Dysphagia occurs in multiple respiratory pathophysiologies, increasing the risk of pulmonary complications secondary to aspiration. Reflux associated aspiration and a dysregulated lung microbiome is implicated in Idiopathic Pulmonary Fibrosis (IPF), but swallowing dysfunction has not been described. We aimed to explore oropharyngeal swallowing in IPF patients, without known swallowing dysfunction.MBSImP).Fourteen consecutive outpatients with a secure diagnosis of IPF were recruited and the 10-item Eating Assessment Tool (Eat 10) used to assess patient perception of swallowing difficulty. Oropharyngeal swallowing was assessed in ten patients using Videofluoroscopy Swallow Studies (VFSS). The studies were rated using validated scales: Penetration-Aspiration Scale (PAS); standardised Modified Barium Swallow Impairment Profile (MBSImp for oral impairment was 5, range [3–7] and pharyngeal impairment 4, range [1–14] indicating, overall mild alteration to swallowing physiology.EAT-10 scores indicated frank swallowing difficulty in 4/14 patients. Videofluoroscopy Studies showed that 3/10 patients had airway penetration, and one aspirated liquid without a cough response. Median We conclude that people with IPF can show a range of swallowing dysfunction, including aspiration into an unprotected airway. To our knowledge, this is the first report on swallowing physiology and safety in IPF. We believe a proportion of this group may be at risk of aspiration. Further work is indicated to fully explore swallowing in this vulnerable group. Idiopathic pulmonary fibrosis (IPF) is a chronic life-threatening lung disease, with poor prognosis, characterised anatomically by progressive scarring of the lung and symptomatically by exertional dyspnoea, with irreversible loss of pulmonary function .While the aetiology is unknown, emerging data suggest an important association with aspiration  linked wThe pharynx is a shared tube for breathing and swallowing. Because of this, normal swallowing is a complex process, coordinated with breathing to protect the airway. Patients with chronic respiratory diseases are at risk of oropharyngeal dysphagia and swallowing difficulties may go unreported , 8. EarlConsecutive outpatients with a secure diagnosis of IPF according to European Respiratory Society/American Thoracic Society were recruited from the regional Interstitial Lung Disease (ILD) clinic at the Royal Victoria Infirmary, Newcastle upon Tyne between March 2014 and February 2015. Outpatients with IPF are reviewed on a regular basis at the ILD clinic at the Royal Victoria Infirmary. Recruits were drawn from within this clinic population. Written informed consent was obtained using a printed form prior to the study assessment. Inclusion criteria included competent adults (over the age of 18) with a secure diagnosis of IPF according to the American Thoracic Society (ATS), European Respiratory Society (ERS), Japanese Respiratory Society (JRS) and Latin American Thoracic Society (LATS) clinical practice guidelines . AccuratEating Assessment tool (EAT-10) is a quick, self-administered and widely used validated questionnaire, which can be used to assess dysphagia symptoms . It has Swallowing was assessed by a Videofluoroscopy Swallow Study (VFSS). VFSS is a dynamic radiographic examination using fluoroscopy to capture and record real-time bolus flow throughout all stages of swallowing. A Speech and Language Therapist and a radiologist, performed the VFSS examinations. The examination typically includes testing different bolus volumes and constituents . The parStudies were rated using two validated scales:(MBSImP), a tool used to evaluate swallowing efficiency that measures 17 physiological components of adult swallowing mechanism using ordinal scaling. Ratings of 0 to 2, 3, or 4 points per component, with each score representing a unique observation of either structural movement, bolus flow or both from the VFSS [The standardised Modified Barium Swallow Impairment Profile the VFSS , 15. It the VFSS . Scores the VFSS .The Penetration Aspiration Scale (PAS) is a tool used to evaluate swallowing safety, ranging in value from 1 to 8, recording the presence of laryngeal penetration/sub-glottic aspiration  . PenetraHigher scores for both tools indicate poorer swallowing.We recruited 14 participants with IPF: 10 males, 4 females, median: 68 years, range 51–82 years. Ten IPF patients: 7 males, 3 females, median: 63 years, range 51–77 years underwent the VFSS  had airway penetration. Only patient 1 aspirated liquid without a cough response; this patient had reduced laryngeal elevation and incomplete laryngeal vestibular closure, resulting in the residue laying below the true vocal chords without a response to eject the aspirated liquid , had markedly raised total EAT-10 scores, with values of 25, 15, 14 and 13.We are unaware of previous EAT-10 data in people with IPF. A recent study in 30 patients with Acute Exacerbations of COPD showed that 67% of patients had a raised EAT-10 score, compared with 23% of patients with cardiac disease .In our study, subjective dysphagia symptoms reported by the EAT-10 tool were not consistently related to the nature and severity of the oropharyngeal swallow impairment observed during VFSS. Patient 1 who demonstrated aspiration on videofluorocopy had the highest EAT-10 score of 25, but patients who scored 13 and 14 in the EAT-10 tool had relatively normal swallow physiology detected during VFSS (Table: 2). Our exploratory findings in a limited number of patients are consistent with previous studies in COPD which have previously shown a weak association of EAT-10 with objective measurements of dysphagia , 12. FurMBSImP classification system [MBSImP may not correspond to an unsafe swallow. Higher scores on some components of the MBSImP may be regarded as ‘normal’; for example, the bolus may enter the pharynx before swallow is initiated even in healthy individuals and some features may be part of a healthy ageing profile [MBSImP. In the scarce literature, normal older swallowers sometimes have scores of 2 and 3 in PAS and our findings may therefore represent the combined effects of both normal aging and IPF pathophysiology and require further study [Videofluoroscopy studies demonstrated a range of physiology in the ten patients studied. The swallow from laryngeal elevation onwards was consistently and highly disrupted in patient 1 and 2; these two patients had an abnormal physiology according to the n system . However profile . On PAS er study . There iHelicobacter pylori [Parenchymal lung scarring and hypoxaemia may disrupt the complex coordination of normal swallowing and breathing function and in principle dysphagia may contribute to a complex dysregulated aerodigestive homeostasis in people with IPF. The true incidence of dysphagia in IPF is unknown but oral dysbiosis has been linked with a range of lung diseases including, pneumonia, COPD, and lung cancer . The orar pylori , which hr pylori .As fibrotic changes progress and lung function declines in IPF, the affected lung may be expected to become more susceptible to external challenges such as aspiration. Non-sterile aspiration, related to dysphagia and unprotected by cough, therefore represents a candidate source of complex lung injury and microbiome dysregulation. Aspiration is noted to be a trigger of threatening acute exacerbations . Acute eThis prospective consecutive case series is descriptive in a limited number of patients. Further exploration is needed to establish the association between dysphagia and IPF, and the clinical significance of such a link. It would be of interest for further studies to assess if the prevalence of dysphagia in IPF is above that expected in a population of a similar age and to compare our IPF findings to other patient groups with comorbidities that could increase the risk of swallowing problems/aspiration (e.g. frailty). Our experience indicated that such studies are possible in people with IPF but also underline that these are challenging. Of 18 patients approached in our study five died, and three deaths occurred in fourteen consented patients, before videofluoroscopy could be performed. In other settings, simple bedside tests of dysfunction are clinically informative in swallowing pathophysiology  and toge


The COVID-19 pandemic has drastically disrupted global healthcare systems. With the higher demand for healthcare and misinformation related to COVID-19, there is a need to explore alternative models to improve communication. Artificial Intelligence (AI) and Natural Language Processing (NLP) have emerged as promising solutions to improve healthcare delivery. Chatbots could fill a pivotal role in the dissemination and easy accessibility of accurate information in a pandemic. In this study, we developed a multi-lingual NLP-based AI chatbot, DR-COVID, which responds accurately to open-ended, COVID-19 related questions. This was used to facilitate pandemic education and healthcare delivery.https://t.me/drcovid_nlp_chatbot). Second, we evaluated various performance metrics. Third, we evaluated multi-lingual text-to-text translation to Chinese, Malay, Tamil, Filipino, Thai, Japanese, French, Spanish, and Portuguese. We utilized 2,728 training questions and 821 test questions in English. Primary outcome measurements were (A) overall and top 3 accuracies; (B) Area Under the Curve (AUC), precision, recall, and F1 score. Overall accuracy referred to a correct response for the top answer, whereas top 3 accuracy referred to an appropriate response for any one answer amongst the top 3 answers. AUC and its relevant matrices were obtained from the Receiver Operation Characteristics (ROC) curve. Secondary outcomes were (A) multi-lingual accuracy; (B) comparison to enterprise-grade chatbot systems. The sharing of training and testing datasets on an open-source platform will also contribute to existing data.First, we developed DR-COVID with an ensemble NLP model on the Telegram platform : 0.826–0.851] and 0.922 [95% CI: 0.913–0.932] respectively. For overall and top 3 results, AUC scores of 0.917 [95% CI: 0.911–0.925] and 0.960 [95% CI: 0.955–0.964] were achieved respectively. We achieved multi-linguicism with nine non-English languages, with Portuguese performing the best overall at 0.900. Lastly, DR-COVID generated answers more accurately and quickly than other chatbots, within 1.12–2.15 s across three devices tested.DR-COVID is a clinically effective NLP-based conversational AI chatbot, and a promising solution for healthcare delivery in the pandemic era. The COVID-19 pandemic has profoundly changed our lives and disrupted global healthcare systems. The demand for medical services is increasing despite persistent movement and social contact limitations. This is further complicated by misinformation related to COVID-19 on the internet and social media , which mNatural language remains a fundamental way information is communicated in the healthcare setting. NLP is a range of computational techniques used to automatically analyze and represent human language . It has https://www.facebook.com/WHO/), as well as the Centers for Disease Control and Prevention Coronavirus Self-Checker (https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/coronavirus-self-checker.html)  , 17. HeaCurrent medical COVID-19 chatbots face several limitations. First, most of these chatbots are created with English as the intended medium, thus limiting the utility for non-native English speakers . In comphttps://t.me/drcovid_nlp_chatbot. English was used as the base to train the chatbot. A training dataset of unique questions-answer pairings was first created to train the NLP architecture, covering a range of common COVID-19 topics in the English language. Each question-answer pairing was expanded into sub-questions to increase the variety and scope of the training dataset. Subsequently, an independent testing dataset comprising questions-answer pairings in English was created to assess English accuracy. Collaborators were also involved in providing testing questions in nine widely-spoken non-English languages. These multi-lingual questions were translated to English questions using Google Translate Application Programming Interface (API). The questions in English were used as the input for our NLP ensemble model. Similarity calculation was used to retrieve the output, that is, the top 1 and top 3 closest matching answers. If the original question was not in English, then the output would be translated to English using Google Translate API, and subsequently displayed on the Telegram application. Our study did not involve patients, and was hence exempt from requirements of approval by the Singhealth Institutional Review Board.In this multi-center prospective study, we developed a multi-lingual conversational chatbot, DR-COVID, hosted on the Telegram platform to answer COVID-19 related queries . The TelInclusion criteria for input data were topics relating to COVID-19, whereas exclusion criteria were those not relevant to COVID-19. The training and testing question-answer pairs were developed sequentially in English, consolidating data from publicly available sources . The traTwo categories of MQA were created: Singapore-centric, and global. Singapore-centric questions were defined as those localized to Singapore geographically, specific to Singapore's population, policies, rules, and regulations. Global questions included those pertaining to global statistics, general information on COVID-19, and policies with impact on a global scale.st Jan 2021 to 1st Jan 2022. Data was vetted for repetition and grammar twice, and the finalized content vetted again.Each MQA was expanded into 5 to 15 unique sub-questions, and each sub-question grouped and identified for answer retrieval based on the corresponding MQA. Next, the training dataset was independently created with at least three questions per MQA. A total of 218 MQA pairings were developed from the period of 1https://github.com/leixiaofeng-astar/drcovid_nlp_chatbot.The training dataset will be made available at GitHub on manuscript acceptance: Two separate large corpus-trained NLP transformer models, spaCy library  and locaRule-based question-answer retrieval was performed using feature extraction, and representation for the input test questions. Subsequently, a similarity score was generated for each MQA, with the highest matched score being the retrieved answer and therefore output. If similarity score fell below the pre-set threshold of 0.85 in our study, the top 3 closest matching MQAs were retrieved as the output instead.The ensemble model underwent three iterations of improvement before being used for eventual assessment. Chatbot performance was assessed based on the accuracy, AUC, precision, recall, and F1 score for the overall, and top 3 answers generated. A positive response was recorded for the top 3 answers if any one answer was appropriate. Grading was performed by two investigators independently. In the event of disparate grading, a discussion was held to reach a consensus, failing which a third investigator would provide the final decision. Subsequently, we invited ten collaborators to each contribute 20 English questions in an open-ended format, and thereafter assessed the performance of the new questions. Confidence intervals were calculated using R (v4.0.5).The NLP models were trained with English corpus. The backend software used Google Translate API to translate the target language question into English as an intermediary step language, followed by analysis and question-answer retrieval, and lastly re-translation by Google Translate API back to the target language. The selected target languages included Chinese, Malay, Tamil, Filipino, Thai, Japanese, French, Spanish, and Portuguese.We invited collaborators to assess the multi-lingual aspect of DR-COVID, with each contributing 20 questions in an open-ended format to assess the accuracy of the generated response. Ten collaborators were invited to assess the chatbot in Chinese and Malay; two in Spanish; and one each for the remaining languages Tamil, Filipino, Thai, Japanese, French, and Portuguese.https://ask.nhsinform.scot/)  assessment , which im.scot/) . This waThe deployments of DR-COVID chatbot application were compared, to highlight the differences in the throughput performance of Graphical Processing Units (GPU) vs. Central Processing Units (CPU). In this study, benchmarking tests were performed between CPU and GPU. NVIDIA TITAN Xp GPU and Intel(R) Xeon(R) W-2145 CPU were used during the evaluation. Data regarding memory usage with sequential time profiler and memory profiler was obtained using 100 users and 3 questions.A total of 2,728 questions in English, comprising 12,90 Singapore-centric and 1,438 global questions, were developed for the training dataset. Eight hundred twenty-one new questions in English were created as the testing dataset for assessment of accuracy, consisting of 335 Singapore-centric and 486 global questions .In terms of primary outcomes of interest, DR-COVID achieved an overall accuracy of 0.838 [95% confidence interval (CI): 0.826–0.851], comprising a proportion of 0.812 [95% CI: 0.791–0.832] correct Singapore-centric answers, and 0.856 [95% CI: 0.841–0.871] correct global answers. The top 3 accuracy was 0.922 [95% CI: 0.913–0.932], comprising 0.895 [95% CI: 0.881–0.914] and 0.940 [95% CI: 0.931–0.951] correct Singapore-centric and global answers, respectively .https://github.com/leixiaofeng-astar/drcovid_nlp_chatbot. Greater details of ROC calculation and its parameters have been appended in Chatbot performance assessment can be formulated as a classification problem, and its accuracy validated in qualitative and quantitative manners, where each MQA belongs to a particular class. Receiver Operating Characteristic (ROC) analysis was performed to assess the classification problem, which enable values of AUC, precision, recall, and F1 score to be gleaned. For overall results, the AUC was 0.917 [95% CI: 0.911–0.925], precision was 0.864 ± 0.193 [95% CI: 0.852–0.876], recall was 0.835 ± 0.218 [95% CI: 0.822–0.850], and F1 score was 0.829 [95% CI: 0.818–0.841]. For top 3 results, all metrics showed an improvement; AUC was 0.960 [95% CI: 0.955–0.964], precision was 0.938 ± 0.124 [95% CI: 0.931–0.946], recall was 0.920 ± 0.145 [95% CI: 0.910–0.929], and F1 score was 0.918 [95% CI: 0.911–0.925]. The confusion matrix diagram and ROC curve for overall results and top 3 results are shown in Ten collaborators were invited to each contribute 20 questions in the English language . When teIn terms of secondary outcomes of interest, nine non-English languages were assessed for accuracy, using a total of 560 questions contributed by the collaborators . Supplemp < 0.05). Amongst other question-answer chatbots tested for comparison, the average time taken for NHS Inform ranged between 2.20 and 2.51s, whereas that of WHO Messenger ranged between 4.04 and 4.85s  Xeon(R) W-2145 CPU @ 3.70GHz 64 GB DDR4 RAM. Using 100 users and three questions each as the test condition, and the sequential time and memory usage recorded was the average throughput 20 times test. To run DR-COVID chatbot on CPU required 3.628 GB, whereas on GPU required 2.936 GB memory plus 600 MB CPU memory. The average time taken per question was 23.52 s for CPU, whereas in comparison, the GPU could achieve a 66.9% reduction in time, at 7.79 s.In this multi-center study, we created a general question-answer chatbot with a training dataset of 2,728 questions to address COVID-related enquiries, incorporated multi-lingual text to text translation, and assessed chatbot performance by evaluating accuracy through external open-ended assessments, and comparing technical performance against enterprise-grade chatbot systems. In terms of primary outcomes of interest, we achieved overall and top 3 accuracies of 0.838 [95% confidence interval (CI): 0.826–0.851] and 0.922 [95% CI: 0.913–0.932] respectively. For overall results, the AUC was 0.917 [95% CI: 0.911–0.925], precision was 0.864 ± 0.193 [95% CI: 0.852–0.876], recall was 0.835 ± 0.218 [95% CI: 0.822–0.850], and F1 score was 0.829 [95% CI: 0.818–0.841]. For top 3 results, the AUC was 0.960 [95% CI: 0.955–0.964], precision was 0.938 ± 0.124 [95% CI: 0.931–0.946], recall was 0.920 ± 0.145 [95% CI: 0.910–0.929], and F1 score was 0.918 [95% CI: 0.911–0.925]. In terms of secondary outcomes of interest, we demonstrated multi-linguicism with 9 non-English languages using an external open-ended testing approach, as well as higher speed and accuracy compared to other chatbots.In our study, we used an ensemble model aiming to overcome technical challenges associated with a single architecture, which gives lower accuracy, higher variance, noise, and bias . The ensExisting literature regarding NLP-based chatbots in the COVID-19 pandemic has been largely experimental or descriptive in nature , 30. NonN = 20 on average, lest the Chinese and Malay languages. Nevertheless, an ongoing analysis is underway to garner and assess more questions for multi-lingual accuracy, as well as to evaluate if the differential accuracy may be attributed to technical or general questions.Next, there were several questions in French of a highly specific nature, which were not within the scope of our existing MQAs, including “can I get infected through aerated steam?” . FurtherAnother Tunisian chatbot Smart Ubiquitous Chatbot, based on Long Short-Term Memory (LSTM) networks, was developed for education, and stress management during the pandemic. It reported an accuracy of 0.92, precision of 0.866, recall of 0.757, and F1 score of 0.808 . SimilarWe demonstrated that when tested on new questions in English provided by collaborators, DR-COVID fared less optimally, with a drop in accuracy from 0.838 to 0.550, compared to using our own testing dataset. These errors are perhaps explainable. Firstly, this variance may illustrate the differential perspectives between the medical community and general public. The training and testing datasets, developed by the internal team comprising medical practitioners and data scientists, tend to be more medical in nature, including “will the use of immunomodulators be able to treat COVID-19?”, and “what is the mechanism of action of rapid COVID-19 tests?”; there was potentially selection bias to some degree. On the other hand, the external questions were contributed by collaborators of both medical and non-medical backgrounds; these relate more to effects on daily life, and coping mechanisms. For example, “is the hospital safe to visit?”. This further illustrates the limitations in our training dataset in covering everyday layman concerns relating to COVID-19 as discussed previously, and therefore potential areas for expansion. That said, we do observe common topics of overlap, such as general information, symptoms, and treatment pertaining to COVID-19.Secondly, despite having undergone several cycles of retraining, our model might not have the most up-to-date information on certain questions. Chatbots require a tedious training and retraining process. Information and policies are constantly changing in a pandemic setting, on both a local and global scale, which necessitates frequent monitoring and updating of the model, to ensure that appropriate information is conveyed. A prime example would be vaccine-related information such as booster dose requirements, newly approved vaccines, and variant-specific efficacy. Our model was not equipped with new information regarding booster vaccines, and was therefore shorthanded in addressing these questions. To circumvent tedious retraining, we could consider reinforcement learning in future implementation, a technique which incentivises the chatbot to learn through trial and error, by “rewarding” correct outputs and “punishing” incorrect answers .Thirdly, insofar as our knowledge regarding COVID-19 is constantly evolving, there remain uncertainties for which it is challenging to give definite answers to. Questions such as “when will the COVID-19 pandemic end?” are difficult to predict, may give seemingly unsatisfactory answers, and therefore affect the accuracy of the chatbot. Ultimately, this difference demonstrates the variability which may arise, and therefore the need to test chatbots externally when implemented in a real-world setting.This study gives hope to the potential expansion and real-world implementation of NLP-DLS chatbots, such as DR-COVID. The use of open-source translation software, with the caveat of its drawbacks as discussed earlier, may improve scalability and multi-lingual customizability. Moreover, integration onto social media platforms–such as Telegram in our case–enables greater reach and convenience, potentially removing geographical constraints ; the WHOFurthermore, information garnered from multiple reliable sources can be presented in a succinct manner, mitigating the dangers of online misinformation . SpecifiLastly, whilst the main purpose of DR-COVID has been to facilitate efficient and accurate information sharing, it may be of utility to explore the inclusion of other tools, including detection of misleading information, triage, risk assessment, monitoring, and general wellbeing. For example, both ensemble- and BERT based DL systems have demonstrated utility in detecting COVID-19 related misinformation on the internet and social media , 41. AnoCOVID-19 is likely to become endemic in time to come . In enviPlanned future studies include expansion to more languages, and integration to web messenger and social media platforms to reach greater audiences. Conversational experience can be refined with contextual awareness to improve relevance of answer retrieval. Future directions would also entail exploration of different but complementary domains such as text-to-speech, and speech-to-speech, which may be of help in specific populations like the visually impaired, or to provide more options for convenience. Other potential use cases in pandemic management include NLP-based risk stratification, contact tracing, and patient monitoring. Finally, DL-based chatbots may be utilized in various medical and surgical specialities for targeted patient education, disease monitoring, and encouraging treatment compliance, amongst others.Chatbots utilizing NLP, a type of conversational AI, have emerged as promising solutions to improve healthcare delivery in the pandemic era. In this study, we developed a multi-lingual NLP-based AI chatbot, DR-COVID, to facilitate healthcare delivery and disease control. Our NLP model, utilizing the ensemble architecture, achieved overall and top 3 accuracies of 0.838 [95% CI: 0.826–0.851], and 0.922 [95% CI: 0.913–0.932] respectively. For overall and top 3 results, AUC scores of 0.917 [95% CI: 0.911–0.925] and 0.960 [95% CI: 0.955–0.964] were achieved respectively. The sharing of training and testing datasets on an open-source platform will also contribute to existing data. Whilst AI-based NLP chatbots can enable healthcare systems to reap public health and resource benefits, clinicians and policymakers should work in tandem to deliver solutions to potential problems in real-world implementation.The original contributions presented in the study are included in the article/LY and WN created the training and testing dataset, collected data, and contributed to study conceptualization. ST and ZW contributed to creation of the training and testing dataset. XL, MY, MP, and XZ conceptualized the methodology of the chatbot model, trained the chatbot, and performed the statistical analysis. LY wrote the first draft of the manuscript. DT provided the overall leadership, conceptualized the study, and as well as procured funding. All authors contributed to manuscript revision and approved the submitted version.


Different characterization techniques were used to analyze the structural and physico-chemical properties of the obtained composites: Scanning Electron Microscopy (SEM)/Transmission Electron Microscopy (TEM)/Energy-dispersive X-ray spectroscopy (EDX) analysis, X-ray powder diffraction (XRD), Fourier-transformed infrared spectroscopy (FT-IR) and X-ray photoelectron spectroscopy (XPS). The photocatalytic performance was evaluated by the degradation of methylene blue under both UVA and visible light irradiation. The nanocomposites show very good photocatalytic activity independent of the presence of reduced graphene oxide, due to the Cu2O/CuO-TiO2 heterojunctions. This finding has been confirmed by the very efficient visible-light-driven degradation of amoxicillin and ciprofloxacin.Easy and cost-efficient modifications of titanium dioxide nanoparticles that improve their efficiency in the visible light domain represent a continuous and challenging research topic. In addition, the effect of graphene on the overall photocatalytic process is still debated. Consequently, herein, we prepared a series of TiO A cuprous oxide (Cu2O)/reduced graphene oxide composite proved to be an efficient visible-light photocatalyst for sulfamethoxazole removal [2 composite [2O/CuO nanorods have been studied and the authors reported on the importance and stability of both copper oxides, together [2O ratio in CuO/Cu2O photocatalysts for methylene blue degradation under visible light and showed a more efficient process when the content of Cu2O is higher [2 in a CuO/Cu2O composite led to an increase in the bandgap energy. Even so, methylene blue photodegradation under visible light improved from about 80% to almost 100% [2O nanospheres by a simple method has been reported [Our current society has a growing need for developing green and economical technologies that make the most out of sunlight. In this context, photodegradation of organic pollutants is of continuous interest to the scientific community, either for dyes  or emergescribed . In ordeescribed . The maiescribed , and graescribed . Among t removal . Moreso,omposite . Other somposite ,10,11,12together . A recens higher . The incost 100% . In addireported . The aut2O/CuO) nanoparticles on the surface (three different weight concentrations 1–3%), and also with graphene of different oxidation degrees—graphene oxide, thermally partially reduced graphene oxide at 200 °C, and thermally reduced graphene oxide at 300 °C. For this, we developed a preparation method that simultaneously accesses both copper oxides, leading to a composite of semiconductors (Cu2O/CuO-TiO2) with reduced graphene oxide. Even though composites containing both Cu2O and CuO onto reduced graphene oxide were reported efficient for photocatalysis [Given the above, we considered it to be of interest to decorate the titania nanoparticles with copper oxides , or visible light irradiation , in order to compare the activity of these catalysts with other reported photocatalysts. We started with the promising idea that the photodegradation of methylene blue by a film-deposited semiconductor develops into an ISO standard . Moreove2 by the thermal decomposition of the impregnated copper nitrate, followed by reduction with a gaseous mixture of 10.5vol.% H2 in Ar (2O-CuO), as detailed further in the XPS section. By mixing them with graphene oxide, we obtained the so-called Cu-TiO2-GO composites. Further thermal treatment, under argon, at either 200 or 300 °C led to Cu-TiO2-trGO200, or Cu-TiO2-trGO300. The detailed experimental procedures are found in the Materials and Methods section.The first step in the preparation of the investigated composites was the obtaining of the Cu-TiOH2 in Ar . The obt2-graphene composites was investigated by TEM/SEM measurements, and is exemplified in The morphology of Cu-TiO2 are present at 25.3°, 37.8°, 48.0°, 53.9°, 62.7°, and 75.0° 2θ angles, which were assigned to the respective (101), (004), (200), (105), (204), and (215) crystal planes of anatase phase TiO2 (JCPDS card file no. 21-1272). As previously reported [The crystalline nature of the prepared composites was examined from the powder XRD spectra represented in reported , the dif2, showing the characteristic peaks at 3439 cm−1 for the stretching, 1525 cm−1 for the bending, and 1375 cm−1 for the deformation of the O–H bond. The intense peak at around 600 cm−1 is assigned to the Ti-O stretching band. In addition, the spectra of the composites with graphene show the C-O-C vibrations at 1065 cm−1 (only for graphene oxide), and 1138 cm−1. The characteristic bands of carbon–carbon bands are shielded by the TiO2 peaks.The structural characterization was further deepened with the FT-IR spectra of the nanocomposites containing 3% copper . These s2O/CuO-containing nanocomposites. The deconvolution of high-resolution XPS data was used to quantify the atomic percentage of each identified chemical bond.The surface chemical composition of the composites was determined from XPS spectra see . The XPS2-GO, the peaks located at 932 eV and 933.3 eV can be assigned also to Cu+ and Cu2+, respectively. The Cu2p shape line for the Cu(3%)-TiO2-trGO200 and Cu(3%)-TiO2-trGO300 samples are similar to previously investigated samples with the identified peaks related to CuO and Cu2O phases. This can be an important finding for the proposed applications of the prepared samples as proved by recent work [2O/CuO heterojunction greatly accelerates the interface charge transfer of the heterojunction, enhancing the photoelectrochemical performance and the stability of the sample.The Cu2p energy level of all samples is composed of main characteristic doublet peaks corresponding to Cu2p1/2 and Cu2p3/2 at ~952.6 eV, and ~932.4 eV, respectively, and shake-up satellite structures at about 10 eV higher than that of the main Cu2p peak, and distinctly broader. The spin-orbit splitting between the two main photoemission peaks (Cu2p1/2 and Cu2p3/2) was determined to be around 20 eV, and was in good agreement with the values reported in the literature [ent work , which s2 hybridized carbon atoms, sp3 hybridized carbon atoms, C–O/C–O–C of hydroxyl or epoxy group, C=O/O–C=O of carbonyl groups, carboxy acid, or ester groups, respectively [The C1s deconvolution was performed on each C1s spectrum to identify the chemical bonding of C atoms , right. ectively .The position of the Ti2p doublet for the investigated samples and the binding energy separation between the Ti2p3/2 and Ti2p1/2 peaks (5.7 and 5.8 eV), as shown in 2 nanocomposite presents a broad absorbance around 700 nm, characteristic of CuO nanoparticles [gE) of the photocatalysts , are shown in 2 values); the maximum adsorption capacity (mq) is 8–10 mg methylene blue/g photocatalyst. The RL value is situated between 0 and 1 for the 1% copper-containing photocatalysts, indicating a favorable adsorption process. The linear fittings according to the Freundlich model are presented in 2 values generally show a better fit to the Freundlich model for the 3% copper-containing materials.The quantitative evaluation of the equilibrium data of methylene blue adsorption on 1 or 3% copper-containing titanium dioxide nanoparticles and graphene composites was realized by the Langmuir and Freundlich adsorption isotherm models. Each of the two models offers some information on the adsorption process and mechanism. The linear fittings according to the Langmuir model are presented in −5 M) solution concentrations versus time during the overall adsorption/photodecomposition process under visible and UV-A light, for Cu -TiO2-graphene (0.5 mg/mL) is shown in The decrease in methylene blue  or comparable photocatalysts. The comparison was limited by the different initial MB concentrations, but also the employed light source (even for a similar MB starting concentration). The power of an irradiation lamp energy consumption (Watt) does not show the amount of light it produces (lumens). As such, without measuring the irradiance it is hard to find common ground for comparing the many different photocatalysts [In order to have a fair comparison with previous data and to understand our results, we extracted data in the table below . They shatalysts . We assu2 (dark blue line) and TiO2 with reduced graphene (violet line). The photodegradation experiments of methylene blue dye show the high efficiency of these catalysts, independent of the presence of any graphene  and the half-life time (t1/2) of the corresponding UV-A-driven photocatalytic processes -TiO2-trGO300) shows little influence on the photocatalytic degradation of methylene blue under UV-A irradiation -TiOadiation . The metadiation . The pH adiation .2O being easily transferred to either the conduction band of TiO2 or CuO, due to the favorable positions of the bands  has not been chosen properly due to misleading discoloration conclusions. In order to verify that the investigated composites (copper oxides and graphene containing titania nanoparticles) are good photocatalysts and to prove the importance of band edge positions of the heterojunction, we tested three of them for the visible-light-assisted photodegradation of amoxicillin (AMX) and ciprofloxacin (CPX). The two emerging organic pollutants are very well decomposed , indepen2-P25) from Evonik.All the reagents were commercially available and used as purchased. All water-based solutions were prepared using either distilled or Milli q water. Graphene oxide (GO) was prepared by a reported procedure . Methyle2 nanocomposites were prepared by liquid impregnation method, according to the following procedure: Titanium dioxide  was impregnated with an appropriate aqueous solution of copper nitrate (Cu(NO3)2.3H2O) in order to obtain one of the targeted metal loadings , and then was left to dry. The resulting powder was thermally treated at 450 °C, in an argon atmosphere for two hours, so that copper nitrate is decomposed to copper oxide nanoparticles. This was followed by a thermal treatment in a reducing gaseous mixture of 10.5 vol.% H2 in Ar, at a continuous flow and 280 °C for 90 min, in order to partially reduce the copper oxide to copper nanoparticles.The starting Cu-TiO2 was denoted as 2-GOCu-TiO. These composites were seated in a quartz boat into a temperature-programmed furnace. Argon gas was flown into the furnace chamber at a flow rate of 0.25 l min−1 and the temperature reached 300 °C in 30 min, resulting into Cu-TiO2 with thermally reduced graphene oxide at either 200 °C, denoted as 2-trGO200,Cu-TiO or 300 °C, denoted as 2-trGO300.Cu-TiOThe freeze-dried GO (150 mg) together with 1.5 g of Cu-TiOThe adsorption and photodegradation steps were simultaneously studied, in the same experimental conditions, by changing the concentration of the methylene blue buffer solution. For this, prior to the irradiation, batch equilibrium experiments (adsorption/desorption) were performed in the dark for over 120 min. The effect of the initial concentration on its adsorption thermodynamics was determined using a given quantity of photocatalyst in phosphate buffer (pH = 7), and varying the MB concentration. After reaching equilibrium, the visible (or UVA) light was turned on, and, at various time intervals, the UV–Vis spectra of the solutions were measured. Finally, the results were calculated by the average values of triplicate measurements of triplicate experiments.eq), and the residual ratio (C/C0) were calculated with the following formulae:iC (µg/L) is the initial MB concentration, tC (µg/L) is the MB concentration at time t, w is the photocatalyst quantity (g), and V is the total solution volume (L).The consumed MB quantity/mass unit of photocatalyst (The morphology of the composites was investigated by Scanning Transmission Electron Microscopy (STEM) at 200 kV using an H-7650 120 kV Automatic Microscope  and the SEM investigation was performed using an SU-8230 operated at 30 kV . The samples were prepared by dropping a few µL of diluted ethanol suspension on the nickel grid.Fourier Transform Infrared (FT-IR) Spectra were recorded in a transmission mode on a Bruker Tensor II spectrometer (from KBr pellets) . The UV–Vis absorption spectra were recorded on a V-570 JASCO Spectrophotometer . X-Ray powder Diffraction (XRD) measurements were performed on a Bruker D8 Advance diffractometer , using CuKα1 radiation (λ = 1.5406 Å).−9 Torr during the measurements. The XPS survey spectra were recorded at 30 eV pass energy, and 0.5 eV/step. The high-resolution spectra for the individual elements  were recorded by accumulating 10 scans at 30 eV pass energy and 0.1 eV/step. Data analysis and deconvolution of the spectra into the corresponding components were performed using Casa XPS software (version 2.3.24 PR1.0) with a Gaussian–Lorentzian (30) product function, and a nonlinear Shirley background subtraction. Peak shifts due to any apparent charging were normalized to the C1s peak set to 284.8 eV.X-ray photoemission spectroscopy (XPS) measurements were performed by using a PHOIBOS 150 2D CCD hemispherical energy analyzer XPS spectrometer  equipped with a multichanneltron detector, and an Al/Mg dual-anode as the excitation source. The vacuum in the measurement chamber was maintained at 1 × 102). Comparison experiments with UVA LEDs  with a light intensity of 885 lx (lumen/m2) were performed when considered necessary.The photocatalytic degradation tests were carried out in a Luzchem LZC-4V  Photoreactor equipped with 12 white LEDs (8 W) that emit in the visible domain 420–800 nm, the light intensity being 241.000 lx  influence the results of methylene blue photodegradation, limiting a direct comparison with other reported results.


With the DFT-D3 density functional dispersion correction, the effective core pseudopotential basis set of LANL2DZ was applied to metallic Ni atoms. The reaction energy barriers of NiNx catalysts are different from the co-adsorption structure during the catalytic hydrogenation of graphene-NiNx . The calculated results showed that the energy barrier and selectivity of graphene-NiN4 for ethylene production were 25.24 kcal/mol and 26.35 kcal/mol, respectively. The low energy barrier and high activity characteristics showed excellent catalytic performance of the catalyst. Therefore, graphene-NiN4 provides an idea for the direction of catalytic hydrogenation.In this study, using the application of density functional theory, the mechanism of graphene-NiN In modern industry, ethylene in petrochemical products is one of the mainstream industrial raw materials and plays a critical role in the national economy. Ethylene is also a crucial industrial source and fundamental synthetic monomer . After s2 and employed STEM (scanning transmission electron microscopy), FTIR , and other means to examine the catalysts’ catalytic performance. The results reveal that the catalyst is capable of the selective hydrogenation of acetylene in an ethylene-rich environment under mild conditions. Among them, 100% ethylene selectivity was observed on CuPd0.08/TiO2 catalyst. Choudhary et al. [2, Pd/TiO2, and Au-Pd/TiO2 catalyst. The results revealed that the Au/TiO2 catalyst (average particle size: 4.6 nm) synthesized using reduction-oxidation was discovered to exhibit high selectivity for the synthesis of ethylene through temperature programming, but the acetylene conversion activity and catalyst stability were not satisfactory. Noble metal catalysts are expensive and rare. Thus, researchers turned their attention to the field of non-precious metal catalysts, such as Ni, Cu, Ni, Zn, etc. Zhuo et al. [1/SV-G, and M1/DV-G  using density functional theory (DFT). The results reveal that among them, Pt1/DV-G and Ni1/DV-G are the suitable catalysts for acetylene hydrogenation, and Ni1/DV-G shows higher selectivity for the semi-hydrogenation products of acetylene. Riley et al. [11 nanoclusters were selected as catalysts, and it was discovered that they have good activity and selectivity for acetylene hydrogenation. Researchers decided to replace different supports to enhance the reactivity and selectivity of non-noble metal catalysts.There is a lot of research in the field of catalytic acetylene hydrogenation on noble metal and non-noble metal catalysts at present. Ball et al.  modifiedy et al.  performeo et al.  examinedy et al.  demonstrx as active centers have recently been reported [x  in various reactions [x  series catalysts. The aim of this paper is to investigate the reaction pathway of catalytic hydrogenation of acetylene based on the reaction mechanism of acetylene hydrogenation. The reactivity and selectivity were used to find the excellent catalysts to promote the hydrogenation reaction. In conclusion, this study broadens the research direction of nickel-based catalysts and also provides some insights into the research in the field of non-precious metals.Graphene research has shown a linear growth tendency since it reached people’s field of vision . Graphenreported ,23,24,25eactions ,33,34,352H2 and H2 on graphene-NiNx catalysts were calculated, and the adsorption energies (Eads) and co-adsorption energies (Eco-ads) were defined as in the following:The DFT calculation process employed in this investigation was completed in the Guassian09 software package . Based oads is single adsorption energy of the reactant molecules on the catalyst. Eco-ads are co-adsorption energies. Eads-state is the whole energy of the C2H2/H2 and NiNx, Eco-ads-state is the overall energy of the system. While 2H2 and H2 molecules and NiNx, respectively.Ex catalyst and reactants C2H2 and H2 molecules are shown in x configurations all have a planar structure, while the C2H2 molecule is a linear structure. According to the various doping amounts of N atoms, graphene-NiNx can be classified into four geometric configurations: graphene-NiN1, graphene-NiN2, graphene-NiN3, and graphene-NiN4, each of which has only one stable configuration. However, 2 (A), graphene-NiN2 (B), and graphene-NiN2 (C) according to the doping N atoms’ arrangement. 2 (A) structure, two doped N atoms are in the ortho position in the same five-membered ring; in the graphene-NiN2 (B) structure, there are also two doped N atoms. The atoms are located in different five-membered rings on opposite sides of the Ni atom; the two doped N atoms in the graphene-NiN2 (C) structure are in the ortho position in the same six-membered ring. For this purpose, the average Ni-N and Ni-C bond lengths of graphene-NiNx  were further calculated in this study, and the calculation results are shown in x catalyst, the average Ni-N bond length for doping four N atoms is 1.961 Å, which is the maximum in the catalyst. In the graphene-NiN1 catalyst, Ni-C is 1.880 Å, which is the maximum data for the average bond length of Ni-C.In this study, the optimal graphene-NiNx  catalysts described on the molecular van der Waals surface (electron density 0.001). The enrichment of positive and negative charges causes a color shift toward blue-red. The higher the shift to blue, the higher the charge density of positive charges, and the higher the shift to red, the higher the charge density of negative charges. As shown in x  were plotted [1, the p orbital of C atom and N atom make the main contribution and the secondary contribution, respectively; in the HOMO orbital, the LUMO orbital makes the main contribution is the p orbital of C atom, while the Ni atom makes a greater contribution in an empty orbital. The HOMO orbital contributions of graphene-NiN3 and graphene-NiN4 have similarities with those of graphene-NiN1, and it is evident that the Ni atoms in the LUMO orbitals contribute less to the frontline orbitals. For graphene-NiN2 catalysts doped with two N atoms, the p orbital of the C atom in graphene-NiN2 (A) makes the primary contributions to both LUMO and HOMO orbital, the p orbital of the N atom is second, and the Ni atom has more contributions. In graphene-NiN2 (B) and graphene-NiN2 (C), the p orbital of C makes the main contribution to the HOMO orbital, followed by the d orbital of the Ni atom, and finally the p orbital of the N atom. Among the LUMO orbitals, the p orbital of the C atom is the main contributor, and the p orbital of the N atom is the secondary contributor. We speculate that Ni atoms and the C and N atoms coupled to Ni atoms are the catalyst’s potential active sites.To deeply understand the catalysts’ properties, the total density of states and the partial density of states of the six catalysts of graphene-NiN plotted  and adde2 and C2H2 molecules on the top of the Ni atom, and on the C and N atoms around the Ni atom, to conduct single adsorption behavior, and we obtain the single adsorption structure with the lowest energy. x  catalyst. The figure shows that the NiNx series catalysts tend to be adsorbed on the Ni atoms and the C and N atoms are coupled around the Ni atoms. The Ni atom is slightly tilted upwards when the graphene-NiN4 catalyst is adsorbing acetylene. x  catalysts through further calculation. The C2H2 of the six catalysts are all larger than the H2 adsorption energy, which proves that during the adsorption, C2H2 is more likely to be preferentially adsorbed, and then H2 is adsorbed to form a co-adsorption structure. Furthermore, the single adsorption energies of both C2H2 and H2 are smaller than the co-adsorption energies ensuring that the reaction is performed within a reasonable range.In the whole reaction, adsorption is the most crucial step. We place the reactants Hx  series catalyst for acetylene hydrogenation, this paper draws a detailed reaction path and the corresponding energy diagram. Graphene-NiN1, graphene-NiN2 (A), graphene-NiN2 (B), graphene-NiN2 (C), graphene-NiN3, and graphene-NiN4 catalysts were investigated for activity and selectivity, respectively.In order to fully explore the reaction process of NiN1 acetylene from acetylene to ethane, while 1, the reactants C2H2 and H2 molecules were adsorbed roughly above the catalyst, almost parallel to the graphene-NiN1 substrate. First, the reaction pathway starts with co-adsorption (R) with co-adsorption energy of −8.91 kcal/mol. To obtain intermediate IM1, co-adsorption undergoes transition state TS1 with an imaginary frequency of −1831.70 cm−1. In TS1, the C2H2 molecule was deformed, the two H atoms gradually migrated above the Ni atom, and the H1 atom vibrated to the C1 atom to obtain the intermediate IM1. The reaction energy barrier of this process is 28.68 kcal/mol, which is the rate-controlling step for the hydrogenation of acetylene to ethylene. Secondly, the H2 atom coupled to the C3 atom must undergo two transition states TS2 (imaginary frequency of −258.53 cm−1) and TS3 (−859.12 cm−1) to obtain the intermediate IM3 to produce ethylene. The H2 atom gradually vibrated to the C2 atom during the process from IM2 to TS3 and then to IM3, shortening the bond length from 2.297 Å to 1.082 Å and forming a bond. The energy barriers required for this process are 0.99 kcal/mol and 11.03 kcal/mol, respectively. The co-adsorption structure IM4 was optimized based on IM3 to realize the secondary hydrogenation of acetylene, with the co-adsorption energy of −79.43 kcal/mol. In the co-adsorption of ethylene and hydrogen, ethylene and H3 and H4 molecules are coplanar. Next, IM4 passes through the transition state TS4 with an imaginary frequency of −1652.41 cm−1 to obtain the intermediate IM5. The reaction energy barrier for the hydrogenation of ethylene to ethane is 28.38 kcal/mol, and this process is a rate-controlling step. In TS4, two H atoms and ethylene are placed in an oblique line in space. The H3 and H4 atoms migrate slowly to the top of the Ni atom, and the H3 atom stretches toward the C1 atom. The bond length between H3 and C1 is shortened from 1.557 Å to 1.094 Å between TS4 to IM5, and they are connected to form a bond. Finally, the H4 atom attached to C4 approaches the C2 atom and yields the product ethane (P) after passing through the transition state TS5 with an imaginary frequency of −1164.78 cm−1. The reaction energy barrier for this step is 6.67 kcal/mol. The bond length of H4 and C2 was shortened from 2.329 Å to 1.094 Å from IM5 to the product ethane and then formed a bond. IRC correctly confirmed the structure before and after the reaction path. It can be concluded from 1 catalysts. 1 has poor selectivity.2 (A) acetylene. First, the reaction pathway starts with co-adsorption (R) with co-adsorption energy of −6.26 kcal/mol. It is clear from the co-adsorption graph that the reactants C2H2 and H2 are in the same plane. The co-adsorption to the intermediate IM1 requires the transition state TS1 with an imaginary frequency of −1764.41 cm−1, and the reaction energy barrier of this process is 30.14 kcal/mol. This step is a rate-controlling step for the hydrogenation of acetylene to ethylene. The vibration analysis of TS1 also reveals that C2H2 is deformed, breaking the straight line. The H1 atom vibrates with the C1 atom. In IM1, H1 is bonded to C1, and H2 is bonded to C3 with bond lengths of 1.089 Å and 1.122 Å, respectively. Secondly, due to the long distance between H2 and C2 atoms, IM1 must pass through two transition states to obtain the intermediate ethylene. In TS2 and TS3, only one imaginary frequency is −120.53 cm−1 and −729.51 cm−1. IM1 only needs to pass through TS2 across an energy barrier of 0.92 kcal/mol to produce the intermediate IM2 that requires one inversion of the vinyl group. Crossing TS3 from IM2 to ethylene requires at least 6.24 kcal/mol of energy. Further ethylene hydrogenation from IM3 yields the co-adsorbed structure IM4, and co-adsorption energy was −64.96 kcal/mol. Next, the reaction undergoes a transition state TS4 with an imaginary frequency of −549.92 cm−1 to obtain the intermediate IM5 with a reaction energy barrier of 35.36 kcal/mol. The process of IM5→IM7 requires going through two transition states (TS5 and TS6) with imaginary frequencies of −678.69 cm−1 and −1560.44 cm−1, respectively. The process is separated into two steps of vibration of the H4 atom: the first step is to attack H4 from C3 to C4, and the second is the H4 atom’s vibration from C4 to N1 atom. The energy barriers required for the two steps are 18.57 kcal/mol and 23.42 kcal/mol. Finally, the reaction requires crossing the energy barrier of 55.38 kcal/mol to generate the product ethane (P) and obtain the transition state TS5 with an imaginary frequency of −1378.67 cm−1. This process is a rate-controlling step of the ethylene to ethane hydrogenation process. In TS7, the H4 atom vibrates toward the C2 atom and the bond length is rapidly shortened from 2.251 Å to 1.095 Å. The presence of corresponding reactants and intermediates at each transition state can be confirmed using IRC. 2 (A) catalysts. 2 (A) has certain activity and ethylene selectivity, and is an effective catalyst for acetylene hydrogenation.2 (B) catalysts in the catalytic acetylene hydrogenation process. First, the reaction pathway also starts from co-adsorption (R) with a co-adsorption energy of −6.01 kcal/mol. The process from R to IM1 requires at least 59.05 kcal/mol of energy and experiences TS1 with an imaginary frequency of −1686.31 cm−1. This step is a rate-controlled step for the hydrogenation of acetylene to ethylene. In TS1, the H2 molecule moves above the N atom, the H1 atom vibrates to the C1 atom. Since the H2 and C2 atoms are far apart, they go through five transition states to form ethylene. IM1 must undergo TS2 with an imaginary frequency of −1522.57 cm−1 and overcome the 30.1 kcal/mol energy barrier to produce intermediate IM2. Then, it crosses the energy barrier of 46.4 kcal/mol to reach the intermediate IM3, and there is a transition state TS3 (imaginary frequency is −300.22 cm−1) in this process. IM3 undergoes TS4 with an imaginary frequency of −1195.10 cm−1 and overcomes the energy barrier of 38.76 kcal/mol to produce intermediate IM4. To generate ethylene, intermediate IM4 also needs to experience TS5 and TS6 with imaginary frequencies of −546.91 cm−1 and −893.52 cm−1, respectively. The energy barriers required for these two processes are 6.16 kcal/mol and 44.11 kcal/mol, respectively. The H2 molecule was placed at a position near ethylene in the IM6 conformation, yielding the co-adsorption structure IM7 with a co-adsorption energy of −52.38 kcal/mol. Subsequently, The H3 and H4 atoms were activated and the bond length expanded to 1.516 Å at one point. The intermediate IM8 was formed through the transition state TS7 with an imaginary frequency of −1791.11 cm−1. This process is a rate-controlling step for the hydrogenation of ethylene to ethane, and the reaction energy barrier is 59.80 kcal/mol. The H4 atom connected to the N2 atom needs to undergo TS8 (imaginary frequency of −1529.63 cm−1) to obtain the intermediate IM9, and this process must overcome the energy of 13.23 kcal/mol. Next, IM9 needs to undergo two transition states of TS9 and TS10 to form the intermediate IM11, and there is only one imaginary frequency of −408.58 cm−1 and −1189.48 cm−1, respectively, and the corresponding energy barriers are 43.89 kcal/mol and 42.41 kcal/mol. Finally, the H4 atom attached to N1 only needs to pass through TS11 with an imaginary frequency of −2348.45 cm−1 and cross the energy barrier of 11.23 kcal/mol to generate the product ethane. In TS11, the bond length between H4 and C2 is abruptly reduced from 2.337 Å to 1.116 Å. After IRC calculation, it can be confirmed that there are corresponding reactants and intermediates in the transition states of the reaction. It can be concluded from 2 (B) catalysts. It can be seen from 2 (B) is not satisfactory.2 (C) acetylene. First, the reaction pathway starts with co-adsorption (R) and co-adsorption energy of −6.22 kcal/mol. The intermediate IM1 needs to cross over TS1 with an imaginary frequency of −1678.32 cm−1 to obtain the intermediate. The reaction energy barrier of this process is 57.16 kcal/mol. This step is a rate-controlled step for the hydrogenation of acetylene to ethylene. Differently from other catalysts, graphene-NiN2 (C) can form ethylene in only one step. In IM1, H1 forms a bond with C1, and H2 forms a bond with C2. The bond lengths are 1.087 Å and 1.086 Å, respectively. IM1 was further hydrogenated to form the most stable and optimized co-adsorption structure IM2, with co-adsorption energy of −51.89 kcal/mol. Secondly, IM2 overcomes the energy barrier of 59.30 kcal/mol and passes through TS2 to obtain the intermediate IM3, which is a rate-controlling step for the hydrogenation of ethylene to ethane. the H3 and H4 atoms continue to vibrate through the N1 atom to C1 in TS2, with an imaginary frequency of −1734.45 cm−1. Thirdly, IM3 undergoes a transition state TS3 with an imaginary frequency of −1598.17 cm−1 to form the intermediate IM4 with a reaction energy barrier of 15.66 kcal/mol. In TS3, the vibrational direction of the H4 atom is from N1 to N2 atom. Finally, IM4 undergoes transition state TS4 with an imaginary frequency of −2386.03 cm−1 to form the product ethane with a reaction energy barrier of 56.58 kcal/mol. IRC can confirm that the corresponding reactants and intermediates exist in the reaction’s transition states. 2 atoms are involved in the site of the graphene-NiN2(C) catalytic reaction. 2 (C) has unsatisfactory selectivity.3 acetylene continuous hydrogenation and the corresponding energy barrier of each step is as shown in 2H2 and H2 molecules are sterically parallel to the catalyst, and the co-adsorption energy is −54.83 kcal/mol. The transition state TS1 with an imaginary frequency of −1710.68 cm−1 is required from the co-adsorption to the intermediate IM1. In TS1, the linear C2H2 molecule is broken and changed. The H1 and H2 atoms move to the C2H2 molecule, and the H1 atom undergoes stretching vibration toward the C1 atom to form the intermediate IM1. In IM1, the reaction energy barrier of the process is 39.85 kcal/mol, which is the rate-controlling step for the hydrogenation of acetylene to ethylene, and the bond length of H1 and C1 atoms is 1.094 Å. Secondly, the H2 atom connected to the C3 atom needs to cross the two transition states of TS2 (imaginary frequency of −127.27 cm−1) and TS3 (−588.63 cm−1) to form the intermediate IM3 to form ethylene. The energy barriers required for these two processes are 0.60 kcal/mol and 3.80 kcal/mol, respectively. Further hydrogenation of acetylene starts from IM4 with a total adsorption energy of −100.52 kcal/mol. In IM4, ethylene and hydrogen molecules are directly above the graphene-NiN3 catalyst. Next, to form the intermediate IM5, the reaction undergoes a transition state TS4 with an imaginary frequency of −2173.29 cm−1, which is a rate-controlled step for the hydrogenation of ethylene to ethane with a reaction energy barrier of 39.04 kcal/mol. In TS4, the H3 and H4 atoms migrate to the vicinity of the C3 atom, and the H3 atom stretches toward the C1 atom. The reaction’s last step requires the transition state TS5 with an imaginary frequency of −263.51 cm−1 to form the product ethane (P). From IM5 to P, the reaction energy barrier for this step is 1.09 kcal/mol. IRC again confirmed the structures’ correctness before and after all transition states in the reaction path. It can be concluded from 3 catalysts. As can be seen from 3 is poor.The specific reaction path of graphene-NiN4 catalysts at each step in the catalytic acetylene hydrogenation process. First, the reaction pathway also starts from co-adsorption (R) with co-adsorption energy of −12.56 kcal/mol. The reactants are free C2H2 and H2 molecules, and their optimal adsorption sites are above the catalyst. The energy of at least 25.24 kcal/mol is required in the process of Co to IM1, which is a rate-controlling step for the hydrogenation of acetylene to ethylene. In the vibrational analysis of TS1, the H2 molecule moved above the Ni atom and below the acetylene molecule, and the H1 atom vibrated to the C1 atom, causing the shape of the C2H2 molecule to change, and only one imaginary frequency value was −1798.29 cm−1. Secondly, the H2 atom connected to the metal Ni atom needs to undergo TS2 (imaginary frequency of −1026.95 cm−1) to form the intermediate IM2 and must overcome the energy of 20.56 kcal/mol. In TS2, the H2 atom attacks the N1 atom, and the vinyl group undergoes a certain inversion. By the time of the intermediate IM2, the N1 atom forms a bond with the H2 atom with a bond length of 1.035 Å. Then, the H2 atom vibrates toward the C2 atom, the bond length between the H2 and C2 atoms is shortened sharply, and there is only an imaginary frequency with a value of −100.93 cm−1 that only needs to cross the energy barrier of 1.25 kcal/mol to form ethylene. Further hydrogenation starts from the co-adsorption structure IM4 with a co-adsorption energy of −59.90 kcal/mol. Subsequently, the H3 and H4 atoms were activated, the bond length was once expanded from 0.742 Å to 1.407 Å, and the intermediate IM5 was formed by the transition state TS4 with an imaginary frequency of −1798.29 cm−1. This process is a rate-controlling step for the hydrogenation of ethylene to ethane, and the reaction energy barrier is 51.59 kcal/mol. The last step is the process from IM5 to the product ethane (P), which requires at least 1.43 kcal/mol of energy and crosses the transition state TS5. The H4 atom connected to the N1 atom vibrates toward the C2 atom in the TS5 vibrational analysis, resulting in an associated imaginary frequency (−405.40 cm−1). The correct front and rear structures are connected to the transition states in the reaction after IRC calculations. In 4 catalysts. 4 has good reactivity and selectivity, and can be considered a potential catalyst for acetylene hydrogenation.1, graphene-NiN2 (A), graphene-NiN2 (B), graphene-NiN2 (C), graphene-NiN3, and graphene-NiN4 catalysts are detailed. To compare the catalytic performance of graphene-NiNx  in acetylene hydrogen, the reaction energy barrier of six catalysts are compared, as shown in x  catalysts to catalyze the hydrogenation of acetylene to ethylene is as follows: graphene-NiN2 (B) > graphene-NiN2 (C) > graphene-NiN3 > graphene-NiN2 (A) > graphene-NiN1 > graphene-NiN4. The order of selectivity in 4 > graphene-NiN2 (A) > graphene-NiN2 (C) > graphene-NiN3 > graphene-NiN2 (B) > graphene-NiN1. The catalytic performance of catalysts depends on both reaction activity and selectivity. The lower the activity of the catalyst and the higher the selectivity, the better the catalytic performance. It is clearly seen that graphene-NiN4 has excellent catalytic properties, followed by graphene-NiN2 (A).In the above, the reaction path and energy of the catalyzed by graphene-NiNThus, we also speculate that the catalyst doped with even N atoms makes the catalyst structure distribution more uniform, enhances the overall symmetry, and is more conducive to the hydrogenation of acetylene, whereas the catalyst doped with odd-numbered N atoms is the opposite, which is unfavorable for the acetylene hydrogenation reaction.x  catalyst doped with a various number of N atoms was studied using the theoretical approach of DFT. The results reveal that the order of energy barrier for ethylene formation from acetylene hydrogenation catalyzed using graphene-NiNx  catalyst is graphene-NiN2 (B) > graphene-NiN2 (C) > graphene-NiN3 > graphene-NiN2 (A) > graphene-NiN1 > graphene-NiN4. Among the six catalysts, graphene-NiN4 has the highest selectivity, followed by graphene-NiN2 (A), and the rest are less selective. Having low activity and high selectivity is evidence of the good catalytic performance of this catalyst. Thus, graphene-NiN4 exhibits excellent catalytic performance and is also a potential non-noble metal catalyst for acetylene hydrogenation.In this study, the reaction mechanism of acetylene hydrogenation catalyzed by graphene-NiN


Thanks to the high thermal conductivity of graphene, a record-high operation speed on the order of 200 kHz is realized. Power consumption for π phase shift is 4.65 mW, approximately half of that of the state-of-the-art OPAs. By introducing air trenches, thermal crosstalk is significantly reduced, resulting in an enlarged fill factor. In addition, a novel beam steering scheme in the θ direction is proposed. By applying a 30 mW heating power, a temperature gradient along antennas is generated and beam steering of 2.3° is achieved, satisfying applications such as long-range collision avoidance for autonomous driving.In this paper, high-performance optical phased arrays (OPAs) assisted by transparent graphene nanoheaters and air trenches have been designed and simulated. By directly locating graphene nanoheaters on silicon waveguides, heating efficiency is enhanced by 62.96% compared to conventional structures with 1 μm SiO Improved performances of optical phased arrays have been achieved assisted by graphene nanoheaters and air trenches. Recently, T. Cao et al. theoretically simulated arrays of gradient Au/Bi2Se3/Au nanostructures and other metamaterials and achieved continuous beam steering of 18°.2–6 However, these exciting results require further experimental research before industrial applications. Optical phased arrays (OPAs) based on arrayed waveguides fabricated via CMOS  technology have been considered as one of the most promising technologies to realize solid-state LIDAR system due to their acceptable beam steering angle and potentially reduced cost.Optical beam shaping and steering is indispensable in many applications such as LIDAR (light detection and ranging) systems for three-dimensional imaging and mapping and free-space optical communication.7–16 Beam steering in ϕ direction  is accomplished either by wavelength tuning using the dispersive nature of grating or by fabricating two-dimensional (2D) OPAs.OPAs have been intensively studied in numerous publications.T is temperature difference,  is thermo-optic coefficient of Si, L is heating length and λ is wavelength in vacuum.7Generally, Si-based OPAs are thermo-optically tuned and the phase shift is determined by12,13,16 However, due to strong optical absorption, metal heaters must be separated from waveguides by thick (∼1 μm) overlay such as SiO2, making metal heaters power-inefficient. In addition, poor heat conductivity of SiO2 hinders heat transfer, prolonging heating and cooling time. Recent breakthrough in silicon OPAs utilizes an adiabatic bend to insert p–n–junction heaters into Si waveguides.7,8,10,15 However this approach not only requires careful design, cutting-edge fabrication technique but also may result in optical loss due to Si doping and the adiabatic bend.Metal heaters are widely used in silicon thermo-optic phase shifter.1 Moreover, wavelength tuned OPAs are generally fed by edge coupling,7,9,12,16 resulting in large coupling loss (>3 dB), due to the relatively small dimensions of Si waveguides compared to optical fibers. Furthermore, although 2D OPAs can achieve 2D beam steering using single wavelength, the major disadvantages are the dramatically increased on-chip power consumption, design and fabrication complexity, and more sophisticated driving circuits.1,17As mentioned above, 2D beam steering requires either wavelength tunable laser diode or 2D OPAs. First, it is challenging to design and fabricate chip-integrated semiconductor laser diode with high power, narrow linewidth and wide tunable range. Wavelength-tunable lasers based on silicon platform have only been realized as proof-of-concept demonstrations.θ direction without wavelength tunning.To conclude, it is imperative to realize thermo-optic OPAs with high power-efficiency, fast operation speed and ease of design and fabrication, and a novel low-power beam steering scheme in the 18–20 Experimental results show that graphene has a uniformly high transmission of 97.7% for wide optical spectrum, from 300 nm to 2500 nm.21 The transparent nature of graphene allows us directly locating graphene on Si waveguide without any materials in between. Thus heat generated from graphene could transfer directly to Si waveguide, making graphene nanoheaters more power-efficient.22 In addition, the thermal conductivity of graphene is measured to be 5300 W m−1 K−1,23–25 over 103 times higher than that of silica and air, which are 1.38 W m−1 K−1 and 0.023 W m−1 K−1, respectively. Such high thermal conductivity facilitates heat conduction in the heating and cooling process, making graphene nanoheaters friendlier for high-speed operating situation. Moreover, graphene is compatible with CMOS fabrication process,26 making graphene nanoheaters readier for large-scale photonic integration circuits (PICs).Graphene, a layer of carbon atoms arranged in hexagonal lattice, has received tremendous research interest since its first realization.θ direction is demonstrated.In this paper, we demonstrate improved performance of OPAs assisted by graphene nanoheaters. We show that heating efficiency of the graphene contact heating structure is improved by 62.96% compared to conventional structure with upper cladding. Key parameters including thermal crosstalk, operation speed and power consumption are simulated and the proposed structure is optimized accordingly. In addition, a novel beam steering scheme in the 27–29 Graphene nanoheaters are patterned by standard optical lithography followed by oxygen plasma etching. The width of graphene nanoheater is (2d + w + 440) nm. Throughout this paper, area of the patterned graphene nanoheaters is 100 μm2 and heating power applied to graphene nanoheater is 1 mW, unless otherwise mentioned.Schematic of the proposed structure is shown in 21 When evanescently coupled with Si waveguide, the absorption coefficient of graphene could be further enhanced.30 Fortunately, thanks to the relatively low density state of graphene, optical absorption could be eliminated by appropriate doping of graphene. According to the Pauli blocking effect, if the doping level of graphene is high enough that the Fermi level of graphene exceeds  regime, where hν is energy of incident photons, graphene becomes totally transparent.31–34 Moreover, numerous publications have discussed methods of uniform doping of graphene, including gate doping,35 ion doping36 and chemical doping.37,38 Therefore, the imaginary part of the refractive index of graphene is assumed zero. In addition, since the thickness of single-layer graphene is 0.34 nm, far smaller than the height of Si waveguide, we could reasonably believe that graphene has negligible influence on optical field distribution. Thus in the optical field simulation, we simplified the model by neglecting the presence of graphene.Though only single-layer atom thick, graphene has uniformly strong light absorption for near infrared light (∼2.3%).7,10 In order to obtain large field of view (FOV), the envelope should be as wide as possible. Since the width of the envelope is reversely proportional to the width of emitter, narrow waveguides are preferable.9 Thanks to the large refractive index difference between Si and BOX (∼2), the propagation loss of optical field is negligible for waveguide widths greater than 500 nm. Therefore, we choose w = 500 nm for simulation, enabling single-mode condition, low propagation loss and wide envelop.The far-field emission pattern of an OPA equals to the far-field pattern of one emitter, or the “envelope”, multiplied by an array factor.Neglecting optical absorption of Si, BOX and air, the real parts of refractive indexes at 1550 nm are set at 3.48, 1.45 and 1.00, respectively. The simulated optical field distribution for transverse-electric (TE) mode is shown in 2 overlay, as shown in In order to clearly demonstrate the merit of graphene nanoheaters which allows for direct contact heating Si waveguide, we first consider the structure without air trenches, which is schematically shown in TWGcore) indicates a higher heating efficiency.25 Simulations of thermal field are carried out by a commercial software COMSOL which is based on Finite Element Method. The simulated thermal field distribution of the proposed structure with graphene nanoheaters and the conventional structure are simulated, as shown in d = 500) for both simulations. Heat sources are defined by boundary heat sources. The thermal conductivity for air, Si, SiO2 and graphene are 0.023 W K−1 m−1, 130 W K−1 m−1, 1.38 W K−1 m−1 and 5300 W K−1 m−1, respectively, and the ambient temperature is 293.15 K.The thermal field is associated with the heating efficiency of graphene nanoheater. Given a fixed heating power (1 mW), a higher waveguide core temperature  since the heating length is reduced for a fixed area of graphene (100 μm2 in our simulation).Relationship between 25,39,40 harnessing the poor thermal conductivity of air, further improving the heating efficiency, trading off response speed to some extent though. The structure is schematically shown in TWGcore is drastically enhanced up to 311.17 K, indicating that the heating efficiency is further improved by a factor of 200% compared to the structure without air trench. TWGcore as function of d is shown in TWGcore decreases with increasing width of graphene nanoheater, showing an obviously opposite trend compared to Recent progress introduces air trenches to thermo-optic phase shifters,TWGcore. In the structure with air trench, the above-mentioned process is still valid. However, increasing width of graphene nanoheater enables increasing width of heat dissipation path, leading to a decreasing TWGcore, countering the effect of additional heating power. As a result, TWGcore is affected by the more dominating factor, which is the width of heat dissipation path concluded from our simulation.In the structure without air trench, as the width of graphene nanoheater increases, more heat power is generated and then conducts horizontally through graphene and finally contributes to the waveguide heating, resulting in increasing Pπ) is another important parameter for an OPA's phase shifter, since it is related to the total on-chip power consumption which scales with number of emitters.1 Using d, as shown in N equals to reciprocal of phase shift . The 10–90% rising time and decaying time for graphene nanoheaters without air trench are 2.57 μs and 2.59 μs. As qualitatively analysed above, the rising time and decaying time for graphene nanoheaters with air trench are relatively longer, which are 3.93 μs and 3.96 μs, respectively, due to the poor thermal conductivity of air. The response time as function of d in both structures are shown in d, as opposed to that in the structure with air trench. Explanation to this opposite trend is due to the opposite trend of TWGcore as function of d. Qualitatively speaking, it requires longer time to reach thermal equilibrium for higher TWGcore. The shortest response time is on order of 5.16 μs, corresponding to an operation speed on order of 190 kHz, which is, to our best knowledge, the fastest thermally tuned OPA. Please note this operation speed is limited by 10–90% response time, unlike The transient response is also simulated, as shown in 11,16 Therefore low thermal crosstalk is another design criterion. Thermal crosstalk among emitters is simulated by applying 1 mW heating power to all graphene nanoheaters except for the central one,16 as shown in d = 500 nm) and distances between adjacent waveguides are 4 μm (dWG = 4 μm).Thermal crosstalk is detrimental for OPAs, especially for the large-scale integrated ones, where each emitter requires an independent thermo-optic phase shifter in order to balance phase difference resulted from fabrication imperfection.2, heating up the waveguide underneath, as well as horizontally to adjacent waveguides in the heat deliver process, leading to severe thermal crosstalk. However, in the graphene contact-heating structure, heating flux generated by graphene flows directly to the Si waveguide. Furthermore, the presence of air trenches introduces heat isolation among channels that significantly diminishes thermal crosstalk. Dependence of the temperature at central waveguide core  on waveguide pitch (dWG) with and without air trench is shown in dWG is smaller than 4 μm, the central waveguide suffers from severe thermal crosstalk in both structures, although thermal crosstalk in the structure with air trench is approximately half of that in the structure without air trench. However, when dWG is larger than 10 μm, thermal crosstalk is negligible. It is worth mentioning that the waveguide pitch in the conventional structure is generally on order of 100 μm to reduce thermal crosstalks.16 Using graphene nanoheaters, the waveguide pitch can be reduced by one order of magnitude, giving rise to significantly reduced size of OPA chip, footprint of nonradiative phase-shifting area and consequently enlarged fill factor.As shown in ϕ direction requires smaller antenna pitch and is only an issue of nano-processing technology. However, beam steering in the θ direction is much more challenging. Generally speaking, in an ideal OPA, beam steering range in θ direction should be as wide as possible. However, for some realistic applications, such as collision avoidance for autonomous driving at 100 m distance or longer, beam steering as wide as 2 degree is practically enough since the angle subtended from a pedestrian or a vehicle is merely on order of 1 degree.Achieving wide steering range in the y-axis at the beginning of the antenna array (x-axis, resulting in beam steering in the θ direction.In a grating emitter, each grating tooth can be regarded as an antenna. Therefore, by operating a graphene nanoheater parallel to the na array , a tempeThe grating pitch and duty cycle modeled in this paper are 600 nm and 50%, respectively. Using 2D symmetry, the polar plot of the upward-radiated far-field pattern is shown in 2 overlay, and is further enhanced by a factor of 200% by the presence of air trench. In addition, the operation speed is on order of 200 kHz, twice the previously reported fastest OPA. Moreover, power consumption for π phase shift is as low as 4.65 mW, approximately half of the state-of-the-art OPAs. Furthermore, thanks to the poor thermal conductivity of air, thermal crosstalk is reduced by factor of 448% compared to the conventional structure, leading to footprint of nonradiative area reduced by one order of magnitude and consequently significantly enlarged fill factor. Finally, by applying 30 mW heating power, beam steering of 2.3° is achieved, satisfying applications such as collision avoidance for autonomous driving.In this paper, improved performances of OPAs assisted by transparent graphene nanoheaters have been demonstrated. First, assisted by transparent graphene nanoheaters, the heating efficiency is enhanced by 62.96% compared to the conventional structure with 1 μm SiOThere are no conflicts to declare.