Both the timing  and amount  of language exposure affect language-learning outcomes. We compared speech recognition accuracy across three listener groups for whom the order (first versus second) and dominance (dominant versus non-dominant) of two languages, English and Spanish, varied: one group of Spanish heritage speakers  and two groups of late onset L2 learners (L1-dominant English/Spanish learners and L1-dominant Spanish/English learners). Sentence-final word recognition accuracy in both English and Spanish was assessed across three “easy” versus “difficult” listening conditions: (1) signal-to-noise ratio , (2) sentence predictability (high versus low sentence predictability), and (3) speech style (clear versus plain speech style). Overall, SHS English recognition accuracy was equivalent to that of the L1-dominant English Spanish learners, whereas SHS Spanish recognition accuracy was substantially lower than that of the L1-dominant Spanish English learners. Moreover, while SHS benefitted in both languages from the “easy” listening conditions, they were more adversely affected by  the presence of higher noise and lower predictability in their non-dominant L1 Spanish compared to their dominant L2 English. These results identify both a benefit and limit on the influence of early exposure. Specifically, the L2-dominant heritage speakers displayed L1-like speech recognition in their dominant-L2, as well as generally better recognition in their non-dominant L1 than late onset L2 learners. Yet, subtle recognition accuracy differences between SHS and L1-dominant listeners emerged under relatively difficult communicative conditions. However, in many immigrant communities , there is a growing number of speakers for whom their L1 is not their dominant language . For thi1 are not fully realized to dominant L1-like levels as SHS age and attend higher levels of education in English. This early, yet unsustained, exposure can then be compared to young adult learners of Spanish (approximately 18–19 years old) who receive several years of late exposure to Spanish . Although the current amount of use for English and Spanish is similar in heritage speakers versus L2 learners, the manner and timeframe in which the languages were acquired are not equivalent. Despite not being symmetrical (as the exposure to Spanish for SHS continues throughout adulthood), comparing L2 learners of Spanish to heritage speakers of Spanish isolates early, yet interrupted, L1 acquisition from exclusively late L2 acquisition.Since age-of-acquisition and language dominance are confounded in the typical L1-dominant bilingual individual, the “switched” dominance of L2-dominant heritage speakers provides a unique window into the separate and combined influences of early versus extended language exposure on speech and language processing. That is, comparing heritage speakers to adult L2 learners of the heritage speakers’ L1 distinguishes early versus late exposure in bilingual listeners, whose levels of knowledge of the target language appear equal, as many heritage speakers place into college-level intermediate language courses for their L1. See  and as aThe current study focuses on L1 and L2 speech-in-noise recognition at the sentence level with a particular focus on acoustic-phonetic and semantic-contextual factors (to be defined and discussed in detail below) that are known to influence L1 and L2 speech recognition: speech style, semantic predictability, and signal-to-noise ratio (SNR). As discussed further below, previous work has shown that some sources of speech recognition enhancement are highly beneficial for L1 speech processing but provide either no benefit or less benefit for L2 speech processing. In this study, we asked which, if either, of a switched-dominance (or heritage) speaker’s two languages would show non-dominant L2-like vulnerabilities in speech recognition, the non-dominant but first-acquired L1 or the dominant but second-acquired L2. The following section outlines the various factors known to influence speech perception.Clear speech refers to a slower, hyper-articulated speaking style that a talker uses in order to aid the listener in speech recognition e.g., . Cross-l2, listeners have been shown to benefit from semantic cues in speech-in-noise processing  has also been shown to affect L1 and L2 listeners differently. Specifically, L1, but not L2ng e.g., . That isng e.g.,  comparedMore recently, The presence of background noise is well known to lower a listener’s accuracy and to raise the effort needed to process speech effectively e.g., , who fouGiven these differences between L1 and L2 processing, the present study questions how signal and semantic-contextual enhancements affect L1 and L2 speech recognition for Spanish Heritage Speakers (L2-dominant) compared to late L2 learners (L1-dominant). We hypothesize that if early language exposure is sufficient for establishing dominant L1-like speech processing routines e.g., , then SHIn order to test the above hypotheses, we administered a speech-in-noise recognition task in both English and Spanish for a group of Spanish Heritage Speakers , as well as a group of L1-dominant English listeners learning Spanish and a group of L1-dominant Spanish listeners learning English. In this task, participants were asked to identify keywords embedded in sentence-final positions for an accuracy score in each language. Using linear mixed-effect models, we then compared the three-way interaction between listener group, signal manipulation , and task language to determine whether, if at all, the heritage speaker group patterned differently from either the L1-English/Spanish learner or L1-Spanish/English learner group. As the results will suggest, while early exposure to both languages played a crucial role in speech processing , heritage speakers typically benefitted from signal enhancements in English, the dominant L2, while being more adversely affected by signal degradation  in Spanish, their non-dominant L1.2.2.1.Participants consisted of three groups: L1-English Spanish learners (L1-E/SL), Spanish heritage speakers (SHS), and L1-Spanish English learners (L1-S/EL). Listeners in the L1-E/SL and SHS groups were explicitly required to have been placed into intermediate Spanish courses at the university level. There was no course placement requirement for L1-S/EL as the majority of this population was graduate students studying at Northwestern University. In each group, there were 12 listeners for a total of 36 listeners . In addition to Spanish course requirements, we established criteria (explained below) to determine which group participants should be placed in based on language acquisition and use. All listeners were compensated $10 an hour for their participation. Before beginning the study, participants were asked to fill out a brief form about their demographic information and language usage. The information about the participants reported below is based on these self-reported fields. Any percentages reported are based on participant group averages. All subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Internal Review Board with approval IRB# STU00200566.L1-E/SL were defined as English L1-dominant speakers learning Spanish as their L2. The average age of L1-E/SL participants was 19.9 years old (standard error = 0.46). This group was dominant in English, as the average age of acquisition (AoA) for English was between 0 and 1 years old. The average AoA of Spanish for this group was 18 years old. Participants in the L1-E/SL group were recruited from intermediate Spanish classes at Northwestern University and on average reported using English 83.5% (standard error = 6.11) and Spanish 8.3% (standard error = 1.86) of the time. As these data indicate, the L1-E/SLs were heavily English dominant.SHS were defined as Spanish L1 speakers who grew up in a Spanish speaking home, but attended elementary school (and beyond) in the USA, where the medium of instruction was English. The average age of the SHS group was 19.7 years old (standard error = 0.36). The average AoA of Spanish for SHS was approximately 0 (birth) and ranged between 3 to 8 years old for English. Critically, SHS are English L2 dominant due to the heavy and prolonged use of English in school and the community. SHS also have fluent reading and writing skills in English based on their formal education in the USA in which the medium of instruction is English, but not necessarily in Spanish as evidenced by SHS participants being placed in intermediate level Spanish courses at Northwestern University on the basis of a Spanish language placement test. Further evidence that SHS are English dominant comes from their self-reported values of language use in both English and Spanish. SHS indicated on average that they used English 74.45% of the time (standard error = 5.1), while reporting that they use Spanish on average 21.58% of the time (standard error = 2.84).The L1-S/ELs were defined as Spanish L1-dominant speakers, who grew up in Latin America and attended high school where the medium of instruction was Spanish. The average age of L1-S/EL was 24.8 years old (standard error = 1.17). The average AoA of Spanish for L1-S/EL was approximately 0 (birth) and the average AoA for English was 11 years old. Evidence that L1-S/EL were clearly Spanish dominant comes from self-reported use of English and Spanish. L1-S/EL reported using English 32.3% of the time (standard error = 6.21), while Spanish use was 55% of the time (standard error = 6.64). All of the L1-S/EL speakers were graduate students at Northwestern University, so it is unsurprising that they would self-report a higher English use (their non-dominant language) compared to the SHS and L1-E/SL Spanish use at the time of testing.Additionally, the contexts in which Spanish and English are used differed across participants. We examined the self-reported contexts in which each listener group used Spanish and English. We found that whereas L1-E/SL and L1-S/EL generally had L2 usage restricted to classroom and colleague settings, SHS reported using Spanish primarily with parents, siblings, friends, and other relatives. These SHS language patterns are consistent with 3, which was given to each participant in both English and Spanish after speech recognition testing. The cloze test consisted of a passage with 40 blanks. Participants were instructed to fill in each blank as best they could to appropriately complete each sentence. The blanks in the cloze tests are designed to target specific syntactic , semantic, and pragmatic features in the language of the test. The cloze tests are not a measure of oral proficiency, but rather a measure of explicit grammatical knowledge in written Spanish and English. Spanish Heritage speakers (SHS) had significantly higher cloze proficiency than L1-E/SL in Spanish  = 2.55, p = 0.04), and there were no significant differences between L1-E/SL and SHS in English proficiency  = −1.13, p > 0.05). Comparing L1 Spanish speaking L1-S/EL to SHS, we also found significantly lower SHS Spanish cloze proficiency  = −4.1, p < 0.01) but higher SHS English cloze proficiency  = 2.36, p < 0.05). It is important to note that the SHS combined language proficiency (English plus Spanish proficiency = 37.5+35.5 = 73) substantially exceeds that of their L1-E/SL counterparts (English plus Spanish proficiency = 38 + 32.5 = 70.5) and is equivalent to that of the L1-S/EL counterparts (English plus Spanish proficiency = 33.5 + 39 = 72.5). The results of written proficiency in English and Spanish for all groups are shown in Proficiency in both languages for all participants was assessed with a written cloze test2.2.The materials for this study consisted of specially designed Spanish and English sentences each with a single keyword (60 keywords in Spanish and 60 keywords in English) in sentence-final position and the rest of the sentence  serving as the context. These sentences were presented to listeners in conditions that varied by use of three factors: speech style of the talker, predictability of the target word, and signal-to-noise ratio (SNR).4  such that the final keyword could be correctly guessed based on the preceding words. Sentence frames that were ‘low predictability’ contained no such contextual cues to the final word. All English stimuli were taken from pork.High contextual predictability: The meat from a pig is called pork.Low contextual predictability: Father thought about the High versus Low Contextual PredictabilityThe current study normed new Spanish stimuli (see below) on L2 Spanish learners following a similar protocol used in The third factor was signal-to-noise ratio (SNR), set at +5 dB (high) and 0 dB SNR (low) for each language. Studies, such as 2.2.1.Predictability for Spanish keywords was measured in a similar way to Phase two consisted of eight new paid participants, all intermediate level learners of Spanish at Northwestern University. Participants in this phase were presented with 100 sentences in which the final word was highly predictable from the context that preceded it, but this time with vocabulary that was reflected in Phase One’s results. Results contained 67 sentences with at least 88% (7 out of 8 participants) agreement on final word response, indicating that the context from early words in the sentence strongly predicted the final keyword.the or a) were given in Spanish to participants so as to eliminate any influence of grammatical gender on predictability. For example, in Spanish the word “the” can be written as either el or la, indicating that the morphological gender of the noun that follows is either masculine or feminine, respectively. To eliminate any artificial effects of gender influencing predictability, these 12 participants saw sentences with el/la (or la/el) before each final word when necessary. The ordering of these articles was randomized across all 70 sentences.In phase 3, the top 70 sentences (67 sentence frames with at least 88% agreement and 3 with at least 75% agreement) were selected for additional testing on 12 new intermediate level Spanish learners at Northwestern University. However, during this phase, both grammatical gender forms of the article . Additionally, the stimuli contained no cognates across languages in order to avoid any confounds based on lexical similarity across languages. We selected the best 60 sentence frames for the current study’s stimuli. No sentences contained the same keyword across languages . A rating of 1 indicated “I am unfamiliar with this word and I do not know how to use it in Spanish”. A rating of 4 indicated “I recognize this as a Spanish word, but I am unsure of its meaning”. A rating of 7 indicated “I recognize this as a Spanish word and I know how to use it”. Participants were asked to provide a rating from 1–7 and provide an English translation if possible. The median rating for all content words used in the study was 6.92 (out of 7) with a standard deviation of 0.49, indicating that participants were highly familiar with all content words in the final stimuli and that vocabulary level was unlikely to influence agreement of the final keywords. Two additional L1 Spanish speakers checked all of the final 60 stimuli for grammar and spelling errors.2.2.3.Stimuli were recorded in a sound-attenuated booth using a Shure SM81 Condenser Handheld microphone. The talker for the stimuli was a simultaneous Spanish-English bilingual for whom the age of acquisition of both languages was approximately 0 (at birth). Sentences were displayed on a computer monitor one at a time with breaks every 20 sentences to avoid fatigue and disfluencies. Speech style and language were blocked such that Spanish was recorded first, followed by English. Clear speech always preceded plain speech styles to avoid any second-mention reduction effects e.g.,  in the c2.2.4.Three L1 Spanish and three L1 English speakers were asked to rate the stimuli recorded by this simultaneous bilingual talker. 20 sentences were randomly selected from each language and given to raters who were instructed to rate her accent on a scale from 1 to 7. 1 indicated “this talker has an L2 accent/is a highly accented speaker of the language” and 7 indicated, “this talker has an L1 accent/is an unaccented speaker of the language”. The average rating for English was 6.75/7 and for Spanish 6.3/7, indicating that this talker is a highly proficient, unaccented speaker of both English and Spanish.2.3.The experiment took place over two days to avoid confounds of language mixing. Day 1 was entirely in English and Day 2 was entirely in Spanish. Because the heritage speakers (the group of interest) were English dominant, we expected that the Spanish session would be the more difficult experimental session. Therefore, SHS would benefit from the Spanish session coming second as they would be able to accommodate to the task, effectively making it harder for us to observe lower cores in Spanish. Any lower abilities to recognize Spanish than English speech-in-noise would then not be able to be accounted for solely on the basis of a lack of task adaptation.The study is a fully crossed design with three factors at two levels (enhancement/high or degradation/low) each. As discussed above, the three factors included signal-to-noise ratio (SNR), semantic predictability, and speech style. SNR was set at +5 dB (enhancement/high) and 0 dB (degradation/low) for both languages. The noise used in the study was speech-shaped noise leveled at the respective SNRs  by leveling all sentences to the same baseline and then manipulating the level of the speech-shaped noise to generate both +5 and 0 dB SNR differences. To reduce perceptual difficulties due to the onset of noise and speech occurring at the same time, each sentence was surrounded by a 500 ms noise lead or lag at the start or end of the stimulus, respectively. Semantic predictability consisted of 60 highly predictable frames (enhancement/high) in English and Spanish and 60 frames in English and Spanish that did not contribute to the context of the sentence (degradation/low). Speech style was either clear speech (enhancement/high) or plain speech (degradation/low).Stimuli were blocked by SNR with the easier +5 dB SNR coming first for both languages. This ensured that any observed reduction in final word recognition accuracy in the harder SNR (0 dB) would have exceeded any benefit gained from a task practice effect. Speech style (clear versus plain) and predictability (high versus low) were randomized within each block of SNR to avoid task learning confounds. If speech style were blocked, participants could be more likely to pay less attention to the content of the sentence during the clear speech block, since they would have realized that the final word was always going to be produced in a clear speaking style. Additionally, the low predictability sentence frames often repeat elements such as “Mom read about the … ” or “Dad thought about the … ”. By completely randomizing predictability, participants had less opportunity to learn that a particular group of keywords would not be identifiable based on context and thus, they were forced to listen to the entire sentence each time.The same participant heard each keyword only twice: once in a high predictable context and once in a low predictable context within the same SNR block. SNR and speech style were held fixed for each keyword by participant. For example, a given keyword might be presented at +5 dB SNR and in clear speech in both the high and low predictability contexts. Thus, the effect of predictability could be assessed as a within-participant factor with SNR and style fixed.Using MaxMSP software for stimulus presentation, participants listened to each sentence one at a time over Sony MDRV700 headphones in a sound attenuated booth. Participants were instructed to type in the last word of every sentence and that they could guess or type, “I don’t know” if they were unsure of the final word.In order to score the outputs, participants’ responses were matched with the actual keyword. Correct identification of the final keyword received a score of 1, while an incorrect score received a score of 0. Spelling errors were ignored, specifically in cases where incorrect spelling did not contribute to a difference in meaning . That is, if a participant misspelled or mis-inflected  a word in a way that indicated accurate auditory recognition, the word was still counted as correct receiving a score of 1. Incorrect number markings  were considered incorrect in both languages. In the case of multiple inflectional errors such as gender and number in Spanish, the word was then considered incorrect receiving a score of 0.3.The main goal of this study is to examine differences in L1 and L2 speech processing between two types of bilinguals: L2 learners (L1-dominant) and heritage speakers . As such, we built two separate logistic mixed effects models comparing the SHS group to each of the two L1-dominant learner groups: (1) SHS versus L1-English/Spanish learners (L1-E/SL), and (2) SHS versus L1-Spanish/English learners (L1-S/EL). The structures for these two models were identical. Both models included each of the three enhancement factors ) as fixed factors. The interactions between all factors  were also included. All factors were contrast coded to their respective enhancement/high and degradation/low values. Language of the task (English versus Spanish) and listener group  were also contrast coded within each model to compare the effects of each language on the three listener groups. As the interactions between language, listener group, and each signal manipulation are the critical comparisons for this study, these three three-way interactions were also included in both models for assessment . The maximal random effects structure that converged was utilized for both models. The final two models included random intercepts for participant and keyword, and random slopes included all main effects (except listener group) and a language-factor interaction for each signal manipulation by subject and predictability by keyword. The significance of each factor (and interaction) was then determined within each model using the model comparison method, which relies on chi-squared values to determine whether the presence of a single (n − 1) factor (or interaction) significantly improves the fit of the data. The results of both models, which separately compare SHS to each of the two L1-dominant/learner groups, will be presented together, grouped by each enhancement factor .3.1.2(1) = 24.57, p < 0.05), indicating that words produced in clear speech were more likely to be correctly recognized than those produced in plain speech. The interaction between style and language was also significant  = 14.01, p < 0.05), which suggests both L1-E/SL and SHS benefitted more from English clear speech than Spanish clear speech. This result is consistent with 2(1) = 4.52, p < 0.05) such that SHS showed less of an overall benefit in clear speech compared to L1-E/SL. However, the three-way interaction between listener group (L1-E/SL versus SHS), language, and style was not significant  = 1.7, p > 0.05), suggesting that neither of these two-way interactions were influenced by the remaining factor.With respect to L1-E/SL versus SHS, there was a significant main effect of speech style  = 23.43, p < 0.05), indicating that keywords produced with clear speech were more accurately recognized than keywords produced with plain speech. The three-way interaction between style, language, and listener group was significant  = 6.33, p < 0.05), suggesting that SHS and L1-S/EL did not equally benefit from speech style across both languages. That is, SHS failed to benefit from clear speech in Spanish, whereas L1-S/EL benefitted from clear speech in both languages. Additionally, there was a significant interaction between style and listener group  = 7.89, p < 0.05), which, like above, suggests that SHS showed an overall smaller benefit from clear speech. The interaction between style and language was also significant  = 12.45, p < 0.05) such that the overall benefit for clear speech was greater in English than in Spanish. While this result may be inconsistent with With respect to L1-S/EL versus SHS, there was a significant main effect of speech style  = 4.19, p < 0.05), indicating that highly semantically predictable contexts aided the recognition of final keywords. The interaction between listener group and predictability was significant  = 8.01, p < 0.05), such that SHS benefitted more from semantic predictability than L1-E/SL. Although the interaction between predictability and language was not significant  = 1.19, p > 0.05), the three-way interaction between listener group (L1-E/SL versus SHS), language, and predictability was significant  = 9.51, p < 0.05). This suggests that SHS and L1-E/SL did not equally benefit from predictability across both languages. While the two groups showed similar benefits from predictability in English, SHS showed a much stronger predictability benefit in Spanish than L1-E/SL. Consistent with 2(1) = 32.79, p < 0.05), indicating that listeners were aided more by clear speech in high predictability contexts.With respect to L1-E/SL versus SHS, there was a significant main effect of predictability  = 14.53, p < 0.05) such that keywords were more accurately recognized when there was ample supporting context in the sentence that preceded them. While both the interaction between listener group and predictability  = 1.13, p > 0.05) and language and predictability  = 1.11, p > 0.05) were not significant, there was a significant three-way interaction between predictability, language, and listener group  = 9.18, p < 0.05), suggesting that SHS and L1-S/EL did not equally benefit from predictability across both languages. That is, although both SHS and L1-S/EL benefitted from predictability in Spanish, SHS showed a much stronger predictability benefit in English compared to L1-S/EL. Again, consistent with Bradlow and Alexander, the interaction between style and predictability was significant  = 41.62, p < 0.05) such that clear speech more greatly aided word recognition in highly predictable contexts than in low predictability contexts.With respect to L1-S/EL versus SHS, there was a significant main effect of predictability  = 52.7, p < 0.05), suggesting that final keyword recognition was improved in the favorable SNR condition, +5 dB. The interaction between SNR and language was significant  = 6.59, p < 0.05), indicating that both L1-E/SL and SHS were more resistant to noise in English than Spanish. The interaction between listener group and SNR was not significant  = 0.47, p > 0.05), nor was the three-way interaction between SNR, listener group (L1-E/SL versus SHS), language  = 0.049, p > 0.05), indicating that L1-E/SL and SHS were comparably affected by SNR manipulations in both languages. Both the interaction between style and SNR  = 0.01, p > 0.05) and the interaction between predictability and SNR  = 0.99, p > 0.05) were not significant.With respect to L1-E/SL versus SHS, there was a significant main effect of SNR  = 52.8, p < 0.05) as final keyword recognition improved at the easier SNR (+5 dB). The three-way interaction between SNR, language, and listener group was significant  = 4.35, p < 0.05), suggesting that SHS and L1-S/EL were differentially affected by noise across both languages. While the interaction between listener group and SNR was not significant  = 0.01, p > 0.05), there was a significant interaction between language and SNR  = 6.46, p < 0.05). Taken together, these results suggest that overall while SHS and L1-S/EL were similarly affected by noise, SHS were more resistant to the presence of noise in English compared to L1-S/EL. Furthermore, the interaction between style and SNR was not significant  = 0.21, p > 0.05). However, the interaction between predictability and SNR was significant  = 8.05, p < 0.05), indicating that highly predictable contexts aided keyword recognition in higher levels of noise.With respect to L1-S/EL versus SHS, there was a significant main effect of signal-to-noise ratio (SNR)  and/or the amount  of linguistic exposure affected speech learning outcomes in adults. Due to the de-confounding of these aspects of the language learning context—early versus extended language exposure—in switched-dominance bilinguals , we were able to investigate their independent influences on speech recognition accuracy under various enhancement/degradation conditions. Specifically, we compared recognition accuracy of sentence-final words in spoken sentences embedded in background noise by L2-dominant Spanish Heritage Speakers  as compared to L1-dominant bilinguals in both languages. In the current study, we manipulated three factors that are known to either enhance or degrade recognition of sentence-final keywords: contextual predictability , signal-One hypothesis is that if early exposure is critical for complete L1-like performance in a language , heritagAn alternative hypothesis emphasizes the critical role of language dominance affecting language processing , predictWith respect to non-dominant L2 learners (L1-English/Spanish learners (L1-E/SL) and L1-Spanish/English learners (L1-S/EL) in this study), these hypotheses do not make opposing predictions since the L2 for these bilinguals is both late-acquired and non-dominant. If early AoA  and langWhy did the SHS fail to benefit from clear speech in Spanish despite benefitting in both Spanish and English from each of the other two enhancements tested in this study (SNR and predictability)? One possible explanation for why SHS did not receive a Spanish clear speech benefit is that the simultaneous Spanish-English bilingual used for stimuli recording did not produce a strong clear speech effect in Spanish. However, if this were true, then we would expect to see no benefit for L1-E/SL or L1-S/EL in Spanish clear speech. This is not the case see ; in fact5.The current study found that Spanish Heritage Speakers (SHS) who are dominant in their L2, English, demonstrated speech-in-noise recognition accuracy at similar levels to L1-dominant English listeners. However, speech recognition vulnerabilities related to signal degradation emerged for these SHS in their non-dominant L1, Spanish. Combined with generally better SHS than late L2 Spanish learner recognition accuracy of Spanish, these results suggest that while early exposure can afford substantial speech processing benefits, extended exposure and usage are necessary for robust L1-like speech-in-noise recognition. While the data from this study revealed subtle differences in non-dominant language speech recognition compared to their dominant language, it is critical to acknowledge that the bilingual language learning experience of switched dominance/heritage speakers affords obvious life-long benefits in terms of total language proficiency. In the present study, the sum of the English and Spanish cloze test scores for all three listener groups showed that the total language proficiencies of the Spanish Heritage Speakers exceeded that of both the L1-English/Spanish learners and the L1-Spanish/English learners, emphasizing the remarkable language asset of the heritage language learner experience.


The Shack–Hartmann wavefront sensor samples a beam of light using an array of lenslets, each of which creates an image onto a pixelated sensor. These images translate from their nominal position by a distance proportional to the average wavefront slope over the corresponding lenslet. This principle fails in partially and/or non-uniformly illuminated lenslets when the lenslet array is focused to maximize peak intensity, leading to image centroid bias. Here, we show that this bias is due to the low Fresnel number of the lenslets, which shifts the diffraction focus away from the geometrical focus. We then demonstrate how the geometrical focus can be empirically found by minimizing the bias in partially illuminated lenslets. The Shack–Hartmann wavefront sensor (SHWS) is used to measure the departure of test wavefronts from a typically flat or spherical reference wavefront . Originaz of the pixelated sensor from the geometrical focus, the lenslet image centroid vector position ρc (z), the light intensity at the lenslet plane I (r), the wavefront gradient ∇Wr) is the centroid of the light intensity at the lenslet plane, z = −f, and r is the transverse position vector. The first term on the right-hand side of the equation does not contain information about the wavefront, and thus can be thought of as an undesired bias term.In essence, a SHWS consists of a pixelated light detector in the back focal plane of a two-dimensional array of lenslets . The lig lenslet :(1)ρc(zIr)(I0). For these lenslets, the bias term vanishes, as ρc(−f) = 0 if we assume that the lenslet center is at the coordinate system origin, and thus Most SHWSs use lenslets that are symmetric relative to their centers  and are uniformly illuminated , and is unevenly illuminated  ≠ 0), thus producing a non-zero bias term, as can be seen from The SHWS principle, however, may fail in two ways in non-uniformly illuminated lenslets: first, by unevenly weighting the gradient across the lenslet, through the wavefront term in N = a2∕λf, with a the aperture radius and λ the wavelength of light) in the 0.47–9.3 range. In this low Fresnel number regime, the point of maximal intensity behind the lenslet (the diffraction focus) is shifted toward the lenslet and away from the geometrical focus [F = f ∕2a ≥ 0.5 and the Fresnel number N > 1 [A brief survey of scientific publications ,12–36 anal focus –39. Thiser N > 1 ,39. AssuThe resulting centroid bias in partially illuminated lenslets could, in principle, be corrected through a careful calibration in which the beam used for defining the reference wavefront had the same intensity profile as the test beam, and in particular, at the partially illuminated lenslets. Unfortunately, this is not practical when the calibration beam covers a larger area of the lenslet array than the test beam. In ophthalmic SHWSs, such calibration is not even possible, as the pupil of the eye is in constant motion. A simpler and arguably more natural method for mitigating the centroid bias would be to focus the pixelated sensor in the geometric focal plane of the lenslet array, as we demonstrate next.ρc(−f) = 0, and therefore, the first term on the right-hand-side of An experimental setup depicted in The adjustable iris was then closed to create partially illuminated lenslets that exhibited a centroid shift proportional to the distance of the pixelated sensor from the lenslet geometrical focus. In this way, the SHWS pixelated sensor was axially translated until the spot displacement of the partially illuminated lenslets was minimized.Visualization 1 (see on 1 see  shows hoVisualization 1 are plotted as a function of peak spot intensity in The spot centroid displacements for the 60 frames in The sequence of SHWS images also shows that, as expected, the spots at the diffraction focus are narrower and brighter see , while aVisualization 1 as if they were due to real wavefront slopes, using the vector matrix multiply method and the slope geometry by Southwell [Finally, and in order to illustrate the impact of the bias term, we integrated the centroid displacement maps from outhwell . The rooIn summary, we have identified the focal shift due to diffraction as a reason for SHWS centroid bias in partially illuminated lenslets and have demonstrated its correction by shifting the pixelated detector to the geometrical focus of the SHWS lenslet array. The presence of this uncorrected bias in pupil edge lenslets has typically been addressed by ignoring these lenslets in wavefront estimation algorithms. This results in poor wavefront estimation at the pupil edges, with its impact varying across applications and wavefronts. In adaptive optics for imaging applications, achieving the best possible wavefront correction at the pupil edge is critical, as the photons at the pupil edge carry the highest spatial frequencies that determine the smallest resolvable objects.


Wearables and artificial intelligence (AI)–powered digital health platforms that utilize machine learning algorithms can autonomously measure a senior’s change in activity and behavior and may be useful tools for proactive interventions that target modifiable risk factors.The goal of this study was to analyze how a wearable device and AI-powered digital health platform could provide improved health outcomes for older adults in assisted living communities.Data from 490 residents from six assisted living communities were analyzed retrospectively over 24 months. The intervention group (+CP) consisted of 3 communities that utilized CarePredict (n=256), and the control group (–CP) consisted of 3 communities (n=234) that did not utilize CarePredict. The following outcomes were measured and compared to baseline: hospitalization rate, fall rate, length of stay (LOS), and staff response time.P=.64), sex (P=.63), and staff service hours per resident (P=.94). The data show that the +CP communities exhibited a 39% lower hospitalization rate (P=.02), a 69% lower fall rate (P=.01), and a 67% greater length of stay (P=.03) than the –CP communities. The staff alert acknowledgment and reach resident times also improved in the +CP communities by 37% (P=.02) and 40% (P=.02), respectively.The residents of the +CP and –CP communities exhibit no statistical difference in age (The AI-powered digital health platform provides the community staff with actionable information regarding each resident’s activities and behavior, which can be used to identify older adults that are at an increased risk for a health decline. Staff can use this data to intervene much earlier, protecting seniors from conditions that left untreated could result in hospitalization. In summary, the use of wearables and AI-powered digital health platform can contribute to improved health outcomes for seniors in assisted living communities. The accuracy of the system will be further validated in a larger trial. Advances in public health and medical treatment over the past century have increased the average life expectancy in the United States by 30 years ,2. The nOlder adults are disproportionally affected by chronic conditions, where 77% have at least two, and 65% have four or more chronic diseases . ConsequAssisted living is a long-term care option that combines housing, personal assistance with activities of daily living, and supportive specialized services and therapy. In the US, approximately 812,000 older adults live in nearly 29,000 assisted living communities -15. AssiMany caregivers in assisted living communities rely solely on their observational powers to detect health changes in older adults in their care. As the number of residents requiring more assistance is increasing in these communities, the number of available caregivers is decreasing dramatically. Technologies can be utilized to augment and force-multiply human observation and provide quality care. Such solutions can provide continuous observation, detecting changes in activity and behavior patterns that may be indicative of a change in health status—information that cannot be provided by intermittent human observation. Artificial intelligence (AI) can be used to bridge the caregiver-senior ratio gap and augment occasional human observation with continuous machine observation and deep learning neural nets to predict when interventions are needed.A growing body of evidence demonstrates that sensor-laden wearables utilizing AI, and in particular machine-learning algorithms, can detect an individual’s daily activity and behavior ,34-41. IIn this study, we tested whether the use of the CarePredict system could effectively improve the care provided in senior living communities. Specifically, we assessed the impact on hospitalization rate, fall rate, length of stay, and staff response time in each assisted living community.The study was designed to assess facility-level and resident-level outcomes for communities that utilized CarePredict’s AI-powered digital health platform, wearable device, and real-time location system. Retrospective analysis of anonymized resident data was collected from six assisted living communities in three states over 24 months. A study flow chart is provided in All de-identified data analyzed in this study were collected and reported by facility staff using each community’s online electronic healthcare software platform. The same software was used in all communities. CarePredict employees were provided access to the extracted anonymized data for scientific evaluation. No identifiable resident information has been or will be shared.The CarePredict system consists of a wrist-worn wearable device, context beacons for room location, and a cloud-based AI-powered platform A-B. The The real-time location or context beacons enhance the accuracy of the wearable’s gesture recognition engine by bringing in room type data and permitting accurate room-level location tracking in an indoor setting . Each beThe system collects unique and rich data sets to train deep learning neural nets to surface crucial insights that correlate with an increased risk for a fall, UTI, or depression. A few correlates used in the system include the following: increased fall risk due to malnutrition, skipping meals, increased nightly get up count, reduced sleep duration, and decreased physical activity level; the increased probability for a UTI due to increased frequency or duration in bathroom visits, unusual toileting patterns, increased nighttime bathroom patterns, and reduced activity level; early signs of depression due to increased frequency of skipping meals, restless sleep, avoidance of bright lights and sunshine, and reduced physical activity levels. Further details on this system and established correlations are provided here ,46-52. AOperators and staff both benefit from the use of an accurate real-time location system. First, staff can know the location of a resident who has pressed the button on their wearable to call for assistance, enabling the closest staff member to attend to a resident quickly. Second, historical insights allow the operators to assess previous shifts’ activities to improve staffing efficiencies. Such information may serve to facilitate improved response times, care coordination, and optimal workforce distribution. In addition, geofence alerts provide an added safety measure against wandering and elopement risks of residents with Alzheimer’s and dementia.With a surge in acuity levels across senior living communities, providers need to have visibility into the amount of care required and provided to its residents. This solution allows caregivers to document at the point of care what services were rendered and a suite of reports that provide response time to alerts, time spent with residents on various direct care activities, and insights regarding quantity and the quality of care provided.The wearable provides two-way voice communication that allows residents to communicate directly with the caregiving staff. Staff can prioritize alerts and respond appropriately. As a single communication platform for residents and staff, the wearable eliminates the need for multiple devices and provides tracking and reporting capabilities for staff efficiency.The wearable is integrated with passive RFID technology so it can be used for keyless door entry, providing convenience and safety to residents and staff and assuring consistent adherence of use.th quarter of the study. The average rate of change between these periods was computed between these two periods. Staff response times were automatically measured in this study using the CarePredict system. Residents trigger an alert and call for staff assistance by depressing the button on their wearable, and a staff member acknowledges the alert using the CarePredict software. We analyzed both the duration of time the staff required to acknowledge an alert and then to reach the resident. The residents’ length of stay in each community was also measured. Length of stay is defined as the number of months a resident resides in a given community. The average, geometric, and median length of stay were analyzed, and detailed descriptions are provided in the supplementary materials section , length of stay, fall incidents, emergency department and/or hospital admissions/discharges. Resident incident reports were utilized to document hospitalizations and fall incidents. The headcount, hospitalization, and fall incident numbers were used to compute both a hospitalization and fall rate. The hospitalization rate was defined as the number of hospitalization incidents per headcount in the facility, and the fall rate as the number of falls recorded per headcount per year. The average “baseline” rates for each community were measured from the first quarter of the study; the average “end of study” measurement was collected in the 8 section .P value <.05 was considered statistically significant.Descriptive statistics, including means, standard deviations, and distributions, were provided for all study variables and compared across groups (+CP vs. –CP communities). Study variables were compared to baseline measurements for each group. A two-sample, two-tailed, t-test was applied for metric variables to test for significant differences between groups. A Informed consent was obtained from the communities and participants included in the study.P=.64) and gender (P=.63). The average staff service time (hours per headcount per month) for each +CP and -CP community is shown in P=.94).The resident demographic data  and facility staff service time were assessed. The average resident ages were 87.3 years (SD 1.2 years) for the +CP communities and 88.1 years (SD 1.6 years) for the –CP communities . The perP=.04). Thus, the hospitalization rate for the +CP communities was 39.8% lower than the –CP communities, 33.8% (SD 6.0) versus 73.6% (SD 18.1), respectively (P=.02). The average fall rate  and change in fall rate compared to baseline was measured for both the +CP and –CP communities (P=.3). Compared to baseline, the fall rates for the +CP communities decreased 1.01 (SD 0.57), and the –CP communities increased 0.82 (SD 0.55). These changes are statistically significant (P=.05). The average fall rate for the +CP communities was 69% lower than for the –CP communities, 0.97 (SD 0.28), and 3.11 (SD 0.75), respectively. The normalized fall rate between groups was statistically significant (P=.01).The hospitalization and fall rates for the six assisted living communities are provided inmunities . There wThe median, geometric, and mean length of stay in the CarePredict and control communities are provided in P=.03), and staff response time improved by 263 seconds (P=.02)  .In this pilot study, we assessed whether the use of a wearable device and AI-powered digital health platform could provide improved health outcomes for older adults in an assisted living community. We found that the communities with CarePredict (+CP) exhibited a 40% lower hospitalization rate, 69% lower fall rate, and 67% greater length of residence stay compared to control communities (–CP). Overall, the use of CarePredict technology in assisted living communities appears to contribute to improved outcomes and shows promise as an effective tool to provide a higher quality of care.There are several possible explanations for these findings. First, since both the residents and staff wear the CarePredict device, it functions as both an effective communication platform and resident alert system allowing for the coordination of prompt care. The system also provides robust staff performance metrics, which can be used to encourage continually improving staff response times to residents who need aid. The alert system prevents minor situations from escalating to emergent situations requiring hospitalization. Residents desire prompt, attentive care in assisted living communities, and the CarePredict system helped contribute to the facility staff acknowledging alerts 40% faster and reach residents in response to those alerts in 37% less time ,55. The Second, the system provides the community staff with detailed information regarding each resident’s activities and behavior. Changes in an adult’s activity and behavior are well-characterized to precede health declines; therefore, staff can use this information to quickly identify older adults that are at an increased probability for a health decline and intervene much earlier ,50. For Fall rates also appear to be positively impacted by the use of the CarePredict solution. The data shows that +CP communities exhibited a 69% lower fall rate than the –CP communities. Fall rates are known to increase steadily with age , and theBy identifying older adults whose activity and behavior pattern indicates decreasing mobility, staff can take pre-emptive action to mitigate senior fall risk, UTIs, or other incidents that may have required hospitalization. Reducing hospital admissions also helps to maintain census, reduce resident turnover, and increase resident LOS in the community. The data shows that +CP communities exhibited a nearly 40% lower average hospitalization rate than the –CP communities (33.8% versus 73.6%). Several senior living outcomes studies by Zimmerman et al and Hedrick et al reported average annual hospitalization rates of 51% and 40%, respectively ,58. The There are several limitations to this pilot study. First, the study was conducted at six assisted living communities with less than 500 total residents. This study needs to be replicated and results confirmed using a larger sample size of individuals. Second, the –CP communities did not use an alert response technology system in this study, and thus staff response times could not be collected and analyzed for the –CP communities. As a result, the impact that the CarePredict technology had on staff response times was only measured and analyzed for the +CP communities. We, therefore, could not compare the staff response times between the +CP and –CP communities, and rather only measured the response times for the +CP communities at baseline and end of the study. Third, staff in the +CP communities used the CarePredict technology system for multiple purposes: to acknowledge and respond to resident alerts, to communicate to residents and other staff members, and to autonomously collect resident activity and behavior data. Since Carepredict served multiple functions, it is difficult to attribute which of these system capabilities and data sets directly contributed to the improved outcomes.To better understand the mechanisms by which these improvements were provided, in future studies, we plan to include a control group of communities that only utilize the CarePredict system for alerting and communication purposes. The added value provided by the predictive analytics feature will then be easier to assess and quantify directly. These results will also allow us to assess the impact that the proactive, actionable data generated by the CarePredict system will have on identifying and preventing high-risk residents from being hospitalized. Finally, although the six communities assessed in this study had comparable resident demographics  and staff service hours per resident, other factors like residents’ hospital and fall history, and quality of care indicators may also have contributed to the differences observed in the measured outcomes.The leading cause of residents moving out of assisted living communities is unplanned hospitalization . Hospita


In this paper, we propose a high-sensitivity optical sensor at terahertz frequencies based on a composite structure containing a one-dimensional photonic crystal (1D PC) coated with a layer of monolayer graphene. Between the 1D PC and the graphene there is a sensing medium. This high-sensitivity phenomenon originates from the excitation of optical resonance between the graphene and the 1D PC. The proposed sensor is highly sensitive to the Fermi energy of graphene, the thickness and refractive index of the sensing medium, and the number of graphene layers. By selecting appropriate parameters, the maximum sensitivity ( An optical sensor, a classical sensor type based on optical principles, can sensitively monitor measured information and convert the information into optical signals or other forms of data according to certain rules . Owing tWe know that the generation of optical resonance has a very positive effect on the realization of high-sensitivity sensor detection. At present, many optical-sensor schemes are mainly realized by the exciting of SPR. This is mainly due to the fact that SPR can produce a very obvious resonance peak, thus creating conditions for sensitive sensor detection. Recently, optical Tamm states (OTSs), a kind of surface wave confined on the contact surface of two different media, have attracted attention in the field of optical sensing ,31. It iWe consider a terahertz sensor by inserting a sensing medium between a polymethylpenten (TPX)/A transfer matrix is applied to calculate the reflectance of the proposed vertically stacked structure . For simThis section focuses on the sensing characteristics of the proposed structure. In essence, the configuration in In order to further illustrate the relevance of graphene to the strong excitation of the resonance mode, we cover the sensing medium with monolayer graphene and draw the normalized electric field distributions in To enhance the sensitivity and to expand the measuring range of the proposed terahertz refractive index sensor, we also take the major parameters of graphene, and the thickness and refractive index of the sensing medium into consideration. The variations in the reflectance at different levels of Fermi energy with respect to the incident angle are plotted, as shown in =0.05 ps . It is f−1). In summary, the effect of relaxation time on sensitivity is more significant than that on Fermi energy.It can be seen from the formula of graphene conductivity that the relaxation time also has an obvious effect on conductivity. However, there are sometimes obstacles to regulating the relaxation time of graphene, because it is difficult to change the relaxation time once the graphene is prepared. Nevertheless, it is necessary to evaluate systematically the effects of relaxation time on the sensing properties of the proposed structure, as shown in The thickness of sensing medium is also an important factor in the sensitivity of the proposed structure. Therefore, we plot the curves of sensitivity according to the thickness of the sensing medium, as shown in Therefore, it is proved that proper selection of the sensing medium thickness (−1. However, it is noteworthy that a continuous increase in the number of graphene layers would lead to a drop in the depth of the reflectance curve, and this would cause difficulties in calculating resonance mode measurements.Next, the relationship between the number of graphene layers and the sensitivity of this structure is also investigated, as shown in We know that numerous approaches to creating a terahertz refractive index sensor have been reported. Lastly, in order to more intuitively reflect the sensing characteristics of the refractive index sensor in this paper, we compiled a table and compared our results with some typical and high-performance previous works, as shown in In this paper, we propose a terahertz refractive index sensor based on a graphene–Bragg reflector composite structure and strong resonance mode excitation to provide higher sensitivity at the terahertz band. The findings have shown that the sensitivity and FOM of the proposed structure are heavily dependent on the thickness and refractive index of the sensing medium and the optical parameters of graphene, mainly because these parameters markedly affect the position of the resonance angle as well as the depth and width of the reflectance curve. Proper settings of the parameters could offer a maximal sensitivity of


The obtained results were compared to previous electrical measurements. The substrate longitudinal optical phonon coupling “LOOPC” modes have also been examined for several epitaxial graphene layers. The LOOPC of the SiC substrate shows a precise map of the density of charge in epitaxial graphene systems for different graphene layers number. Correlations between the density of charge and particular graphene layer shape such as bubbles have been determined. All experimental probes show a high degree of consistency and efficiency. Our combined studies have revealed novel capacitor effect in diverse epitaxial graphene system. The SiC substrate self-compensates the graphene layer charge without any external doping. We have observed a new density of charge at the graphene—substrate interface. The located capacitor effects at epitaxial graphene-substrate interfaces give rise to an unexpected mini gap in graphene band structure.Graphene distinctive electronic and optical properties have sparked intense interest throughout the scientific community bringing innovation and progress to many sectors of academia and industry. Graphene manufacturing has rapidly evolved since its discovery in 2004. The diverse growth methods of graphene have many comparative advantages in terms of size, shape, quality and cost. Specifically, epitaxial graphene is thermally grown on a silicon carbide (SiC) substrate. This type of graphene is unique due to its coexistence with the SiC underneath which makes the process of transferring graphene layers for devices manufacturing simple and robust. Raman analysis is a sensitive technique extensively used to explore nanocarbon material properties. Indeed, this method has been widely used in graphene studies in fundamental research and application fields. We review the principal Raman scattering processes in SiC substrate and demonstrate epitaxial graphene growth. We have identified the Raman bands signature of graphene for different layers number. The method could be readily adopted to characterize structural and exceptional electrical properties for various epitaxial graphene systems. Particularly, the variation of the charge carrier concentration in epitaxial graphene of different shapes and layers number have been precisely imaged. By comparing the intensity ratio of 2D line and G line—“I It is considered a unique two-dimensional (2D) carbon allotrope. Theoretical studies of graphene date back to 1947 . T. T124]. ize, see a. The stize, see b,c and lize, see b. The 2Der layer ,129,130.2D, the intensity ratios IG/I2D and IG/I2D as well as the associated density of charge across the bubble are summarized in 2 area as shown. The epitaxial graphene bubble was produced on 4H-SiC with C face termination. Raman analysis was done using maps on a larger surface area to confirm the existence of the new charge redistribution inside and outside the bubble. G/I2D) across the sample surface and is indicative of the graphene layer number , where 4H-SiC (000-1) substrate Raman signature exists. The Raman modes shift position of the substrate depend on the used laser. In our study, we have used a 488 nm Ar-ion laser as an excitation source. Different Raman bands of the 4H-SiC substrate have been located, particularly, we have located A1 (LO) band at around 967 cm−1 [1 (LO) band to the local charge carrier concentration [1 (LO) phonons and polarize graphene at the same time. Phonon—plasmon coupling could be verified by a simple comparison of the Raman shift in the A1 (LO) mode located at 962 cm−1 between a freestanding and graphene-coupled 4H-SiC. The LOOPC modes intensity 1[LO] (E1 [TO]). All the changes of the LOOPC mode are associated with changes in its shift position and its line shape  and demonstrates much smaller variation compared to A1. Note, that the theoretical model fits particularly well for single layer A4. of the observed differences in the Raman shift position of the A1 (LO) mode are assigned to different density of charge. Thus, we have revealed a highly sensitive theoretical model that may describe the local graphene charge carrier through substrate imaging. The experimental findings confirm the premise that graphene has very unique quantum capacitance that induces new charge distribution in the graphene-substrate interface. Thus, we have extrapolated that the improper fits of the A1 (LO) mode in A 1,2 and A3, may be explained by a high local charge density fluctuation, arising from the capacitance effects, while the correct fit of the A1 (LO) mode in A4 may be due to weak doping in such areas. The LOOPC method precisely describes the density of charge in the graphene layers. The LOOPC method was successfully employed to describe the electronic properties in graphene with different layer numbers . The worrea see, a,b. The SiC see, a. For thant see, b. Similaant see, b. Howeve1 (LO) mode broadening is at the limit of the LOOPC model and will not be discussed here. In any case, the mode broadening behavior does not affect the description of the density of charge presented here. Our evaluation of the LOOPC method has established that in weakly doped regions the A1 (LO) mode position shift follows generally the shift of the graphene Raman modes. However, in certain regions, we have observed a particularly large difference between the substrates A1 (LO) mode and the graphene’s G band. This has been attributed to a large electron density variation in the corresponding areas. The proposed large charge density fluctuations may be induced by the strong screening effects from the Coulomb forces between the substrate and graphene. Thus, the LOOPC technique is an advanced optical method based on Raman spectroscopy analysis giving evidence for the phonon–plasmon coupling in epitaxial graphene. This simple method allows us to image properly the density of charge at the epitaxial graphene–SiC interface system, based purely on the behavior of the substrate Raman modes. In addition, it can be used to determine the free carrier concentration n in the graphene layer. This can be done by investigating the 4H-SiC plasma frequency m* corresponds to the electron effective mass in SiC ,q, the crystal momentum and n can be determined by adjusting parameters 1, A2, A3 and A4 were obtained previously [1, A2, A3 and A4 was found to be n1 = 2.7129 × 1018 cm−3, n2 = 2.5214 × 1018 cm−3, n3 = 4.1 × 1018 cm−3 and n4= 1.8857 × 1018 cm−3, respectively. The charge density for the undoped 4H-SiC substrate was calculated as substraten = 3.448 × 1011 cm−3. The obtained results were compared to the charge density values deduced from the graphene G band position shifts and showed excellent agreement with previous findings [grn = substraten* L. Using this expression, we find that the thickness of the charged surface layer of the SiC substrate is approximately L = 0.54 mm which is equal to 2857 SiC bilayers. All these results add further proof to the capacitor effect emerging at graphene-substrate system ωp2=4eviously . Using tfindings . The LOOtem see, . Note, tActually, graphene layers–charged substrate layers system behaves as a resonant cavity for plasmon excitations. These plasmon waves move along the graphene surface. Previously, we have discussed the emergence of capacitor effects in an epitaxial graphene bubble. However, similar phenomena may be observed at the graphene-substrate interface due to other contributing electrostatic factors, such as substrate defects, charged impurities and charged puddles of epitaxial graphene that capture charges due to the presence of SiC step terraces ,139,140.C becomes [For epitaxial graphene bubble, the appearance of local electrostatic field leads to a gap opening and capacitor formation. The capacitance of the whole system may be separated into the sum of the capacitances of each component. C-face terminated epitaxial graphene interface system is composed of graphene layers and doped SiC layers. Therefore, the total capacitor could be written as the summation of a quantum capacitor ates DoS . The capecessary , so the  becomes : (5)1C=16 ms−1 in graphene. Thus, we found that A is the area of the plates of the capacitor and Here, the quantum capacitance fined as :(6)CQ=2AThe chemical potential as below : (7)μ=EFFE = 0.14 eV and the −4 mF m2 for the single layer graphene in A1. Due to its small value the quantum capacitance dominates the total capacitance at the interface [A represent the area of the substrate surface and bond size ≈ 1.89 Å). We have found that the electrostatic capacitance of SiC per unit area is equals to 4 mF m2 [QC confirming our conclusions that the graphene effects dominate at the interface. Therefore, the contribution of Consequently, we may also determine the Fermi energy and the associated quantum capacitance per unit area. From Reference , we haventerface . This efnterface . We may 04 mF m2 . The Ceqgraphene . It impographene .n is an arbitrary integer. Raman spectroscopy provides a unique tool to detect the Landau quantization, determine the Fermi energy and reveal electron correlations which may influence the Landau quantization. Thus, in-depth analysis of the Raman spectra may establish the effective distance between the Landau levels and reconstruct the entire Landau spectrum. Indeed, such unique experiments have been done by Potemskii et al. [One of the most amazing properties of graphene is Quantum Hall Effect (QHE), which is usually observed in magneto-transport measurements at low temperatures . Such sti et al.  and reveelations .However, the graphene as any two-dimensional (2D) crystal cannot exist in a flat state unless an external flattening support is applied. One example to preserve the flatness of graphene is to sandwich (or confine) it between two BN layers. According to the Mermin-Wagner theorem, many unconfined 2D material may experience all sorts of 3D distortions leading to the formation of nanodomes, wrinkles, nano-valleys . The phenomenon was first theoretically predicted by Kane and Mele in 2007  and thenThe nano-dome or buckled deformation in graphene leads to localized lattice strain in regions surrounding the distortion. Such strain mimics effectively a large inhomogeneous magnetic field. Note, that there no actual magnetic field present and this phenomenon is called a pseudo-magnetic field. By controlling the shape of the graphene deformation, it is therefore possible to adjust the strain and the homogeneity of the magnetic field. The simplest way to achieve this is by depositing nanostructures of an appropriate form on the substrate underneath the graphene layer—creating a landscape and removing its flatness. Recently it was shown that when graphene is placed on such a textured substrate it is possible to manipulate the formation of topological states, such as edge and snake states . The metRaman spectroscopy is powerful non-destructive characterization technique and may properly address localized electron states in epitaxial graphene with nano-domes, where lattice strain is mimicking a huge, inhomogeneous pseudo-magnetic field. Such states localize electrons, similar to Landau levels and are of various intrinsic origins, related to lattice strain. We expect that around graphene nano-domes, due to the presence of the strong pseudo-magnetic field, there may exist both edge and the snake states as well as other topological defects still waiting to be discovered. Similar unique behavior has been already been observed in a real transverse magnetic field.The existing Raman experiments  also dem2g and the G bands. Indeed, under a magnetic field the G-band displays a mode doublet where one Raman mode remains fixed and the other mode oscillates depending on the value of the applied field [Furthermore, the investigation of the Landau levels in graphene has been done with the use of Raman spectroscopy, by following the behavior of the Eed field . It has ed field ,154. Theed field ,156. Theed field .The analysis of the Raman results allows to develop a better understanding of the electron-phonon interaction in the investigated graphene system  as well It is well known that two-dimensional electron gases, similar to graphene, when exposed to a magnetic field may absorb electromagnetic radiation via electronic transitions between Landau levels . Recentl2, which shows a combined spatial average over strained and unstrained regions of graphene. In fact, several distinct Landau level spacings were observed, which follow the Landau quantization Law for graphene’s massless Dirac electrons. The strength of the pseudo-field, extracted from the strain, was about ~41 T [Other forms of the lattice strain exist in graphene and graphene-related materials, such as: 1-epitaxial triangular nano-prisms  and 2-naut ~41 T , which i2) plasma [2 plasma etching of the CVD graphene can produce surface structures similar to bubbles and nano-prism deformations in epitaxial graphene. During this plasma treatment many holes appear in the top layer, where the edges of the holes glue to the bottom of the basal plane of the graphene layer. Such edges become comparable to the foundations found in bubbles of epitaxial graphene. Similar phenomenon has been located in newly designed quasi free-standing epitaxial graphene layers. This type of graphene has been developed through epitaxial Graphene-SiC intercalation. Such a challenging method is based on the decoupling of the graphene layer from the buffer layer-SiC interface via several intercalants [2 plasma etching. Indeed, the free-standing nature of the grown graphene layers obtained by both techniques makes them comparable to reshaped graphene layer, that is, bubbles and pyramid, where similar properties exist. More directly the plasma etching perforation and epitaxial graphene-SiC intercalation may be related to theoretical predictions of graphene Kirigami [Similar phenomena have been reported in bilayer CVD graphene perforated by Nitrogen (N) plasma . Here, trcalants . These lrcalants ,167,168.Kirigami .  ratio to determine graphene layer numbers. The obtained results agreed with earlier electrical measurements. Additionally, a new method based on the substrates longitudinal optical phonons (LOOPC) mode has been used to establish the density of charge in epitaxial graphene for different layer shape and numbers have been examined. We have found that the substrate self-compensates the graphene layer charge without any external doping. The new density of charge at the substrate–graphene layer interface emerges as a capacitor that is dominated by the graphene quantum capacitance effect.In this review, we demonstrate the structural and electrical properties of the SiC substrate. Also, we describe precisely epitaxial graphene growth for both terminations—silicon and carbon—from the SiC substrate. We introduce the Raman analysis of SiC substrate and graphene. We demonstrate different Raman spectroscopy methods for determining the number of graphene layers. The electrical properties of various epitaxial graphene systems are discussed. The density of charge in epitaxial graphene systems of different shapes and layers number is obtained. The studies were done using Raman modes in graphene, particularly focusing on the behavior (I


This article is devoted to the in silico study of the sensory properties of mono- and bilayer phospholipid-graphene films with planar and curved graphene sheets. The DPPC  molecules are considered as phospholipid structures. These molecules are part of lipid bilayers, liposomes and cell membranes. To find a way to improve the sensory properties of phospholipid-graphene films, we studied the effect of the curvature of the graphene sheet on the charge transfer and electrical conductivity of the films. The distribution of the electron charge density over the film atoms was calculated using the self-consistent-charge density-functional tight-binding method (SCC-DFTB). The calculation of the current through phospholipid-graphene films was carried out within the framework of the Landauer–Buttiker formalism using the Keldysh nonequilibrium Green function technique. As a result of the calculations, the optimal configuration of the arrangement of DPPC molecules between two graphene layers was established. This configuration provides the maximum possible increase in current to 1 μA at low voltages of ~0.2 V and is achieved for curved graphene with a radius of curvature of ~2.7 nm at individual points of graphene atomic network. Graphene-based materials have attracted increasing attention due to their two-dimensional structure of atomic thickness, large specific surface areas and excellent electro-conductive and mechanical properties . The comGraphene-phospholipid structures are comprehensively studied using both experimental research methods and computer simulation methods ,15,16,17The use of graphene-phospholipid complex is very promising in a wide range of biological applications ,26,27,28The objects of this study are mono- and bilayer phospholipid-graphene films. The monolayer film is formed by a graphene sheet with adsorbed DPPC molecules. The bilayer film consists of two graphene monolayers with DPPC molecules between them. The aim of this paper is to find a way to improve the sensory properties of phospholipid-graphene films by curving graphene monolayers using in silico computational methods. In this paper, we consider only DPPC  molecules, since they are part of the cell membrane and liposomes used actively in biomedicine ,31,32,33The search for the equilibrium configuration of the monolayer film supercells was carried out using the SCC-DFTB method , which hT(E) is the transmission function, which determines the total quantum-mechanical transparency of the conductive structure over all independent conduction channels for an electron with energy E. f1 and f2 are the Fermi–Dirac functions that characterize the energy levels of the source (electrode 1) and drain (electrode 2), corresponding to the energy level of the conducting structure. The electron transmission function T(E) and current I were calculated using the Mizar program  [T(E), otherwise the computational resources are insufficient to calculate the transmission function using the SCC-DFTB method. In this study, to speed up the calculation of T(E) of polyatomic supercells, we used the original method implemented in the Mizar program software [T(E) for a small number of k-points of the first Brillouin zone and then interpolate it for any k-point of the first Brillouin zone and obtain the complete transmission function T(E).Calculation of the current through phospholipid-graphene film was carried out within the framework of the Landauer–Buttiker formalism  accordin Russia) . This pr Russia)  for studsoftware . This meTo study the properties of phospholipid-graphene films, first, it is necessary to construct energetically favorable (equilibrium) atomistic models of the film supercells. Therefore, we first carried out a search for the equilibrium configuration of the arrangement of the DPPC molecule on the graphene monolayer. y-axis) by one degree, while optimizing the atomic structure of the entire supercell. Double optimization was carried out when all the coordinates of all atoms and the translation vectors of the supercell along the X and Y directions act as variable parameters. The optimization criterion was the value of the total energy totE. That is, the supercell structure was considered as optimized when a global minimum of total energy was achieved for this atomic configuration. Thus, 360 optimization procedures for the DPPC-graphene structure were carried out.To identify the energetically favorable DPPC configuration relative to the graphene monolayer, the relaxation scanning approach was implemented. It consists in sequentially rotating the DPPC molecule around its axis  ± 0.012 eV/nmpotE and the energy equE corresponding to the equilibrium structure. The value of equE was determined as the average value, around which the value of potE fluctuates after a few picoseconds of MD simulation. The equilibrium energy equE is −27,073.186 eV in the case of planar graphene sheet and equE = −27,078.693 eV in the case of curved graphene sheet. In the case of planar graphene, upon reaching the equilibrium configuration of a phospholipid-graphene bilayer film, the potE energy fluctuations amounts to an average of 0.357 eV; the maximum deviation from equE value is not more than 0.01%  characteristics of graphene layers in phospholipid-graphene films taking into account the calculated redistribution of electron charge density in the films. To simulate the current transfer through a given supercell in a given direction, for example, along the x-axis, an adjacent supercell in the negative direction of the x-axis is selected as electrode 1 and it is translated to infinity in this direction. A neighboring supercell in the positive direction of the x-axis is selected as electrode 2, and it is translated to infinity in this direction. Some voltage V is applied between the electrodes; its value varies in the interval (0–1) V with a step of 0.01 V. Such a connection to the electrodes is shown in y-axis perpendicular to the direction of current transfer. DPPC molecules are not shown in To clarify and better understand the effect of the curvature and packing density of DPPC molecules on electrical conductivity and sensory properties of bilayer phospholipid-graphene films, we calculated the current–voltage  a model with a supercell of 22.14 × 29.82 Å in size containing 504 carbon atoms; and (2) a model with a supercell of 14.76 × 29.81 Å in size containing 336 carbon atoms. These atomistic models of the film differ slightly in curvature, but they differ significantly in charge transfer. I–V characteristics for a planar individual graphene monolayer (black curve): two dashed I–V curves for the model with a supercell of 22.14 × 29.82 Å in size (they are indicated by the number “1”) and two solid I–V curves for the model with a supercell of 14.76 × 29.81 Å in size (they are indicated by the number “2”). The I–V curves for current transfer in the zigzag direction of graphene are marked in red, and the I–V curves for current transfer in the armchair direction are marked in green. Both directions of current transfer are the same for graphene, but they differ markedly for phospholipid-graphene films. Both atomistic models of the films are characterized by a rapid increase in current in the armchair direction with increasing voltage. For example, at a voltage of 0.4 V, the difference between the current values in the zigzag and armchair directions is 87.5% and 125%, respectively. A similar noticeable domination of the armchair direction in current transfer is also observed in the third atomistic model of the film with two DPPC molecules in one supercell. The I–V curves of this atomistic model are shown in I–V curves of graphene for comparison. In this case, at a voltage of 0.4 V, the current value is insignificant, but greater than for atomistic models with one DPPC molecule in a supercell. At a voltage of 0.2 V, the current is ~30% greater  for each of them. The calculated T(E) functions for Models “2” and “3” are presented in T(E) curves meet the colors of the corresponding I–V curves for these models in T(E) curve stands out in I–V characteristic for an atomistic model with two DPPC molecules in the armchair direction of current transfer. It can be seen that the function T(E) in this case sharply increases near the Fermi level  in comparison with all other cases. This explains the rapid increase in current at low voltages.To clarify the reasons for this difference in the I–V characteristics. As can be seen in T(E) as compared to the initial graphene, providing a sharp increase in the electron conductivity near the Fermi level. Note that the optimal configuration of the arrangement of DPPC molecules, which provides the above effects, is achieved only in the case of curved graphene. The maximum effect of charge transfer is observed when the radius of curvature becomes equal to ~2.7 nm at some points of graphene layers.Monolayer and bilayer phospholipid-graphene films were studied from the standpoint of improving their sensory properties. One of the important points for use of material in sensors is the rapid response of the material, that is, the appearance of an electric current in the circuit even at low voltages. We found the optimal configuration of the arrangement of DPPC molecules between two graphene layers, which provides the best Thus, bilayer phospholipid-graphene films can be used in sensors, with one of the graphene layers acting as the working surface. One of graphene layers has a negative charge of ~(−0.2e) and the other graphene layer is almost neutral. It also favorably distinguishes such bilayer films in comparison with monolayer ones, since the charged graphene layer will more actively manifest itself due to the electrostatic effect.


Brain-derived neurotrophic factor (BDNF) is one of the most abundantneurotrophins in the central nervous system. Numerous studies suggestthat BDNF has extensive roles by binding to its specific receptor, tropomyosin-related kinase receptor B (TrkB), and thereby triggering downstream signaling pathways. Recently, growing evidence highlightsthat the BDNF/TrkB pathway is expressed in the cardiovascular system andclosely associated with the development and outcome of cardiovascular diseases (CVD), including coronary artery disease, heart failure, cardiomyopathy, hypertension, and metabolic diseases. Furthermore, circulating BDNF has also been revealed as a new potential biomarker for both diagnosis and prognosis of CVD. In this review, we discuss the current evidence of the emerging role of BDNF/TrkBsignalingand address the challenges that remain in translating these discoveries to novel therapeutic strategies for CVD. The neurotrophin (NT) family is generally reserved for four structurally related factors: neuronal growth factor (NGF), brain-derived neurotrophic factor (BDNF), NT-3, and NT-4/5 . Among t2O2)-induced damage of C2C12 myoblasts by scavenging reactive oxygen species (ROS) and activatingthe PI3K/Akt and ERK signaling pathways [To date, a variety of signaling pathways have been found to be regulated by BDNF/TrkB, such as PI3K/Akt, mitogen-activated protein kinase (MAPK), PLCγ, nuclear factor κB (NF-κB),and so forth . Theoretpathways . Huangetpathways . In contpathways , which cpathways .However,pathways . Moreovepathways . More woBecause BDNF is a widely existing neurotrophic factor in brain tissue and peripheral nerves, it can affect nerve development, survival, and postinjury repair. Previous studies mainly focused on its role in neurological diseases and found that BDNF had protective effects against cerebral ischemic injury. Currently, more and more studies suggest that the BDNF/TrkB axis plays an important role in cardiovascular function. Here, we summarize current advancements of the critical role of BDNF/TrkB in common CVD .Several previous studies firstly paid attention to theeffects of BDNF on myocardial infarction (MI). It was found that intramyocardial injection of BDNF post-MI induced angiogenesis in the infarct area and the peripheral area. The study also found that simultaneous injection of basic fibroblast growth factor (bFGF) and BDNF significantly increasedmicrovascular density in a canine infarct model and improved blood perfusion and cardiac function compared withbFGF appliedalone . In fact2+ cycling, contractility, and relaxation via regulating Ca2+/calmodulin-dependent protein kinase II (CaMKII) signals. However, TrkB.T1 was increased in the failing rodent myocytes, which is insensitive to BDNF due to lack of tyrosine kinase activity [A study found that BDNF enhanced normal cardiomyocyte Caactivity . Moreoveactivity . Exercisactivity . On the activity . A very activity . Serum Bactivity . Exercisactivity .We previously found that BDNF/TrkB signaling was impaired in DOX-induced cardiotoxicity. Cardiac BDNF protein expression was lower in DOX-treated mice than control mice. Intravenous injection of recombinant human BDNF inhibited cardiac dysfunction and cardiomyocyte apoptosis by activating Akt signals . SimilarL. plantarum and insulin supplementation inhibited cardiac apoptosis and fibrosis by modulating BDNF and enhancing TrkB expression [Studies have clarified that circulating BDNF level is decreased in type 2 diabetic patients. Replenishing BDNF or enhancing its downstream signaling pathway may be beneficial for diabetes . Cardiacpression . In addipression . It was pression ,43. In ppression . BDNF ampression .Studies have demonstrated that BDNF/TrkB signaling is required for normal cardiac contraction and relaxation. Fulgenzi et al. reported thatcardiac BDNF maintained heart contraction force and long-term homeostasis through TrkB.T1. Deletion of TrkB.T1 in the cardiomyocytes caused dysregulated calcium signaling and cardiomyopathy . In contIt wasreported that intravenous treatment of 7,8-DHF produced an antihypertensive effect in spontaneously hypertensive rats due to its vasodilation role . Another3 receptor expression [It should be noted that BDNF and TrkB are expressed in various cell types such as cardiomyocytes, endothelial cells (ECs), vascular smooth muscle cells (VSMCs), and fibroblasts in the cardiovascular system, thus the potential role of BDNF/TrkB in noncardiac cells should also be seriously considered. In particular, it was noticed that BDNF/TrkB participated in the activities of fibroblasts. For example, Cherubini and colleagues found that TrkB was expressed in lung fibroblasts, and activation of BDNF/TrkB axis promoted the epithelial–mesenchymal transition in idiopathic pulmonary fibrosis . Similarpression . The detSeveral clinical studies paid attention to the association of circulating BDNF in either plasma or serum with CVD, including coronary artery disease, angina, HF, and diabetes. In 2005, Ejiri et al. firstly reported that coronary sinus and aorta BDNF level was significantly higher in the unstable angina group than the stable effort angina and noncoronary artery disease groups . It was At present, studies on BDNF structure, molecular morphology, action, and mechanism have achieved stage achievements. The emerging role of BDNF/TrkB in CVD was paid more and more attention. Several clinical applications of BDNF have been developed, which can treat common neuropsychiatric diseases, such as Parkinson’s disease, Alzheimer’s disease, depression, schizophrenia, and so forth ,71,72,73