Surface engineering of ZnO electrocatalyst by N doping towards electrochemical CO_(2) reduction

The discovery of efficient,selective,and stable electrocatalysts can be a key point to produce the largescale chemical fuels via electrochemical CO_(2) reduction(ECR).In this study,an earth-abundant and nontoxic ZnO-based electrocatalyst was developed for use in gas-diffusion electrodes(GDE),and the effect of nitrogen(N)doping on the ECR activity of ZnO electrocatalysts was investigated.Initially,a ZnO nanosheet was prepared via the hydrothermal method,and nitridation was performed at different times to control the N-doping content.With an increase in the N-doping content,the morphological properties of the nanosheet changed significantly,namely,the 2D nanosheets transformed into irregularly shaped nanoparticles.Furthermore,the ECR performance of Zn O electrocatalysts with different N-doping content was assessed in 1.0 M KHCO_(3) electrolyte using a gas-diffusion electrode-based ECR cell.While the ECR activity increased after a small amount of N doping,it decreased for higher N doping content.Among them,the N:ZnO-1 h electrocatalysts showed the best CO selectivity,with a faradaic efficiency(FE_(CO))of 92.7%at-0.73 V vs.reversible hydrogen electrode(RHE),which was greater than that of an undoped Zn O electrocatalyst(FE_(CO)of 63.4%at-0.78 V_(RHE)).Also,the N:ZnO-1 h electrocatalyst exhibited outstanding durability for 16 h,with a partial current density of-92.1 mA cm^(-2).This improvement of N:ZnO-1 h electrocatalyst can be explained by density functional theory calculations,demonstrating that this improvement of N:ZnO-1 h electrocatalyst comes from(ⅰ)the optimized active sites lowering the free energy barrier for the rate-determining step(RDS),and(ⅱ)the modification of electronic structure enhancing the electron transfer rate by N doping.

Extracellular vesicles for neural regeneration after spinal cord injury

What is spinal cord injury:Spinal cord injury(SCI)is the damage to the structure of the bundles of cells and nerves that communicate signals from the brain to the body and extremities.The pathology of SCI includes both primary and secondary injuries(Morales et al.,2016).Physical forces such as compression,shearing,contusion,and tearing are major causes of primary injury in SCI.There are two main processes in primary injury:acute and subacute.The acute phase includes traumatic disruption of axons and hemorrhage of the blood vessels around the spinal cord.Hemorrhagic injury to the vessels can lead to increased edema within the neural and cord tissues,susceptibility to infiltration by microglia and astrocytes,excitotoxicity,and demyelination.Similarly,disruption of the blood-spinal cord barrier results in the release of inflammatory cytokines from specific cells and vessels.

Tuning the reactivity of TiO_(2)layer with uniform distribution of Sub-5 nm Fe_(2)O_(3)particles via in situ voltage-assisted oxidation for robust catalytic reduction

The trade-off between efficiency and stability has limited the application of TiO_(2)as a catalyst due to its poor surface reactivity.Here,we present a modification of a TiO_(2)layer with highly stable Sub-5 nm Fe_(2)O_(3)nanoparticles(NP)by modulating its structure-surface reactivity relationship to attain efficiency-stability balance via a voltage-assisted oxidation approach.In situ simultaneous oxidation of the Ti substrate and Fe precursor using high-energy plasma driven by high voltage resulted in uniform distribution of Fe_(2)O_(3)NP embedded within porous TiO_(2)layer.Comprehensive surface characterizations with density functional theory demonstrated an improved electronic transition in TiO_(2)due to the presence of surface defects from reactive oxygen species and possible charge transfer from Ti to Fe;it also unexpectedly increased the active site in the TiO_(2)layer due to uncoordinated electrons in Sub-5 nm Fe_(2)O_(3)NP/TiO_(2)catalyst,thereby enhancing the adsorption of chemical functional groups on the catalyst.This unique embedded structure exhibited remarkable improvement in reducing 4-nitrophenol to 4-aminophenol,achieving approximately 99%efficiency in 20 min without stability decay after 20 consecutive cycles,outperforming previously reported TiO_(2)-based catalysts.This finding proposes a modified-electrochemical strategy enabling facile construction of TiO_(2)with nanoscale oxides extandable to other metal oxide systems.

Primary Ewing sarcoma of the kidney mimicking cystic papillary renal cell carcinoma in an older patient:A case repor

BACKGROUND Ewing’s sarcoma(ES)is a neuroectodermal tumor that typically occurs in the bones and soft tissues of children and young adults.Primary renal ES is rare;only a few cases and a small case series have been documented,and only four cases involved primary renal ES in older people(>65 years old).CASE SUMMARY Herein,we describe the radiological and pathological features of primary renal ES in an older person.A 76-year-old man complained of poor oral intake and was found to have a large cystic renal mass with indistinct margins on computed tomography.Ultrasound-guided biopsy revealed that the tumor contained small round blue cells.The patient underwent a right radical nephrectomy.The tumor cells showed diffuse membranous CD99,and nuclear friend leukemia integration 1 transcription factor and NK2 Homeobox 2.Fluorescence in situ hybridization revealed EWSR1 translocation.Postoperatively,18F-fluorodeoxyglucose positron emission tomography revealed no evidence of metastasis.The patient was diagnosed with primary renal ES.Six months following the surgery,local recurrence and distant metastasis were observed.Primary renal ES is rare and often lethal in older individuals.The specific imaging findings are unknown,and treatment protocols have not been standardized.CONCLUSION This case report describes the radiological and pathological features of primary renal ES in an older person.

Self-adhesive pads in defi brillation or cardioversion: risks due to unfamiliarity and recommendations

During cardiopulmonary resuscitation(CPR),defibrillation,or cardioversion,the use of self-adhesive pads instead of conventional paddles has gradually become common.In particular,it was recommended during the COVID-19 pandemic,as it allows defibrillation or cardioversion to be performed without close contact between the rescuer and patient.[1,2]Self-adhesive pads can minimize the downtime of chest compressions before and after defibrillation and protect the patient from electric arc formation,skin burns,and fi re.Once the pads are attached,electrocardiographic rhythm analysis,defibrillation,synchronous cardioversion,and cardiovascular support are all possible.Unlike paddles,self-adhesive pads can save time because the application of conductive jelly,repeated positioning,and application of 10-12 kg of pressure on the chest are not needed.The European Resuscitation Council(ERC)Guidelines 2021 state that the use of self-adhesive pads is recommended in advanced cardiovascular life support(ACLS).[3]Nevertheless,some medical staff still tend to prefer the use of conventional paddles instead of self-adhesive pads in hospitals.

Review on inorganic and polymeric materials-coordinated metal-organic-framework photocatalysts for green hydrogen evolution

Metal-organic frameworks(MOFs)possess a distinct advantage over conventional heterogeneous photocatalysts because of their carefully defined architecture and particular pores,which facilitate the targeted incorporation of other efficient cocatalysts or semiconductor materials.The integration of MOFs with other materials has resulted in significant breakthroughs,as the coupled materials improve the performance due to the combined effect.The unique MOF structures allow them to host foreign materials,which results in harvesting the visible region of the solar spectrum and effectively mitigating charge recombination by promoting charge separation.The review presents an evaluation of the latest developments in the utilization of surface and/or pore chemistry of MOF-supported heterojunctions for photocatalytic green-hydrogen generation with a basic understanding of the mechanism involved.The review begins with the basic principles of photocatalysis,the significance of MOFs,their optical properties,the methods used for synthesizing MOFs,and their coordination with other inorganic and polymeric materials.Furthermore,methods to increase photocatalytic H2 evolution using MOF-supported heterojunction have been proposed as standard practice.Lastly,to address environmental challenges,we highlight the future potential of MOF-supported heterojunctions for use in green-energy production.We hope that this review provides guidance to researchers in the development of effective heterojunctions based on MOFs to address challenges in energy applications and catalytic processes.

Chronic venous insufficiency, could it be one of the missing pieces in the puzzle of treating pain?

Pain is a common complaint among patients seeking medical care.If left un-treated,pain can become chronic,significantly affecting patients’quality of life.An accurate diagnosis of the underlying cause of pain is crucial for effective treatment.Chronic venous insufficiency(CVI)is frequently overlooked by pain physicians.Moreover,many pain physicians lack sufficient knowledge about CVI.CVI is a common condition resulting from malfunctioning or damaged valves in lower limb veins.Symptoms of CVI,ranging from mild to severe,include pain,heaviness,fatigue,itching,swelling,skin color changes,and ulcers in the lower limbs.Recently,it has become more widely known that these symptoms can be attributed to CVI.Even slight or mild CVI can cause related symptoms.Pain physicians primarily consider neuromusculoskeletal disorders when assessing patients with leg pain,and often neglect the possibility of CVI.In clinical practice,when pain physicians encounter patients with unresolved leg pain,they must assess whether the patients exhibit symptoms of CVI and conduct tests to differ-entiate CVI from other potential causes.

Use of artificial intelligence in the field of pain medicine

In this editorial we comment on the article“Potential and limitations of ChatGPT and generative artificial intelligence in medial safety education”published in the recent issue of the World Journal of Clinical Cases.This article described the usefulness of artificial intelligence(AI)in medial safety education.Herein,we focus specifically on the use of AI in the field of pain medicine.AI technology has emerged as a powerful tool,and is expected to play an important role in the healthcare sector and significantly contribute to pain medicine as further developments are made.AI may have several applications in pain medicine.First,AI can assist in selecting testing methods to identify causes of pain and improve diagnostic accuracy.Entry of a patient’s symptoms into the algorithm can prompt it to suggest necessary tests and possible diagnoses.Based on the latest medical information and recent research results,AI can support doctors in making accurate diagnoses and setting up an effective treatment plan.Second,AI assists in interpreting medical images.For neural and musculoskeletal disorders,imaging tests are of vital importance.AI can analyze a variety of imaging data,including that from radiography,computed tomography,and magnetic resonance imaging,to identify specific patterns,allowing quick and accurate image interpretation.Third,AI can predict the outcomes of pain treatments,contributing to setting up the optimal treatment plan.By predicting individual patient responses to treatment,AI algorithms can assist doctors in establishing a treatment plan tailored to each patient,further enhancing treatment effectiveness.For efficient utilization of AI in the pain medicine field,it is crucial to enhance the accuracy of AI decision-making by using more medical data,while issues related to the protection of patient personal information and responsibility for AI decisions will have to be addressed.In the future,AI technology is expected to be innovatively applied in the field of pain medicine.The advancement of AI is anticipated to have a positive impact on the entire medical field by providing patients with accurate and effective medical services.

Management of lymphedema is really a matter in patients with breast cancer

Lymphedema is a prevalent complication affecting patients with breast cancer,greatly impacting their quality of life.This editorial describes diagnostic methods and therapeutic interventions for managing lymphedema in patients with breast cancer.Diagnosis relies on clinical evaluation and objective measures,including arm circumference and volumetric assessments,along with lymphoscintigraphy and ultrasonic measurements.Treatment primarily involves complex decongestive physical therapy,comprising manual lymphatic drainage,compression therapy,exercise,and meticulous skin care.These interventions aim to reduce swelling,alleviate discomfort,and prevent further complications.Additionally,lifestyle modifications such as avoiding extreme temperatures and maintaining proper hygiene are essential.Flavonoids can be used for drug therapy.Despite its prevalence,lymphedema often receives inadequate attention in clinical practice,emphasizing the importance of raising awareness and enhancing medical services for affected individuals.Clinicians play a pivotal role in educating patients about preventive measures and ensuring timely intervention.Overall,a comprehensive approach encompassing early diagnosis,multidisciplinary management,and patient education is essential to mitigate the burden of lymphedema in patients with breast cancer and improve their overall well-being.

In-situ construction of grass-like hybrid architecture responsible for extraordinary corrosion performance: Experimental and theoretical approach

Despite the engineering potential by the co-existence of inorganic and organic substances to protect vulnerable metallic materials from corrosive environments,both their interaction and in-situ formation mechanism to induce the nature-inspired composite remained less understood.The present work used three distinctive mercaptobenzazole(MB)compounds working as corrosion inhibitors,such as 2-mercaptobenzoxazole(MBO),2-mercaptobenzothiazole(MBT),and 2-mercaptobenzimidazole(MBI)in a bid to understand how the geometrical structure arising from O,S,and N atoms affected the interaction toward inorganic layer.MB compounds that were used here to control the corrosion kinetics would be interacted readily with the pre-existing MgO layer fabricated by plasma electrolysis.This phenomenon triggered the nucleation of the root network since MB compounds were seen to be adsorbed actively on the defective surface through the active sites in MB compound.Then,the molecule with twin donor atoms adjacent to the mercapto-sites affected the facile growth of the grass-like structures with‘uniform’distribution via molecular self-assembly,which showed better corrosion performance than those with having dissimilar donor atoms with the inhibition efficiency(η)of 97%approximately.The formation mechanism underlying nucleation and growth behavior of MB molecule was discussed concerning the theoretical calculation of density functional theory.

Protocol for lower back pain management: Insights from the French healthcare system

In this editorial we comment on the article published in a recent issue of the World Journal of Clinical Cases.This article described a novel ultrasound-guided lateral recess block approach in treating a patient with lateral recess stenosis.The impact of spinal pain-related disability extends significantly,causing substantial human suffering and medical costs.Each county has its preferred treatment strategies for spinal pain.Here,we explore the lower back pain(LBP)treatment algorithm recommended in France.The treatment algorithm for LBP recommended by the French National Authority for Health emphasizes early patient activity and minimal medication use.It encourages the continuation of daily activities,limits excessive medication and spinal injections,and incorporates psychological assessments and non-pharmacological therapies for chronic cases.However,the algorithm may not aggressively address acute pain in the early stages,potentially delaying relief and increasing the risk of chronicity.Additionally,the recommended infiltrations primarily involve caudal epidural steroid injections,with limited consideration for other injection procedures,such as transforaminal or interlaminar epidural steroid injections.The fixed follow-up timeline may not accommodate patients who do not respond to initial treatment or experience intense pain,potentially delaying the exploration of alternative therapies.Despite these limitations,understanding the strengths and weaknesses of the French approach could inform adaptations in LBP treatment strategies globally,potentially enhancing patient outcomes and satisfaction across diverse healthcare systems.

Differences between the healthcare systems of Quebec and France for the treatment of pain due to spinal disorders

In Quebec,Canada,the public healthcare system offers free medical services.However,patients with spinal pain often encounter long waiting times for specialist appointments and limited physiotherapy coverage.In contrast,private clinics provide expedited care but are relatively scarce and entail out-of-pocket expenses.Once a patient with pain caused by a spinal disorder meets a pain medicine specialist,spinal intervention is quickly performed when indicated,and patients are provided lifestyle advice.Transforaminal epidural steroid injections are frequently administered to patients with radicular pain,and steroid injections are administered on a facet joint to control low back or neck pain.Additionally,medial branch blocks are performed prior to thermocoagulation.France’s universal healthcare system ensures accessibility at controlled costs.It emphasizes physical activity and provides free physical therapy services.However,certain interventions,such as transforaminal and interlaminar epidural injections,are not routinely used in France owing to limited therapeutic efficacy and safety concerns.This underutilization may be a potential cause of chronic pain for many patients.By examining the differences,strengths,and weaknesses of these two systems,valuable insights can be gained for the enhancement of global spinal pain management strategies,ultimately leading to improved patient outcomes and satisfaction.

Improving Prediction of Chronic Kidney Disease Using KNN Imputed SMOTE Features and TrioNet Model

Chronic kidney disease(CKD)is a major health concern today,requiring early and accurate diagnosis.Machine learning has emerged as a powerful tool for disease detection,and medical professionals are increasingly using ML classifier algorithms to identify CKD early.This study explores the application of advanced machine learning techniques on a CKD dataset obtained from the University of California,UC Irvine Machine Learning repository.The research introduces TrioNet,an ensemble model combining extreme gradient boosting,random forest,and extra tree classifier,which excels in providing highly accurate predictions for CKD.Furthermore,K nearest neighbor(KNN)imputer is utilized to deal withmissing values while synthetic minority oversampling(SMOTE)is used for class-imbalance problems.To ascertain the efficacy of the proposed model,a comprehensive comparative analysis is conducted with various machine learning models.The proposed TrioNet using KNN imputer and SMOTE outperformed other models with 98.97%accuracy for detectingCKD.This in-depth analysis demonstrates the model’s capabilities and underscores its potential as a valuable tool in the diagnosis of CKD.

Depression Intensity Classification from Tweets Using Fast Text Based Weighted Soft Voting Ensemble

Predicting depression intensity from microblogs and social media posts has numerous benefits and applications,including predicting early psychological disorders and stress in individuals or the general public.A major challenge in predicting depression using social media posts is that the existing studies do not focus on predicting the intensity of depression in social media texts but rather only perform the binary classification of depression and moreover noisy data makes it difficult to predict the true depression in the social media text.This study intends to begin by collecting relevant Tweets and generating a corpus of 210000 public tweets using Twitter public application programming interfaces(APIs).A strategy is devised to filter out only depression-related tweets by creating a list of relevant hashtags to reduce noise in the corpus.Furthermore,an algorithm is developed to annotate the data into three depression classes:‘Mild,’‘Moderate,’and‘Severe,’based on International Classification of Diseases-10(ICD-10)depression diagnostic criteria.Different baseline classifiers are applied to the annotated dataset to get a preliminary idea of classification performance on the corpus.Further FastText-based model is applied and fine-tuned with different preprocessing techniques and hyperparameter tuning to produce the tuned model,which significantly increases the depression classification performance to an 84%F1 score and 90%accuracy compared to baselines.Finally,a FastText-based weighted soft voting ensemble(WSVE)is proposed to boost the model’s performance by combining several other classifiers and assigning weights to individual models according to their individual performances.The proposed WSVE outperformed all baselines as well as FastText alone,with an F1 of 89%,5%higher than FastText alone,and an accuracy of 93%,3%higher than FastText alone.The proposed model better captures the contextual features of the relatively small sample class and aids in the detection of early depression intensity prediction from tweets with impactful performances.

SMARCA4-deficient undifferentiated thoracic tumor:A case report

BACKGROUND SMARCA4-deficient undifferentiated tumors(SMARCA4-DUTs)present with diverse clinical manifestations and progress to metastasis and even cause death within a few months.This novel subset of undifferentiated tumors occurs in the middle-aged population and is strongly associated with a smoking history.Distin-guishing it from other malignancies is challenging.CASE SUMMARY A 62-year-old man presented with chest pain for 7 d.The patient had no respiratory symptoms and normal pulmonary function test results.The patient had been a smoker for 8 years and quit smoking 2 years ago.Chest computed tomography revealed a huge mass involving the left upper and lower lung lobes with pericardial invasion and multiple metastases.Tumor samples were obtained using open frozen biopsy,after several unsuccessful attempts.The tumor was composed of sheets of undifferentiated disclosive cells with vesicular nuclei and prominent nucleoli.The differential diagnosis included high-grade lymphoma,germ cell tumor,NUT carcinoma,undifferentiated carcinoma,and sarcoma.The tumor cells were large,arranged in sheets,and did not exhibit glandular or squamous differentiation.Frequent foci of necrosis were noted.There was no evidence of epithelial differentiation on immunohistochemical staining.The SMARCA4 stain showed complete loss of expression of SMARCA4,which is diagnostic.CONCLUSION In the present case,thoracic SMARCA4-DUT was diagnosed based on clinical features,absence of epithelial differentiation,and negative SMARCA4 expression.

Automated Brain Tumor Diagnosis Using Deep Residual U-Net Segmentation Model

Automated segmentation and classification of biomedical images act as a vital part of the diagnosis of brain tumors(BT).A primary tumor brain analysis suggests a quicker response from treatment that utilizes for improving patient survival rate.The location and classification of BTs from huge medicinal images database,obtained from routine medical tasks with manual processes are a higher cost together in effort and time.An automatic recognition,place,and classifier process was desired and useful.This study introduces anAutomatedDeepResidualU-Net Segmentation with Classification model(ADRU-SCM)for Brain Tumor Diagnosis.The presentedADRUSCM model majorly focuses on the segmentation and classification of BT.To accomplish this,the presented ADRU-SCM model involves wiener filtering(WF)based preprocessing to eradicate the noise that exists in it.In addition,the ADRU-SCM model follows deep residual U-Net segmentation model to determine the affected brain regions.Moreover,VGG-19 model is exploited as a feature extractor.Finally,tunicate swarm optimization(TSO)with gated recurrent unit(GRU)model is applied as a classification model and the TSO algorithm effectually tunes theGRUhyperparameters.The performance validation of the ADRU-SCM model was tested utilizing FigShare dataset and the outcomes pointed out the better performance of the ADRU-SCM approach on recent approaches.

Recent Advances in Multifunctional Reticular Framework Nanoparticles:A Paradigm Shift in Materials Science Road to a Structured Future

Porous organic frameworks(POFs)have become a highly sought-after research domain that offers a promising avenue for developing cutting-edge nanostructured materials,both in their pristine state and when subjected to various chemical and structural modifications.Metal–organic frameworks,covalent organic frameworks,and hydrogen-bonded organic frameworks are examples of these emerging materials that have gained significant attention due to their unique properties,such as high crystallinity,intrinsic porosity,unique structural regularity,diverse functionality,design flexibility,and outstanding stability.This review provides an overview of the state-of-the-art research on base-stable POFs,emphasizing the distinct pros and cons of reticular framework nanoparticles compared to other types of nanocluster materials.Thereafter,the review highlights the unique opportunity to produce multifunctional tailoring nanoparticles to meet specific application requirements.It is recommended that this potential for creating customized nanoparticles should be the driving force behind future synthesis efforts to tap the full potential of this multifaceted material category.

Machine Vision Based Fish Cutting Point Prediction for Target Weight

Food processing companies pursue the distribution of ingredientsthat were packaged according to a certain weight. Particularly, foods like fishare highly demanded and supplied. However, despite the high quantity offish to be supplied, most seafood processing companies have yet to installautomation equipment. Such absence of automation equipment for seafoodprocessing incurs a considerable cost regarding labor force, economy, andtime. Moreover, workers responsible for fish processing are exposed to risksbecause fish processing tasks require the use of dangerous tools, such aspower saws or knives. To solve these problems observed in the fish processingfield, this study proposed a fish cutting point prediction method based onAI machine vision and target weight. The proposed method performs threedimensional(3D) modeling of a fish’s form based on image processing techniquesand partitioned random sample consensus (RANSAC) and extracts 3Dfeature information. Then, it generates a neural network model for predictingfish cutting points according to the target weight by performing machinelearning of the extracted 3D feature information and measured weight information.This study allows for the direct cutting of fish based on cutting pointspredicted by the proposed method. Subsequently, we compared the measuredweight of the cut pieces with the target weight. The comparison result verifiedthat the proposed method showed a mean error rate of approximately 3%.

Intelligent Service Search Model Using Emerging Technologies

In recent years,the Internet of Things(IoT)has played a vital role in providing various services to users in a smart city.However,searching for services,objects,data,and frameworks remains a concern.The technological advancements in Cyber-Physical Systems(CPSs)and the Social Internet of Things(SIoT)open a new era of research.Thus,we propose a Cyber-Physical-Social Systems(CPSs)for service search.Herein,service search and object discovery operation carries with the suitable selection of friends in the network.Our proposed model constructs a graph and performs social network analysis(SNA).We suggest degree centrality,clustering,and scalefree emergence and show that a rational selection of friends per service exploration increases the overall network navigability.The efficiency of our proposed system is verified using real-world datasets based on service processing time,path length,giant component,and network diameter.The simulation results proved that our proposed system is efficient,robust,and scalable.

Advanced prediction of organic–metal interactions through DFT study and electrochemical displacement approach

Heterocyclic compounds are the promising biological compounds as nature-friendly for the corrosion protection of metallic surface.In this work,three heterocyclic compounds such as 1-azanaphthalene-8-ol(8-AN),2-methylquinoline-8-ol(8-MQ),and 8-quinolinol-5-sulfonic acid(8-QSA)were used as green compounds,and their anti-corrosion performance for AZ31 Mg in saline water was discussed on the basis of impedance interpretation and surface analysis.Findings found that the electrochemical performance was improved in the order of 8-AN>8-MQ>8-QSA,demonstrating the electron donor effect of N-heterocycles to form coordination complexes on the magnesium surface.From the electrochemical performance,the protective layer constructed at the optimal concentration reinforces the barrier against aggressive environments,with potential inhibition efficiency of 87.4%,99.0%,and 99.9%for 8-QSA,8-MQ,and 8-AN,respectively.Quantum chemical parameters and electron density distribution for free organic species in the absence and presence of Mg^(2+)cation were evaluated using density functional theory(DFT).Upon the formation of coordination complexes between organic compound and Mg^(2+),energy gap underwent change about ΔE=5.7 eV in the 8-AN/Mg^(2+)system.Furthermore,the adsorption of heterocyclic compounds on Mg surface reveals the formation of strong covalent bonds with Mg atoms,which further confirmed by the electron density difference and projected density of states analyses.Based on theoretical calculations,three inhibitors can adsorb on the metal surface in both parallel and perpendicular orientations via C,O and N atoms.In the parallel configuration,the C-Mg,N-Mg and O-Mg bond distances are between 2.11 and 2.25˚A,whereas the distances in the case of perpendicular adsorption are between 2.20 and 2.40˚A(covalent bonds via O and N atoms).The results indicated that parallel configurations are energetically more stable,in which the adsorption energies are-4.48 eV(8-AN),-4.28 eV(8-MQ)and-3.82 eV(8-QSA)compared to that of perpendicular adsorption(-3.65,-3.40,and-2.63 eV).As a result,experimental and theoretical studies were in well agreement and confirm that the nitrogen and oxygen atoms will be the main adsorption sites.