Therefore, we create a high-quality planar alignment of the LC via a non-contact photoalignment method using a commercial polymerizable photoalignment material LIA-01 (from DIC Corporation). The solution of LIA-01 contains potentially harmful to graphene NMP solvent. However, a non-contact alignment process and low temperature processing allow gentle coating of inner surfaces of both substrates successfully yielding a properly functioning IPS electrode structure. The orientation of the LC is chosen to be at 15° with respect to IPS electrodes and was optically generated in assembled empty cells producing perfectly matched alignment on both top and bottom substrates simultaneously, which is a distinct advantage of the photoalignment technique.Characterization of manufactured IPS prototypes consisted of quantitative electro-optical measurements on optical bench, and qualitative observations between crossed polarizers and under the polarizing optical microscope (POM). The performance of graphene-based IPS prototype was evaluated by comparing its static transmittance voltage response (TV curve), response time, and contrast ratio (CR) with a reference ITO-based IPS prototype of identical design. A commercial nematic liquid crystal (NLC) mixture TMS83700 (from HCCH) having positive dielectric anisotropy (Δε = 10.6), low birefringence (Δn = 0.1), and low viscosity (γ = 0.063 Pa·s) is used in all characterization experiments with both types of manufactured IPS devices. A He:Ne laser (633 nm) is used as a light source for electro-optic measurements, and a white light for observations between crossed polarizers.39. However, since the thickness of both ITO and graphene electrodes is much smaller than the cell gap thickness and dimensions of the IPS pattern this difference in driving voltage and maximum brightness is indistinguishable in current experiment. Numerical analysis confirms that visible separation between TV curves from ITO and graphene IPS prototypes has the strongest contribution from electro-optic effect of minor structural imperfections in produced IPS electrode patterns, and a weak contribution from a small deviation in the alignment orientation of LC with respect to electrodes in different cells.The TV curve of graphene IPS prototype is consistent thorough multiple switching cycles and matches well with the TV curve of the reference ITO IPS prototype. Normalized TV curves of graphene and reference ITO IPS cells measured with 1 kHz square waveform AC voltage driving match closely, as shown on Fig. The response time of both graphene, and reference ITO prototypes is measured for switching between 1 V and 5.66 V corresponding to the state of maximum transmittance, and calculated as a change of transmittance between 10% and 90%. Experimentally measured average turn-on switching time of the graphene IPS cell is 20.1 ms, which is 10.7% smaller compared to the reference ITO cell (22 5 ms). The average turn-off switching time of graphene IPS cell is 23.8 ms, and is 4.4% larger compared to the ITO IPS cell (22.8 ms). The liquid crystal surface anchoring strength on graphene substrates is comparable to that of ITO substrates manifested by the negligible difference in response times of the two cells as shown in Fig. γ, cell gap thickness d, and twist elastic constant K22, and analytically can be approximated as follows34:K22 = 6.8 pN for NLC mixture TMS83700 demonstrates that observed difference in turn-off response time corresponds to a difference in the cell gap thickness of 2.2%. Empty prototype cells had a small deviation of the cell gap thickness of up to +/− 0.2 µm or +/− 5.7%. Therefore, we conclude that the response time of graphene and ITO IPS prototypes is different by no more than the accuracy of the experimental measurements.The turn-off switching time of IPS LC device depends on rotational viscosity of the LC Micrographs and photographs of the graphene IPS prototype cell between crossed polarizers are shown on Fig. Inactive electrodes of graphene IPS pattern illustrate practical challenge for processing of graphene into a high-resolution electrode patterns. Narrow and long electrode lines made from graphene are more susceptible to mechanical damage during photolithography and other processing compared to more robust ITO. Current results demonstrate a successful fabrication of a high-resolution millimeter-scale IPS pattern out of monolayer graphene which is able to sustain processing required for LC devices. However, further optimization of fabrication process is required to improve the yield of photolithography and defect free alignment.In conclusion, we have demonstrated graphene-based single pixel laboratory prototype of an IPS LC device. Photolithographic patterning of single layer graphene into a high-density electrode pattern and a high-quality photoalignment allow efficient electro-optical switching of the liquid crystal. The static voltage response curve and response time of the fabricated graphene-based single pixel laboratory IPS prototype is confirmed to be equivalent to a reference ITO IPS prototype of the same design. Identical electro-optic performance and better optical transmittance of graphene compared to ITO make our findings useful for development of patterned graphene electrodes with a complex shape for displays and other liquid crystal based electro-optical devices on rigid and potentially flexible substrates.4 (~50 Sccm) and H2 (~20 Sccm) as precursor. To transfer graphene, a thin layer of PMMA  was spin coated on Cu foil at 2000 RPM for 1 min. as a carrier layer. The backside graphene was etched by O2 plasma . Then the film (PMMA/Graphene/Cu foil) was left in (NH4)2S2O8 (0.1 mol/L) water solution to etch Cu foil, and subsequently washed in DI water several times to clean the surface. PMMA/Graphene film was next scooped onto target quartz substrate. After the film was completely dry and tightly attached onto the substrate, the whole sample was soaked in acetone to dissolve PMMA. As a result, graphene film was transferred onto quartz substrate.Graphene was grown on Cu foil  at 1000 °C in a low pressure (~500 mTorr) CVD furnace with CH–6 Torr) by Ar gas plasma (8 mTorr) with addition of O2 gas (0.44 Sccm). Magnetron power delivered to sputtering target was 500 W. The thickness of deposited ITO layer was ~10 nm and sheet resistance in the range of 125 Ω/□ − 290 Ω/□. Commercial ITO-coated soda-lime glass used in transmittance study has the thickness of ITO layer of ~25 nm and sheet resistance of 80 Ω/□. The thickness of graphene layer verified by the AFM study is not more than 4 nm, and the resistance of the plain graphene film measured through ITO contacts was in the range 4–15 kΩ, which is consistent with other literature9.The ITO layer was deposited on the quartz glass substrates with graphene films by a pulsed DC magnetron sputtering method. Before sputtering graphene substrates were rinsed with deionized water, isopropanol , and dried in air oven at 90 °C during 10 min. The reactive ITO sputtering was performed in a vacuum chamber  in Oxford 80Plus etching machine. Substrates patterned by both etching steps were stripped from photoresist by rinsing with acetone , and then were rinsed with methanol , dried in air oven at 90 °C during 10 min. Both graphene and ITO substrates were coated with a commercial photoalignment material LIA-01 (DIC Corporation). Alignment layer was deposited by spin coating from a solution with ~2 wt.% of solid material , and dried on a hot plate 85 °C during 3 min. The IPS prototypes cell have cell gap thickness of 3.5+/− 0.2 µm controlled by the diameter of particle spacers dispersed in an optical adhesive  glue seal. Assembled IPS prototype cells were exposed to linearly polarized UV light  at a room temperature (21 °C) to generate a high-quality unidirectional planar alignment of LC molecules on the surface of both substrates. The LC is aligned normally to the polarization direction of the UV light and at 15° with respect to direction of patterned IPS electrodes.The ITO sputtered substrates were cleaned for a second time with isopropanol , and dried in air oven at 90 °C during 10 min. Both wet and dry photolithography steps were performed with a positive photoresist S1818 . The substrates for IPS prototypes were spin coated with a diluted photoresist solution  and dried on a hot plate at 95 °C during 1 min. Photoresist coated substrates with graphene and supplementary ITO layers were exposed to a UV light through a Chromium shadow mask and developed in a 2.5% water solution of tetramethylammonium hydroxide. Opened areas of photoresist pattern were exposed to an etching acid solution in order to etch the ITO layer, and later dry etched with O