Considerable clinical heterogeneity in idiopathic pulmonary fibrosis (IPF) suggests the existence of multiple disease endotypes. Identifying these endotypes would improve our understanding of the pathogenesis of IPF and could allow for a biomarker-driven personalised medicine approach. We aimed to identify clinically distinct groups of patients with IPF that could represent distinct disease endotypes.We co-normalised, pooled and clustered three publicly available blood transcriptomic datasets . We compared clinical traits across clusters and used gene enrichment analysis to identify biological pathways and processes that were over-represented among the genes that were differentially expressed across clusters. A gene-based classifier was developed and validated using three additional independent datasets .−5).We identified three clusters of patients with IPF with statistically significant differences in lung function (p=0.009) and mortality (p=0.009) between groups. Gene enrichment analysis implicated mitochondrial homeostasis, apoptosis, cell cycle and innate and adaptive immunity in the pathogenesis underlying these groups. We developed and validated a 13-gene cluster classifier that predicted mortality in IPF (high-risk clusters vs low-risk cluster: HR 4.25, 95% CI 2.14 to 8.46, p=3.7×10We have identified blood gene expression signatures capable of discerning groups of patients with IPF with significant differences in survival. These clusters could be representative of distinct pathophysiological states, which would support the theory of multiple endotypes of IPF. Although more work must be done to confirm the existence of these endotypes, our classifier could be a useful tool in patient stratification and outcome prediction in IPF. The clinical course of idiopathic pulmonary fibrosis (IPF) is highly heterogeneous, which has prompted speculation that the disease may consist of multiple ‘endotypes’.Gene expression profiles could be used to identify these endotypes but previous studies have been limited by sample size, ability to validate and clinical interpretation.By combining and clustering multiple gene expression datasets, we identified three distinct clusters of patients with IPF with significant clinical differences between groups, as well as differences in gene expression that implicated mitochondrial homeostasis, apoptosis, cell cycle and innate and adaptive immunity.We went on to develop a 13-gene cluster classifier that was able to predict mortality in two validation cohorts of patients with IPF.Our findings support the hypothesis of multiple endotypes of IPF and highlight distinct underlying biological mechanisms that could inform a precision medicine strategy for IPF.Idiopathic pulmonary fibrosis (IPF) is a complex, ultimately fatal disease, characterised by progressive scarring of the lungs, with a median survival of 3–5 years postdiagnosis.Disease endotypes are subtypes of a disease as defined by a particular pathophysiological mechanism. It has been speculated that distinct endotypes of IPF exist,Transcriptomic data can be used to define disease endotypes, as similar transcriptomic profiles in affected individuals may reflect common underlying biological mechanisms. Previous transcriptomic analyses of patients with cancer have been particularly successful in defining clinically significant patient subgroups, which have led to improvements in treatment.In this study, we aimed to conduct the largest unsupervised clustering analysis of available transcriptomic datasets to date, with independent validation, to identify clinically distinct groups of patients with IPF. We hypothesised that these groups could represent individuals with different endotypes of IPF. Rather than undertake single dataset analyses, we co-normalised and pooled multiple datasets together to increase the sample size and enhance statistical power. Additionally, we used classification to develop a method to accurately assign additional individuals with IPF to one of these groups. This classifier displayed the ability to predict survival in IPF and so we then compared the performance of our classifier in independent validation datasets to a previous method of outcome prediction in IPF.The design of our study is shown in 10.1136/thoraxjnl-2021-218563.supp1Supplementary dataAs the discovery datasets originated from different studies and the transcriptomic data were collected using varying platforms, there would have been considerable technical  differences in gene expression between them. As such, the discovery datasets required adjustment before they could be combined and clustered. We co-normalised the discovery datasets using the COmbat CO-Normalisation Using conTrols (COCONUT) method,We used R V.3.4.0 and the Combined Mapping of Multiple clUsteriNg ALgorithms (COMMUNAL)2 tests for count data, analysis of variance for non-skewed continuous data, Kruskal-Wallis tests for skewed continuous data and survival analysis methods for time-to-event data . For this we used as many of the genes in their signature as were present in the validation datasets. Similarly, each subject was assigned into one of our discovery clusters, which were each classed as low risk/high risk based on the discovery stage findings. Survival analysis methods were used to determine which method performed best at predicting survival  across clusters (p=0.009). Subjects in cluster 1 had a similar median predicted DLCO to those in cluster 3, whilst subjects in cluster 2 had the greatest median predicted DLCO, indicating that these individuals had relatively preserved lung function. Additionally, there was a significant difference in average score from the gender, age and physiology (GAP) index for IPF mortality (p=0.006),With all studies combined and unclustered individuals removed , there wGene enrichment analysis revealed that cluster 1 was significantly enriched for biological mechanisms relating to metabolic changes, including electron transport and cellular respiration . ClusterWe used the pooled, co-normalised gene expression data for all 196 subjects who were successfully clustered in the discovery analysis to train a gene expression-based cluster classifier . The claCO, although the difference in DLCO between validation clusters was not statistically significant (p=0.069). Cox PH models (−5). However, the difference in survival over time between clusters 1 and 3 was not statistically significant .There were 194 subjects with IPF across the three validation cohorts. Expression levels for all 13 genes used in the classifier were available in all three validation cohorts. We used the classifier to assign each individual to a cluster and compared phenotypic traits across clusters . As in tH models  estimateet al to predict outcome in IPF using a 52-gene signature.et al were present in the GSE27957 dataset and 50/52 (96.2%) were available in the GSE28042 dataset. Overall, there was 68.3% agreement between the two methods . This model had a C-index  of 0.664 (95% CI 0.590 to 0.737). SAMS , suggesting an improvement in predictive ability. A likelihood ratio test between the two models gave a p value of 0.005, suggesting that the improvement in predictive ability when including the classifier’s risk predictions was statistically significant. The multivariate Cox model containing SAMS’ risk predictions had a C-index of 0.760 (95% CI 0.684 to 0.837), which suggested an improvement over the Cox model containing only age, sex, ancestry, FVC and DLCO, although the likelihood ratio test p value between these two models was not statistically significant (p=0.105).The risk predictions made using the classifier remained statistically significant (p=0.007) after adjusting for age, sex, ancestry, FVC and DLCO , with anBy applying new statistical methods for data co-normalisation and machine learning to multiple publicly available datasets, we identified three clusters of patients with IPF with statistically significant differences in lung function and survival. As the clustering in this study was undertaken independently of clinical data, yet significant differences in clinical traits were observed between clusters, this suggests that they may be representative of distinct and clinically relevant endotypes of IPF.In this study, we used datasets in which the gene expression had been measured from whole blood samples. However, as IPF is a lung disease, characterised by damage to the alveolar epithelium, patterns of gene expression identified in blood may not reflect the underlying pathology of the disease and may instead reflect downstream effects or the presence of confounders, such as secondary infections or treatment effects. Nonetheless, blood is more accessible than a lung-specific tissue/cell type and the expression of a gene in blood is often a significant predictor of the expression of that gene in lung.The genes that were most differently expressed in subjects in cluster 1 were significantly enriched for biological mechanisms related to electron transport and cellular respiration. Recent findings appear to suggest that metabolic dysregulation could be a contributing factor to fibrosis, although its role is not yet fully understood.Among the biological pathways that were significantly enriched for cluster 2 were pathways related to apoptosis and cell cycle. It has been previously reported that apoptosis is increased in alveolar epithelial cells of patients with IPF but decreased in myofibroblasts,Cluster 3 was enriched for terms related to the immune system response. The role of the immune system in IPF has been controversial in the past; failed immunomodulatory therapies in IPF, some of which have led to worse outcomes, have led to speculation that certain immune responses are protective while others are harmful.−5), which was consistent with the findings in the discovery stage (p=0.008). In addition, while survival information was not directly available for the individuals in cluster 1 in the discovery stage, the significantly low average DLCO and high average GAP score for the individuals in that cluster is consistent with the poor survival that was observed for cluster 1 in the validation stage. As the classifier appears to have the ability to assign subjects who are at a lower risk of death into cluster 2 and the subjects who are at a greater risk of death into the other two clusters, it could potentially be used to predict survival in IPF.The 13-gene expression-based cluster classifier was able to assign the subjects with IPF from the validation datasets to clusters with statistically significant differences in survival between clusters 2 and 3  allowed for three datasets to be combined,There were several limitations to this study. First, as we relied on the use of publicly available data, some clinical variables were relatively underpowered due to missingness within the data or having not been reported in all studies. In particular, survival information was only available in one of the three discovery cohorts and two of the three validation cohorts, which may have limited our ability to clinically distinguish clusters 1 and 3 in terms of survival. In addition, we lacked detailed data for clinically significant traits such as patient reported outcomes, lung function decline over time and the incidence rate of acute exacerbations. Additionally, we did not possess information regarding the background therapy of the subjects with IPF. However, for the three cohorts with survival data available, we were able to glean from the original papers that the patients with IPF were either treatment-naïve populations (GSE93606) or that there were only a small proportion that were receiving immunosuppressive therapy at the time of the blood collection (GSE27957 and GSE28042). In addition, these populations were not given anti-fibrotics and so treatment effects are unlikely to have been driving the large differences in survival that were observed between clusters. These limitations highlight the need for a single large prospective study on this topic with more comprehensive phenotyping.A further weakness of our study is that each participating cohort of subjects with IPF was subject to survival bias, as only subjects who survived long enough to enrol into each study could have contributed their transcriptomic data to it. This could have restricted the level of heterogeneity of IPF that we were able to capture in the study and limited the generalisability of our findings.Additionally, COCONUT makes the assumption that the healthy controls across the different studies came from the same statistical distribution and so all differences between healthy controls across studies must have been due to non-biological variation. This means that any large differences in confounding factors between the groups of healthy controls would have restricted the efficacy of the co-normalisation. However, there were no significant differences in age (p=0.187) or sex (p=0.477) between the healthy controls across the three studies.If the clusters identified in this study do truly represent endotypes of IPF, it may be worth speculating about the nature of these endotypes. As IPF is a complex disease, with many known common genetic and environmental exposures, it is unlikely that it would behave under a traditional discrete endotype model and instead more likely that it would behave under a more complex model, such as the palette model described by McCarthy.To conclude, these results could support the hypothesis of multiple endotypes of IPF as there appear to be at least two clinically distinct groups of patients with IPF that can be identified through cluster analysis of transcriptomic data. As these clusters were defined using expression from groups of genes that were significantly enriched for many different biological pathways and processes, they could be representative of distinct pathophysiological states. Additionally, a classifier with the ability to assign additional individuals with IPF to one of the clusters was developed. With further development, this classifier could be a useful tool in outcome prediction in IPF as well as helping us gain a better understanding of the underlying biological processes that may be driving the observed differences in survival.


Sarcoidosis is a granulomatous disease of unknown origin with multisystem involvement. It mainly affects the lungs and intrathoracic lymph nodes, featuring noncaseating granulomas . The cliCalatroni et al. recommended systematic screening of renal function, including serum creatinine, urine analysis, and serum calcium levels. A kidney transplant is considered an acceptable option for patients with uremic sarcoidosis, and complications seem to be similar to those associated with a kidney transplant for other types of renal failure.The diagnosis of sarcoidosis is formulated by multidisciplinary evaluation of clinical, radiological, and immunological findings , especiaSedki et al. suggest lung imaging and measurement of serum ACE concentrations at the initial evaluation of sarcoidosis. However, further investigation is warranted in patients with intrahepatic cholestasis, negative for anti-mitochondrial antibodies, and with liver biopsy evidence of granulomas, especially if these are predominantly lobular. Sedki et al. recommend a primary bile cirrhosis-specific anti-nuclear antibody (ANA) (gp210 and sp100) test in the case of ANA positivity.No specific and sufficiently accurate biomarkers have yet been identified for the clinical management of sarcoidosis. Among diagnostic markers, angiotensin-converting enzyme (ACE)  has a coFauter et al. analyzed this complication in 12 patients with histological evidence of hepatic sarcoidosis. They highlighted the ineffectiveness of PH management and/or sarcoidosis therapy in these patients, concluding that a liver transplant could be the best therapeutic option when corticosteroids fail.A major complication of hepatic sarcoidosis is portal hypertension (PH), a prognostic marker associated with high morbidity and mortality. Despite extensive research, the etiology and pathogenesis of sarcoidosis remain largely unknown. Several triggers have been implicated in its development, including autoantigen-specific T cells, antibodies producing B lymphocytes, and autoimmune inflammation. Sarcoidosis is not classified as an autoimmune disorder, although autoimmune components play an important role in its pathogenesis.Rizzi et al. reviewed the relationship between sarcoidosis and autoimmunity, highlighting the role of humoral immunity in its pathogenesis. Although patients with sarcoidosis are inclined to develop specific autoantibodies, this is postulated to occur by molecular mimicry. In susceptible individuals, it occurs when there is a similarity between a foreign and a self-peptide, which promotes the activation of autoreactive T and B cells. Clinical improvement with anti-CD20 monoclonal antibody therapy, observed in some refractory cases of sarcoidosis, supports this argument. B-cell depletion has provided insights into the importance of autoimmunity in this immunological context. Several pathogenetic pathways and genetic predispositions are common to autoimmune diseases. The review article of Malkova et al. describes a genetic predisposition to chronic progressive sarcoidosis that prevents antigen elimination and promotes autoimmune inflammation, impairing the immune system or activating autoimmune disorders. Fibrosis is associated with fibroblast activation after the differentiation of leukocytes which release cytokines in a setting of chronic inflammation.Patterson et al. suggested that failure to clear antigens contributes to cardiac sarcoidosis and that the distribution of lymph node activity in the thoracic region differs over time. According to the study, lymph nodes are more likely to be involved in the progression of sarcoidosis in patients with chronic disease.Irrespective of organ involvement, the etiopathogenesis of fibrosis and failure of affected organs in patients with progressive sarcoidosis is still debated. Zhao et al. performed genome-wide miRNA profiling in the lymph nodes of patients with sarcoidosis, comparing it with the profiles of patients with tuberculosis lymphadenitis (TBLN) and demonstrating the high diagnostic value of such profiles for sarcoidosis. MiR-185-5p is still useful in the differential diagnosis of TBLN and sarcoidosis, particularly when patients with the latter disease are in stage I or II.It has been reported that lymph nodes are often involved in sarcoidosis, and a number of studies have been carried out using samples obtained by endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) to explore the pathogenesis and establish diagnostic biomarkers. The purpose of this Research Topic is to present some new experimental findings and updated reviews on organs rarely affected by sarcoidosis and the relationship between autoimmune diseases and sarcoidosis. At the threshold of a new era of personalized treatment, the discovery of new therapeutic targets could help prevent the development of chronic inflammation and tissue damage in these patients.The author confirms being the sole contributor of this work and has approved it for publication.


Several observational studies have found that idiopathic pulmonary fibrosis (IPF) is often accompanied by elevated circulating C-reactive protein (CRP) levels. However, the causal relationship between them remains to be determined. Therefore, our study aimed to explore the causal effect of circulating CRP levels on IPF risk by the two-sample Mendelian randomization (MR) analysis.We analyzed the data from two genome-wide association studies (GWAS) of European ancestry, including circulating CRP levels  and IPF . We primarily used inverse variance weighted (IVW) to assess the causal effect of circulating CRP levels on IPF risk. MR-Egger regression and MR-PRESSO global test were used to determine pleiotropy. Heterogeneity was examined with Cochran's Q test. The leave-one-out analysis tested the robustness of the results.IVW = 1.446, 95% CI 1.128–1.854, P = 0.004).We obtained 54 SNPs as instrumental variables (IVs) for circulating CRP levels, and these IVs had no significant horizontal pleiotropy, heterogeneity, or bias. MR analysis revealed a causal effect between elevated circulating CRP levels and increased risk of IPF (ORThe present study indicated that elevated circulating CRP levels could increase the risk of developing IPF in people of European ancestry.The online version contains supplementary material available at 10.1186/s12931-022-02309-x. Idiopathic pulmonary fibrosis (IPF) is an aggressive, irreversible lung disease marked by scar formation caused by an atypical response to epithelial injury . IPF has risk of cardiovascular disease, psoriatic arthritis, type 2 diabetes, and cancer [IPF was initially considered an inflammatory disease . Later, d cancer –14. And d cancer . Severald cancer  and may d cancer , 17. Intd cancer . In shorMR analysis is a new epidemiological approach that uses genetic variations as instrumental variables (IVs) to estimate the causal relationship between exposure and outcome , 20. GivIn short, the present research intended to assess the causal effect of circulating CRP levels on the risk of developing IPF by a two-sample MR approach using the summary statistics from two large sample genome-wide association studies (GWAS) of European ancestry.https://gwas.mrcieu.ac.uk/ .To identify genetic loci related to circulating CRP levels, we utilized data from a large-scale GWAS meta-analysis of 88 studies  . This GWP = 5 × 10–8 threshold and r2 < 0.001 [To use genetic variation to assess the causal association between exposure (circulating CRP levels) and outcome (IPF), it must satisfy three critical assumptions for Ivs : (i) IVs < 0.001 . Then, wthe MR-PRESSO global test [P < 0.05 was considered significant.A two-sample MR analysis was utilized to examine the genetic relationship between circulating CRP levels and IPF risk. Inverse variance weighted (IVW)  was utilbal test  and MR-Ebal test . Secondlbal test . Additiobal test . We perfIVW = 1.446, 95% CI 1.128–1.854, P = 0.004) (Table P = 0.003), weighted median  and weighted mode  methods also yielded results consistent with the IVW method (Table We obtained 54 SNPs as IVs to assess the genetic association of circulating CRP levels with IPF, and the causal effect of each SNP on IPF is shown in the forest plot Fig. . Then, wPIVW = 0.204, PMR Egger = 0.242, Table P = 0.170, Table P = 0.143, Table Subsequently, we performed sensitivity analyses to assess our results. Firstly, Cochran's Q test results suggested no heterogeneity among IVs (Our study explored the causal effect of circulating CRP levels on the risk of IPF using a two-sample MR analysis. The results showed that elevated circulating CRP levels could lead to an increased risk of IPF. Moreover, sensitivity analysis suggested that our results were robust.Our results provide one piece of evidence for the previously controversial conclusion. A proteomics study by Niu et al. identified that CRP might be a potential specific biomarker for IPF . And anoPrevious studies have identified environmental pollutants, dust, inflammatory responses, and oxidative stress as potential causes of IPF , 37. In Our study has several advantages. Firstly, the present study is the first MR study to assess that elevated circulating CRP levels could increase the risk of IPF and that this association has a causal effect. Secondly, this MR study is based on two large samples of GWAS data from European populations, which provides us with sufficient power to estimate the causal relationship. Thirdly, the MR analysis reveals a long-term effect of genetically determined circulating CRP levels on IPF risk, which is unlikely to be influenced by confounders.Also, there are some limitations to the study. Firstly, our findings are mainly based on participants of European ancestry and may not apply to populations of other races. Secondly, although we did not find the presence of horizontal pleiotropy, there may be residual bias because the exact function of most of these SNPs is unknown. Thirdly, because our study utilized GWAS summary data and not individual-level data, we were unable to stratify our analysis by other factors such as age and gender. In addition, the circulating CRP levels we studied are genetically controlled, so our results reflected the effect of long-term circulating CRP levels on IPF rather than a short-term response to inflammation.Overall, our study indicated that elevated circulating CRP levels could increase the risk of developing IPF. This result probably provides new insight into the understanding of the pathogenesis of IPF. However, further pathological and biochemical studies are needed to investigate further the profound relationship of increased risk of IPF by elevated circulating CRP levels.Additional file 1. The 57 SNPs related to circulating CRP levels at a genome-wide significant level.Additional file 2. The 56 SNPs in IPF GWAS.Additional file 3. IVs for circulating CRP levels.


The ultrahigh EMI shielding property of the LG-4 film originates from the excellent electrical conductivity (6740 S cm−1), as well as multi-layer structure composed of graphene laminates and insulated air pores. Moreover, the LG-4 film shows excellent flexibility and high thermal conductivity (803.1 W m−1 K−1), indicating that the film is a promising candidate for lightweight, flexible thermal conducting film with exceptional EMI shielding performance.As the demand for wearable and foldable electronic devices increases rapidly, ultrathin and flexible thermal conducting films with exceptional electromagnetic interference (EMI) shielding effectiveness (SE) are greatly needed. Large-sized graphene oxide flakes and thermal treatment were employed to fabricate lightweight, flexible and highly conductive graphene films. Compared to graphene films made of smaller-sized flakes, the graphene film made of large-sized flakes possesses less defects and more conjugated domains, leading to higher electrical and higher thermal conductivities, as well as higher EMI SE. By compressing four-layer porous graphene films together, a 14 μm-thick graphene film (LG-4) was obtained, possessing EMI SE of 73.7 dB and the specific SE divided by thickness  shielding effectiveness (SE) are greatly needed. Electromagnetic interference (EMI) shielding protection is the most effective way to prevent EM pollution.4–7 According to electromagnetic theory, the main mechanisms to reduce undesirable EM emissions include reflection of EM radiation by impedance mismatching and absorption of the EM wave energies by dielectric or magnetic loss.8 Electrically conductive materials are reflective shielding materials, which could reflect most of the incident waves, due to their high electrical conductivity. For the portion of waves able to enter shielding materials, absorption loss is the dominant consumption of this EM energy, which depends on the interaction between conducting parts of shielding materials and EM waves.7,9,10With the rapid development of information technology in recent years, electromagnetic (EM) pollution caused by electronic components of wearable and foldable equipments, such as flexible electrodes, storage devices, and smart sensors,4,11–13 For instance, the carbon nanotube (CNT) sponge with a thickness of 1.8 mm shows a high EMI shielding effectiveness (SE) of 54.8 dB and specific SE (SSE) of 5480 dB cm3 g−1 in X band.5 By adding conductive polymer and magnetic loss materials, the graphene/CNT film with a thickness of 0.6 mm can achieve EMI SE as high as 133.22 dB.14Hence, materials with high electrical conductivity, such as metals, carbon materials and conductive polymers, have been applied as effective EMI shielding materials. However, metal shielding materials have the disadvantages of high densities and easy corrosion, limiting their application in lightweight, wearable electronic equipment. With a high specific surface, outstanding thermal and electrical conductivities, carbon nanomaterials have been studied as lightweight and efficient EMI shielding materials.15–17 Moreover, the shielding films are also required to be highly thermally conductive, since electronic components of these devices, such as smart sensors and flexible electrodes,15,18,19 produce significant heat emission during operation. If the heat is not effectively dissipated, it will lead to malfunction of these source electronic devices and reduction of their service life.20,21 Hence, ultrathin, flexible and thermal conducting films with exceptional EMI SE are demanded.However, in some cases like foldable and flexible electronic devices, the thickness of the shielding films is highly restricted.−1 K−1) and ultrahigh electrical conductivity (∼104 S cm−1).22,23 Ultrathin graphene films could absorb and reflect EM waves effectively, which was widely studied as efficient shielding materials.24,25 Moreover, large-scale production of graphene films could be achieved through reduction of graphene oxide (GO) films.26,27 Based on these merits, reduced graphene oxide (rGO) films are promising to be applied as ultrathin thermal conducting films with excellent EMI SE. According to Shen's work, a 8.4 μm-thick rGO film with EMI SE of 20 dB and in-plane thermal conductivity of 1100 W m−1 K−1 was obtained by thermal annealing of GO film at 2000 °C.20 Through improving reduction temperature, higher electrical and thermal conductivities, as well as EMI shielding performance were achieved.13,21 By graphitization of GO films at 3000 °C, Xi et al. has fabricated the foam-like graphene films, with EMI SE up to 65–105 dB.28 The ultrahigh EMI SEs of the foam-like graphene films were attributed to significantly improved electrical conductivity and “expansion enhancement effect” of insulating space layers.Graphene has attracted much attention, due to its outstanding thermal conductivity  films were not mechanically robust enough to meet the requirement of harsh deformations for the wearable and foldable equipments. Cracks were found in the PG films after repeated folding or bending  of 25 680 dB cm2 g−1, as well as outstanding thermal conductivity of 803.1 W m−1 K−1. Moreover, the LG-4 film could endure harsh deformation, with tensile strength of 42.61 MPa and elongation at break of 7.85%, proving to be a promising candidate as thermal conducting materials in wearable and foldable electronic devices.In this work, large-sized graphene oxide flakes and compression of porous graphene films were employed to prepare ultrathin flexible graphene films with ultrahigh EMI SEs and excellent thermal conductivity. Herein, the porous graphene films were fabricated with the combination of casting-evaporation of GO suspension and thermal annealing at 2600 °C. Due to their better thermal and electrical performances, the porous film made of large-sized graphene flakes (PLG) shows an increase in EMI SE of 7.8 dB, compared to that made of smaller-sized graphene flakes. And four 35.6 μm-thick PLG films were compressed into a 14 μm-thick film (LG-4), with an excellent EMI SE of 73.7 dB and specific SE divided by the thickness  at 40 kV. Fourier transform infrared (FTIR) spectra were recorded by a Nicolet 10 spectrometer.Sizes of the GO flakes, and morphologies of the GO and graphene films were investigated by scanning electron microscope . Raman spectra were performed on a LabRAM HR Raman Spectroscopy, and a laser excitation of 532 nm was employed. The elemental compositions of the samples were investigated by X-ray photoelectron spectroscopy . X-ray diffraction (XRD) data was collected with a X'Pert Pro  diffractometer using monochromatic Cu Kα1 radiation  at room temperature. The specific heat capacities were measured from differential scanning calorimeter , and densities of the graphene films were calculated based on sample weight and volume. Diameter of graphene films for thermal conductivity testing is around 25 mm.Static uni-axial tensile tests of the film samples were conducted on a dynamic mechanical analyzer . Two ends of the sample, with a length of about 6 mm and width of around 3 mm, were gripped by tension clamps tightly Fig. S3. All tenS-parameters of the samples, including the reflection (S11 or S22) and transmission (S12 or S21) of a transverse EM wave, were measured by a vector network analyzer  using the wave-guide method in X-band. The standard sample dimensions were 22.86 mm × 10.16 mm, and the tested samples were made by attaching slightly larger graphene films onto a polyurethane (PU) foam substrate. As shown in Fig. S4,A, R and T are the absorption, reflection and transmission coefficients, respectively. SEtotal, SEabs and SEref are the total, absorptive and reflective EMI SE, respectively.The Sizes of graphene oxide flakes, and morphology of the LGO film were observed by SEM, as shown in −1), carboxyl stretching vibrations (∼1725 cm−1), aromatic carbon bonds (∼1615 cm−1), and epoxy and alkoxy bonds (∼1065 cm−1). From The chemical compositions and molecular structures of GO and graphene films with varied flakes sizes are systematically characterized by FT-IR, XPS, XRD and Raman spectroscopy. As shown in The XPS spectra of all GO and porous graphene films is shown in d-spacing around 8.8, 8.5 and 8.3 Å, respectively. After graphitization, the 2θ of graphene films were shifted to 26.42°, 26.52° and 26.58° for SG-1, MG-1 and LG-1, respectively. The shift of 2θ suggests that the GO films were well reduced during thermal treatment. Moreover, d-spacing of these compressed graphene films are slightly greater than that of the natural graphite, attributed to residual structural defects. Due to higher degree of compactness and more ordered structure during assembling of graphene flakes, d-spacing of the LG-1 film (3.352 Å) is a bit smaller than that of the SG-1 film (3.368 Å).−1 and 1592 cm−1 for GO films, corresponding to D band and G band. The peak intensity ratio of D band to G band (ID/IG) is used to characterize structural defects and sp2 hybridized domains. The SGO film possesses the ID/IG of 1.74, which is higher than that of the MGO (1.65) and the LGO (1.53). The results demonstrate that larger-sized graphene flakes has less oxygenated groups and fewer defects, which might lead to higher electrical conductivity and better EMI shielding performance. After graphitization, a new peak appears at 2718 cm−1 for the compressed graphene films, corresponding to the 2D peak, which indicates the formation of graphite structure during thermal treatment.Raman spectroscopy of the GO films and graphene films are shown in EMI shielding performance of the PG films and compressed graphene films made of varied flakes sizes were investigated. As shown in 28 As shown in d, which decreased significantly for LG-1 and LG-2 after compression. As shown in τeff) is defined as the ratio of total transmitted EM waves to incident waves, which could be calculated by the 29Z1 and Z2 are wave impedance of free space and graphene materials, r is propagation constant of free space, and d is average distance between adjacent graphene laminates. On the condition that d ranges from 0 to 100 μm and f is from 8.2 to12.2 GHz, τeff is obviously lower than 1. Therefore, the air pores contribute significantly to shielding performance. With smaller d, compressed graphene films show much lower EMI SE, compared with porous graphene films. On the condition that d = λ/2 and r = j × 2 × π/λ (λ is wavelength), τeff is calculated as −1, indicating that destructive interference of waves might happen. Hence, the excellent EMI shielding performance of the porous graphene films is probably because of the interference among the component waves, which is not related to the number of graphene laminates in the multi-layer structure, but related to the size of air pores. For the PLG films, although insulated air pores could not consume waves directly, these air pores produce multiple interfaces of graphene laminates and air pores, contributing to dispersing extra waves due to the wave interference.A multi-layer model and electromagnetic theories are applied to study the effect of air pores on EMI shielding performance of graphene films.Though the PG films possess higher EMI SEs, the insulated air pores would significantly decrease their electrical and thermal conductivities. Hence, compression of PG films is necessary to obtain ultrathin, flexible thermal conducting films. To improve the relatively low EMI SEs of compressed graphene films, adding the thickness of shielding materials is simple and efficient. Four layers of PLG films were compressed together to obtain the 14 μm-thick LG-4 film, leading to ultrahigh EMI SE of 73.7 dB . And, EMtotal, SEref and SEabs at 10 GHz of the graphene films made of large-sized flakes and the SG-4, MG-4 films were calculated  to 8.1 × 108 Ω (LGO), due to less defects and functional groups. After graphitization, electrical conductivity of the porous graphene films show a remarkable increase, due to defects restoration during thermal treatment. The electrical conductivity of the PSG film is measured as 1170 S cm−1, which increases to 1270 S cm−1 for the PLG film. After compression, electrical conductivity of the LG-4 film increases several times, up to 6740 S cm−1. Compared with graphene films made of smaller-size flakes, large-sized graphene films possess higher electrical conductivity, leading to better EMI shielding performance. Moreover, a small increase of electrical conductivity would lead to a significant decrease in the skin depth of a shield.33,34 And the decrease in the skin depth for the LG-4 film results in the significant enhancement of SEabs. The skin depth (δ) is calculated according to the following equation:σ is the electrical conductivity, f is the frequency and μ is the magnetic permeability. On the conditions that the electrical conductivity of LG-4 is 6960 S cm−1, μ keeps constant to be μ0, and f is at 10 GHz, the calculated value of δ is 6.0 μm based on The electrical conductivity of GO and graphene films were investigated, as shown in δ, EMI SE of LG-1 still reaches to 33.1 dB, due to the consumption of more EM energy by the multi-layer structure of shielding materials. With nearly the same thickness, EMI SE of the LG-4 film is higher than that of the SG-4 and MG-4 films, due to smaller σ for the LG-4 film. Complex permittivity  and loss tangent (tan δ) are analyzed to further study shielding mechanism, as presented in Fig. S9.†Although the thickness of LG-1 film (0.0035 mm) is thinner than the calculated t) has been widely used, when taking density and weight into account. Notably, the-14 μm-thick LG-4 film possesses EMI SE of 73.7 dB, which is higher than the 10 μm-thick copper foil , low density (2.05 g cm−3), ultrathin thickness (0.014 mm), anti-corrosion, high flexibility and easy fabrication.To evaluate lightweight EMI shielding materials, like foldable or wearable electronic devices, the specific shielding effectiveness divided by thickness .20 Compared with that of the SG-1 film, a 27.9% improvement of thermal conductivity was observed for the LG-1 film, which is attributed to the reduced phonon-boundary scattering resulted from more compacted and ordered structure of graphene films made of large-sized graphene flakes with fewer defects. With the size of graphene flakes increasing, acoustic phonons with longer wavelengths are available for heat transfer, leading to higher thermal conductivity.52,53 However, the out-plane thermal conductivities of graphene films are quite low, compared with their in-plane thermal conductivities  and elongation at break (εb) are improved for these GO and compressed graphene films. For example, the σ of the SGO film is 35.6 MPa, and the εb is 1.15%. Those of the LGO film are significantly improved to 52.6 MPa and 1.93%, when the average flakes sizes increased from 5–8 μm to 40–50 μm. This size effects induced enhancement is due to less defects and stronger π–π interaction between graphene flakes for the LGO film.9The ultrathin graphene films in this research have not only superior electrical and thermal properties, also excellent mechanical performance. Representative stress–strain curves of GO and graphene films are shown in σ is observed for all graphene samples, while εb of graphene films is dramatically increased. As shown in εb of 7.85%, which is in good agreement with its high flexibility and foldability. And the SEM images (After graphitization, a slight decrease of M images  reveal tThe above results demonstrate that the LG-4 film is promising to be used as thermal conducting films in wearable or foldable electronic devices, which require high EMI SE, low density, ultrathin thickness, excellent thermal conductivity and high flexibility.t of 25 680 dB cm2 g−1, which is one of the highest among the reported values. The superior EMI shielding performance is attributed to less defects, excellent electrical conductivity and multi-layer structure of the LG-4 film. The LG-4 film also shows high thermal conductivity of 803.1 W K−1 m−1, and excellent mechanical flexibility with a elongation at break of 7.85%. The above results indicate that the LG-4 film shows great potential as excellent thermal conducting films applied in wearable or foldable electronic devices, which require lightweight, high flexibility, thermal conductivity, and efficient EMI shielding performance.In conclusion, ultrathin and flexible graphene films were fabricated with large-sized graphene flakes, which displayed outstanding EMI shielding performance, thermal conductivity and mechanical properties. The 14 μm-thick LG-4 film possesses EMI SE of 73.7 dB and the SSE/There are no conflicts to declare.RA-009-C8RA09376H-s001


Control over charge carrier density provides an efficient way to trigger phase transitions and modulate the optoelectronic properties of materials. This approach can also be used to induce topological transitions in the optical response of photonic systems. Here we report a topological transition in the isofrequency dispersion contours of hybrid polaritons supported by a two-dimensional heterostructure consisting of graphene and α-phase molybdenum trioxide. By chemically changing the doping level of graphene, we observed that the topology of polariton isofrequency surfaces transforms from open to closed shapes as a result of doping-dependent polariton hybridization. Moreover, when the substrate was changed, the dispersion contour became dominated by flat profiles at the topological transition, thus supporting tunable diffractionless polariton propagation and providing local control over the optical contour topology. We achieved subwavelength focusing of polaritons down to 4.8% of the free-space light wavelength by using a 1.5-μm-wide silica substrate as an in-plane lens. Our findings could lead to on-chip applications in nanoimaging, optical sensing and manipulation of energy transfer at the nanoscale. 3 heterostructure by tuning the graphene doping level, which enables partial focusing at deep subwavelength.Polaritonic topological transitions of the isofrequency dispersion contour are observed in a graphene/α-MoO In this context, a collection of different phases in magic-angle bilayer graphene has been achieved by changing its carrier density16. Similar concepts have been theoretically explored in photonics using hyperbolic metamaterials composed of subwavelength structures, such as a periodic array of graphene ribbons17 or a stack of graphene dielectric layers18, in which a topological transition in the isofrequency dispersion contour can occur by changing the doping level of graphene. However, these hyperbolic metamaterials rely on a strong anisotropy of the effective permittivity tensor, which is ultimately limited by spatial nonlocal effects that can hinder a practical verification of this concept.The control of charge carrier concentration by either electrostatic or chemical means has been widely studied as a way to induce phase transitions of different nature, such as structural in transition metal dichalcogenides3) films was explored to control the topology of the isofrequency dispersion contour of phonon polaritons (PhPs) by varying the relative twist angle between the two α-MoO3 layers22. Owing to the in-plane anisotropy of the permittivity within the reststrahlen band from 816 to 976 cm−1, the real part of the permittivity is positive along the [001] direction but negative along the [100] direction24, a property that renders α-MoO3 a natural hyperbolic material supporting in-plane hyperbolic PhPs26. The low-loss in-plane hyperbolic PhPs in α-MoO3 thus emerge as an ideal platform to explore further possibilities of doping-driven and electrically tunable topological transitions in photonics.Recently, a twisted stack of two α-phase molybdenum trioxide (α-MoO3 film covered with monolayer graphene by changing the doping level of the latter. We observed the polariton dispersion contour to vary from hyperbolic (open) to elliptic (closed) on increasing the doping level of graphene, leading to the emergence of a mode dominated by its graphene plasmon polariton (GPP) component propagating along the [001] direction at high doping levels. The nature of the polaritons emerging at high doping in the heterostructure evolved from GPP to PhP when moving from the [001] to [100] α-MoO3 crystallographic direction. In addition, when the vdW heterostructure was placed on top of a gold substrate instead of SiO2, a rather flattened dispersion contour was obtained due to a topological transition. As an application, we have designed an in-plane subwavelength focusing device by engineering the substrate.In this work we have achieved the control of polariton dispersion in a van der Waals (vdW) heterostructure composed of an α-MoO2  components along the [100], [001] and [010] crystal directions satisfy εx < 0, εy > 0 and εz > 0, respectively  under a fixed representative incident free-space wavelength of λ0 = 10.99 μm (frequency of 910 cm−1). As the graphene Fermi energy increases from 0 to 0.7 eV, the opening angle φ of the hyperbolic sectors gradually increases due to a change in the PhP wavelength when varying the dielectric environment, and eventually the dispersion contour changes its character from a hyperbolic (open) to elliptic (closed) shape. Note that the Fermi energy at which this topological transition occurs is conditioned to the appearance of well-defined graphene plasmons along the [001] direction at λ0 when increasing its doping level30. When the substrate was changed from SiO2 to gold, we found flatter dispersion contours , the dispersion contours for the hybrid polaritons on both SiO2 and gold substrates display an elliptic-like shape .Numerically simulated field distributions of hybrid polaritons for different graphene Fermi energies are shown in Fig. ate Fig. , highly ape Fig. . As a co3 heterostructures  is recorded simultaneously with the topography, making it possible to directly map the vertical near-field components of the hybrid polariton wavefronts launched by the antenna  on a SiO2 substrate at the same incidence frequency. The image in Fig. x direction, while fringes around the antenna appear in the y direction, indicating that the dispersion contour has evolved into a closed shape, as shown in Fig. EF = 0.3 eV  match quite well the calculated dispersion diagrams in all cases.To visualize the polariton wavefrontsont Fig.  for both eV Fig. , indicatved Fig.  for sampges Fig.  and simuges Fig.  further 3 determines the influence of the dielectric environment , which should not exceed the skin depth of hybrid polaritons  across the amplitude of the canalization mode give a full-width at half-maximum (FWHM) of around 115 ± 5 nm , as shown in Supplementary Fig. Close to θ with respect to the y direction, we could selectively launch and manipulate hybrid polaritons with different in-plane wave vectors. The launching contribution from our antenna can be decomposed into four parts: two endpoints acting as resonant dipoles and two parallel edges behaving as line dipoles (and assimilated to linear arrays of discrete dipoles).By rotating the long axis of the antenna by an angle EF = 0.1 eV) that, at θ = 0°, the field pattern of the exciting hybrid polaritons exhibits vertical fringes parallel to the long axis of the antenna, dominated by the line dipole contributions generated by the edges. Note that the two endpoint dipoles are not well excited when θ = 0° because the polarization direction of the incident light is not aligned with the long axis of the antenna. We can extract the polariton wave vector from the fringes parallel to this long axis. As θ increases from 0 to 90°, the wavefronts produced by the two endpoints gradually show up and interact with those produced by the edges. In the regime with rotation angles θ ≤ 40°, the distance between adjacent fringes parallel to the antenna edge is reduced from 590 to 380 nm, from which we can obtain the wave vector k perpendicular to the antenna. The extracted wave vectors are shown by red symbols in Fig. θ ≥ 60°  all lie strictly along the x direction  shown in Supplementary Fig. y direction. This focal spot size is less than 1/21 of the corresponding illumination wavelength, thus emphasizing a deep subwavelength focusing effect . This property has allowed us to design a deep-subwavelength device for in-plane focusing of hybrid polaritons, where negative refractive occurs at the boundary between two different substrates. Our study opens promising possibilities to tune topological polaritonic transitions in low-dimensional materials40 with potential applications in optical imaging, sensing and the control of spontaneous emission at the nanoscale18.We have experimentally demonstrated that the topology of the isofrequency dispersion contours for the hybrid polaritons supported in a heterostructure composed of a graphene sheet on top of an α-MoONote added in proof: While preparing this manuscript, two related theoretical and experimental studies on the tuning of highly anisotropic phonon polaritons in graphene and α-MoO3 vdW structures were reported42.3 flakes were mechanically exfoliated from bulk crystals synthesized by the chemical vapour deposition (CVD) method19 and then transferred onto either commercial 300 nm SiO2/500 μm Si wafers (SVM) or gold substrates using a deterministic dry transfer process with a polydimethylsiloxane (PDMS) stamp. CVD-grown monolayer graphene on copper foil was transferred onto the α-MoO3 samples using the poly(methyl methacrylate) (PMMA)-assisted method following our previous report44.High-quality α-MoO46. We designed the gold antenna with a length of 3 μm and a thickness of 50 nm, which provided a high launching efficiency over the spectral range from 890 to 950 cm−1 within the α-MoO3 reststrahlen band. Alternatively, a thicker antenna with a stronger z component of the electric field could be used to launch the polaritons more efficiently in future studies. Note that narrow antennas (50-nm width) were used to prevent their shapes from affecting the polariton wavefronts, especially when their propagation is canalized  on an approximately 350 nm PMMA950K lithography resist. Electron-beam evaporation was subsequently used to deposit 5 nm Ti and 50 nm Au in a vacuum chamber at a pressure of <5 × 10−6 torr to fabricate the Au antennas. Electron-beam evaporation was also used to deposit a 60-nm-thick gold film onto a low-doped Si substrate. To remove any residual organic material, samples were immersed in a hot acetone bath at 80 °C for 25 min and then subjected to gentle rinsing with isopropyl alcohol (IPA) for 3 min, followed by drying with nitrogen gas and thermal baking  was used to image optical near fields. The atomic force microscopy (AFM) tip of the microscope was coated with gold, resulting in an apex radius of ~25 nm (NanoWorld), and the tip-tapping frequency and amplitudes were set to ~270 kHz and ~30–50 nm, respectively. The laser beam was directed towards the AFM tip, with lateral spot sizes of ~25 μm under the tip, which were sufficient to cover the antennas as well as a large area of the graphene/α-MoO3 samples. Third-order harmonic demodulation was applied to the near-field amplitude images to strongly suppress background noise.A scattering scanning near-field optical microscope (Neaspec) equipped with a wavelength-tunable quantum cascade laser  impinged at an angle of 60° relative to the tip axis52. To avert any effects caused by the light polarization direction relative to the crystallographic orientation of α-MoO3, which is optically anisotropic, the in-plane projection of the polarization vector coincided with the x direction  of α-MoO3  is a cover layer, layer 2  is an intermediate layer and layer 3  is a substrate where z is the value of the vertical axis and dh is the thickness of α-MoO3 . The α-MoO3 film was modelled by an anisotropic diagonal tensor diag{εx, εy, εz}, where εx, εy and εz are the permittivity components along the x, y and z axes, respectively. Also, monolayer graphene was located on top of α-MoO3 at z = 0 and described as a zero-thickness current layer characterized by a frequency-dependent surface conductivity taken from the local random-phase approximation model55:EF, the inelastic relaxation time τ and the temperature T; the relaxation time is expressed in terms of the graphene Fermi velocity vF = c/300 and the carrier mobility μ, with e is the elementary charge; kB is the Boltzmann constant; ℏ is the reduced Planck constant; and ω is the illumination frequency.The transfer matrix method was adopted to calculate the dispersion and IFCs of hybrid plasmon–phonon polaritons in graphene/α-MoOrp of the three-layer system admits the analytical expressionrjk denotes the reflection coefficient of the j–k interface for illumination from medium j, with j,k = 1–3; j for a propagation wave vector kp(θ) (where θ is the angle relative to the x axis), which can be expressed as j along the x and y axes, respectively); j along the z axis; and Z0 is the vacuum impedance.Given the strong field confinement produced by the structure under consideration, we only needed to consider transverse magnetic (TM) modes, because transverse electric (TE) components contribute negligibly. The corresponding p-polarization Fresnel reflection coefficient q when the denominator of equation   over a plane 20 nm above the surface of graphene. The boundary conditions were set to perfectly matching layers. Graphene was modelled as a transition interface with a conductivity described by the local random-phase approximation model (see above)57. We assumed a graphene carrier mobility of 2,000 cm2 V–1 s–1. Supplementary Fig. 2 and Au, respectively, at the mid-infrared wavelengths used.We also used a dipole polarized along the Methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41565-022-01185-2.Supplementary InformationSupplementary Figs. 1–31.


ForAlexa, which has two types of forms, Question-And-Answer (Q&A) and Random-Quote. Both these forms allow the user to build intents (an activity that is invoked by a spoken request from the user), but with slightly different functions. The Q&A form is used to compile answers that Alexa will offer in response to an utterance (question), while the Random-Quote extends the interaction between Alexa and the user, based on the questions asked in the first form. ForAlexa also has a help assistant, as well as a manual, which explains all the steps necessary for the design of an intent. This tool allows educators to develop apps quickly and easily for their classes and this type of app could be an alternative to be used for students with special needs, such as the visually-impaired.Intelligent Personal Assistants (IPAs), such as Amazon’s Alexa, are now widely used for an ample variety of tasks, ranging from personal management to education. These tools have shown considerable promise for student-educator interactions, especially at a distance, a potential that has come to the forefront during the ongoing COVID-19 pandemic. Even so, this potential is still underexploited, even in the current scenario. Alexa’s apps are known as skills, which include all the different commands that Alexa is capable of executing. It is important to note, however, that the use of such technology is work-intensive and can be relatively complex. Given this, to facilitate the development of new skills in Alexa, we have developed an online tool that permits the creation of questions and answers, as well as honing the interaction between Alexa and the user. We have named this tool The online version contains supplementary material available at 10.1186/s12052-022-00169-z. Social distancing, quarantine and the isolation provoked by the COVID-19 pandemic have forced us to modify our way of life in many different ways, which may be more or less permanent . Artificial Intelligence is now a global reality . However, Alexa can also be implemented in devices not produced by Amazon  or manually, in the case of devices with a screen. Once Alexa is activated, a specific skill can be activated by voice or by a touch followed by a voice command. When a skill is requested, its content is processed by the algorithms in Alexa’s AI, such as voice recognition, machine learning, and the understanding of natural language.Alexa processes the user’s request by accessing web-hosted services or its own server (Amazon AWS), and then responds to the user Fig. A. This rThe tools would potentially be extremely valuable as auxiliary teaching aids for specific disciplines, in particular for special needs students. Even so, many of the challenges of teaching the visually impaired will still persist (Orsini-Jones ForAlexa) which explains the steps necessary for the construction of teaching skills. We also developed a skill for a specific, undergraduate-level discipline, population genetics (in Portuguese), and at the end of this discipline, we applied questionnaires to evaluate the students’ use of the skill created using ForAlexa .To facilitate the use of this tool by other educators, in particular those that may be inexperienced in programming, we have created a web-based form page .We chose Alexa, the artificial intelligence (AI) platform from Amazon Inc., for the present study, due primarily to the fact that the development of apps on this platform is facilitated considerably by a specific page on the Amazon system, which provides detailed instructions and examples of codes, and an extremely user-friendly interface for the development of apps, i.e., skills (Johnstone and Razavi intent) is an action that attends to a question (utterance) that the user asks Alexa. It is important to note here that a given intent can be activated by questions formulated in different ways. To ensure this, the developer must consider the different ways in which the user may formulate the same solicitation  with the invocation name (Evolution Professor). The intention .https://developer.amazon.com), and create a new skill , informing its name and standard language, as well as the skill model and its host. The user should then create an account and follow the instructions on the ForAlexa homepage. The ForAlexa workflow is shown in Fig. ForAlexa has a help assistant to facilitate the steps outlined below. This assistant is turned on by default, and allows the user to understand basic concepts, what is permitted or not , and even to build questions and answers rapidly, as well as reinforcing the interaction between the user and Alexa.To begin with, the user must register or login to the Alexa Developer Console . These intents are normally related to the invocation name of the skill (LaunchRequest) and its help request (HelpIntent). There are a number of other required intents, but to simplify, the ForAlexa help assistant only teaches how to edit the LaunchRequest (the edition of required intents is explained below).Once logged in, the user must create a repository, which will present a list of required ork Fig. A. Once eintent has been registered  intents or Random Quotes , the corresponding form will be loaded  and the question that Alexa must be able to answer, e.g., “What is Evolution?”  asking the user a new question (reprompt), for example, asking whether the user requires any more information on the topic —“If you want, you can ask me another question, for example, ask me questions on chapter one or any other chapter you like”.If the form is of the Q&A type, the developer must provide the answer that Alexa will provide in response to the utterance, followed by one of two options—(i) finalizing the intent can also be included here. In this case, the form has the option of creating suggestions for random questions on a given subject or class, such as “would you like to ask more questions about evolution?” or “would you like to ask more questions about class one?”. The developer can provide to the random questions the utterances created in the Q&A form.If the developer wishes to include a greater level of interaction between Alexa and the user, the random-quote type of intent is substituted by the random quotes field, where the developer defines how many random quotes the AI will use, with a default value of 2. An introductory phrase (which is not obligatory), which defines the possibilities of interaction with the AI, can be introduced here. The developer then provides the random quotes that will be selected when an intent is activated. In the final step, the developer can provide a phrase to be spoken by Alexa following the random quote. While this phrase is not required, it does amplify the potential for interaction between the AI platform and the user. At this point, the developer can save or update the intent by clicking on the respective button  intent name “QuestionsAboutEvolution” , and (ii) an utterance “Questions about Evolution”, followed by an introduction, such as “You can ask me”, and then all the possible questions on a given subject or class, such as “What is Evolution?”, “What is Microevolution?”, “What is Macroevolution?” and so on. Finally, in After Alexa, the developer may choose a sentence of the Q&A type “If you want, you can ask me another question, for example, ask me questions about Evolution”. When Alexa is asked “Questions about Evolution”, it will select questions (2 by default) randomly from the skill to ask the user, and then ask whether the user wants to ask one of these questions or whether the user wants to hear more questions chosen randomly. In other words, the dialog on a theme or a class will continue.An example of a random quotes intent is the LaunchRequest. Here, the developer should edit the Skill Invocation Name, but not the Intent Name. At Skill Invocation Name, the developer must substitute the default sentence with the name of the skill that the users of Alexa will need to say to activate the skill. This is the new name of the skill  option. In this case, Alexa will continue the interaction, saying, for example, “If you want, you can ask me another question, for example, ask me questions about Evolution?” .The second required intents are registered or updated, ForAlexa revalidates the database  whether an utterance is duplicated in the database. Once the absence of duplications is confirmed, the data are saved, but if a duplication is found, the user is redirected to the preceding screen, which will describe the error that requires correction in step 2 .Once all the content is confirmed, the developer is directed to the next phase Fig. , step 4,ile Fig. E , for example, “Tell me about the Hardy-Weinberg equilibrium”, rather than “What is the Hardy-Weinberg equilibrium?”. In these cases, it is possible to include options in ForAlexa that allow Alexa to answer that it has no content on the topic. Alexa will then ask the user if they are interested in hearing some questions on a specific theme (HelpIntent). If the user says “yes”, the Yes.Amazon intent will direct the user to an alternative question. The random question form can also enable Alexa to present random questions on the subject matter (or class) and then ask the user if they would like to hear more questions. These questions are chosen randomly, but Alexa will eventually include the correct question that the developer included, i.e., What is the Hardy-Weinberg equilibrium? The organization of a skill for educational purposes is presented in Fig. One fundamentally important point in the creation of a skill is the Alexa–user interaction, which must ensure the most effective possible communication between the student and the skill. We bore this in mind when developing This interaction with the user is fundamental, because it permits the user to establish a “conversation” with the IPA and not simply ask questions and receive answers. The idea here is for the IPA to lead the user through the skill . While the skill does contain some more general material, which could be used by anyone interested in the theme, it is structured by the topics presented by the educator (Population Genetics) during the classes.A skill was designed in Once the skill has been presented to the students and its use has been demonstrated, they were free to use the skill as they prefer. It is important to emphasize, however, that the skill does not substitute the adequate study of a topic, but rather, provides a reference base for the principal concepts presented in each class. Given this, we monitored the use of the skill by the students in relation to a given topic, in terms of the questions asked and the specific use of the skill. We also asked the students to explain in their own words what they had learnt from Alexa on a specific topic.Overall, the Population Genetics skill was well-accepted by the students, who mostly gave it the most favorable evaluation Fig. . The stuThe creation of a skill is an idiosyncratic task, and each educator will likely adapt it in some way to the content of their discipline and their own personal style of teaching. In our skill, for example, when Alexa is asked what the Hardy-Weinberg Equilibrium is, after providing the answer, Alexa will ask whether the user would like to know something else , VoiceApps (www.voiceapps.com) or Voiceflow (www.voiceflow.com). While no source code is available for Blueprints and only question-and-answers in English, the latter two tools are potentially interesting, but they have only limited function in free plans.Given this, https://github.com/luanrabelo/ForAlexa). After obtaining the code, the user can import the skill to their developer profile, where its different components, including its questions, answers, and invocation name, can be modified. The user can also use the online form for the rapid development of a skill. Even though the development of a skill can be facilitated, it is important to remember that many different types of problems may arise, such as the need to include special characters that are not permitted. This demands a minimum level of knowledge on the part of the developer in order to resolve problems of this type following the upload of the files to Alexa. In particular, we would emphasize, once again, the need for adequate and comprehensive testing to guarantee the creation of the most functional possible skill, which is not a simple task. For this, we would recommend any developer new to this activity to create some test questions and answers to test the efficacy of both the form and the development area in Alexa. To facilitate this, we have created a document with simple questions and answers, and all the interactions necessary for the adequate completion of the forms .The source code of our skill and the forms are open-access, and can be found at GitHub  question-and-answer and(B) and random quotes types. An intent name (C) and utterance (D) are requiredin both forms, while an answer (E) is only required in the question-and-answertype form. The user can then finalize or manifest their interest in asking Alexaa new question (F) or ask a question (G). In the random quotes mode, the usercan choose how many random questions they would like Alexa to proffer (H), anintroductory phrase, which defines the possibilities of interaction with the AI(I), the random questions (J), add space for more random questions (K), anddefine the final phrase or question that Alexa will say (L).