Tuning Two-Dimensional Phthalocyanine Dual Site Metal-Organic Framework Catalysts for the Oxygen Reduction Reaction.

Metal-organic frameworks (MOFs) offer an interesting opportunity for catalysis, particularly for metal-nitrogen-carbon (M-N-C) motifs by providing an organized porous structural pattern and well-defined active sites for the oxygen reduction reaction (ORR), a key need for hydrogen fuel cells and related sustainable energy technologies. In this work, we leverage electrochemical testing with computational models to study the electronic and structural properties in the MOF systems and their relationship to ORR activity and stability based on dual transitional metal centers. The MOFs consist of two M1 metals with amine nodes coordinated to a single M2 metal with a phthalocyanine linker, where M1/M2 = Co, Ni, or Cu. Co-based metal centers, in particular Ni-Co, demonstrate the highest overall activity of all nine tested MOFs. Computationally, we identify the dominance of Co sites, relative higher importance of the M2 site, and the role of layer M1 interactions on the ORR activity. Selectivity measurements indicate that M1 sites of MOFs, particularly Co, exhibit the lowest (<4%), and Ni demonstrates the highest (>46%) two-electron selectivity, in good agreement with computational studies. Direct in situ stability characterization, measuring dissolved metal ions, and calculations, using an alkaline stability metric, confirm that Co is the most stable metal in the MOF, while Cu exhibits notable instability at the M1. Overall, this study reveals how atomistic coupling of electronic and structural properties affects the ORR performance of dual site MOF catalysts and opens new avenues for the tunable design and future development of these systems for practical electrochemical applications.

Synergistic effects of mixing and strain in high entropy spinel oxides for oxygen evolution reaction.

Developing stable and efficient electrocatalysts is vital for boosting oxygen evolution reaction (OER) rates in sustainable hydrogen production. High-entropy oxides (HEOs) consist of five or more metal cations, providing opportunities to tune their catalytic properties toward high OER efficiency. This work combines theoretical and experimental studies to scrutinize the OER activity and stability for spinel-type HEOs. Density functional theory confirms that randomly mixed metal sites show thermodynamic stability, with intermediate adsorption energies displaying wider distributions due to mixing-induced equatorial strain in active metal-oxygen bonds. The rapid sol-flame method is employed to synthesize HEO, comprising five 3d-transition metal cations, which exhibits superior OER activity and durability under alkaline conditions, outperforming lower-entropy oxides, even with partial surface oxidations. The study highlights that the enhanced activity of HEO is primarily attributed to the mixing of multiple elements, leading to strain effects near the active site, as well as surface composition and coverage.

Retardation of oxidative rancidity in ghee adding orange peel powder at different storage temperature.

This study is aimed to determine and compare the antioxidant activity of Orange Peel Powder (OPP) in ghee at different temperatures (4 °C, 25 °C and 60 °C) for divergent storage periods (0, 7, 14 and 21 days). To compare the antioxidant potentiality, synthetic antioxidant BHA (Butylated Hydroxy Anisole) is used. Twelve ghee samples were prepared where one was control, another one was BHA treated and the rest ten were admixing OPP in ghee at different ratios. After sensory evaluation three highest scored ghee samples (0.5%. 1.0% and 1.5%) were selected. Samples were analyzed for peroxide (PV), thiobarbituric acid (TBA), free fatty acids (FFA) value and radical scavenging activity. Though storage temperature and storage period were increased OPP treated ghee samples peroxide, TBA and FFA values were lowered significantly compared to control samples. Moreover, 1.0% and 1.5% OPP treated ghee samples such values were lowered than BHA treated ghee samples and all these are on the favor of ghee quality. OPP treated ghee samples' DPPH quench potentiality is also stronger than BHA treated ghee samples. Therefore, OPP is a great source of antioxidants and this can be used in ghee as a natural source of antioxidants.

Health-related quality of life and coping strategies adopted by COVID-19 survivors: A nationwide cross-sectional study in Bangladesh.

INTRODUCTION: This study aims to investigate the health-related quality of life and coping strategies among COVID-19 survivors in Bangladesh. METHODS: This is a cross-sectional study of 2198 adult, COVID-19 survivors living in Bangladesh. Data were collected from previously diagnosed COVID-19 participants (confirmed by an RT-PCR test) via door-to-door interviews in the eight different divisions in Bangladesh. For data collection, Bengali-translated Brief COPE inventory and WHO Brief Quality of Life (WHO-QoLBREF) questionnaires were used. The data collection period was from October 2020 to March 2021. RESULTS: Males 72.38% (1591) were more affected by COVID-19 than females 27.62% (607). Age showed significant correlations (p<0.005) with physical, psychological and social relationships, whereas gender showed only a significant correlation with physical health (p<0.001). Marital status, occupation, living area, and co-morbidities showed significant co-relation with all four domains of QoL (p<0.001). Education and affected family members showed significant correlation with physical and social relationship (p<0.001). However, smoking habit showed a significant correlation with both social relationship and environment (p<0.001). Age and marital status showed a significant correlation with avoidant coping strategies (p<0.001); whereas gender and co-morbidities showed a significant correlation with problem-focused coping strategies (p<0.001). Educational qualification, occupation and living area showed significant correlation with all three coping strategies(p<0.001). CONCLUSION: Survivors of COVID-19 showed mixed types of coping strategies; however, the predominant coping strategy was avoidant coping, followed by problem-focused coping, with emotion-focused coping reported as the least prevalent. Marital status, occupation, living area and co-morbidities showed a greater effect on QoL in all participants. This study represents the real scenario of nationwide health-associated quality of life and coping strategies during and beyond the Delta pandemic.

Investigation of the Structure of Atomically Dispersed NiNx Sites in Ni and N-Doped Carbon Electrocatalysts by 61Ni Mössbauer Spectroscopy and Simulations.

Ni and nitrogen-doped carbons are selective catalysts for CO2 reduction to CO (CO2R), but the hypothesized NiNx active sites are challenging to probe with traditional characterization methods. Here, we synthesize 61Ni-enriched model catalysts, termed 61NiPACN, in order to apply 61Ni Mössbauer spectroscopy using synchrotron radiation (61Ni-SR-MS) to characterize the structure of these atomically dispersed NiNx sites. First, we demonstrate that the CO2R results and standard characterization techniques (SEM, PXRD, XPS, XANES, EXAFS) point to the existence of dispersed Ni active sites. Then, 61Ni-SR-MS reveal significant internal magnetic fields of ∼5.4 T, which is characteristic of paramagnetic, high-spin Ni2+, in the 61NiPACN samples. Finally, theoretical calculations for a variety of Ni-Nx moieties confirm that high-spin Ni2+ is stable in non-planar, tetrahedrally distorted geometries, which results in calculated isotropic hyperfine coupling that is consistent with 61Ni-SR-MS measurements.

Fuzzy-Assisted Mobile Edge Orchestrator and SARSA Learning for Flexible Offloading in Heterogeneous IoT Environment.

In the era of heterogeneous 5G networks, Internet of Things (IoT) devices have significantly altered our daily life by providing innovative applications and services. However, these devices process large amounts of data traffic and their application requires an extremely fast response time and a massive amount of computational resources, leading to a high failure rate for task offloading and considerable latency due to congestion. To improve the quality of services (QoS) and performance due to the dynamic flow of requests from devices, numerous task offloading strategies in the area of multi-access edge computing (MEC) have been proposed in previous studies. Nevertheless, the neighboring edge servers, where computational resources are in excess, have not been considered, leading to unbalanced loads among edge servers in the same network tier. Therefore, in this paper, we propose a collaboration algorithm between a fuzzy-logic-based mobile edge orchestrator (MEO) and state-action-reward-state-action (SARSA) reinforcement learning, which we call the Fu-SARSA algorithm. We aim to minimize the failure rate and service time of tasks and decide on the optimal resource allocation for offloading, such as a local edge server, cloud server, or the best neighboring edge server in the MEC network. Four typical application types, healthcare, AR, infotainment, and compute-intensive applications, were used for the simulation. The performance results demonstrate that our proposed Fu-SARSA framework outperformed other algorithms in terms of service time and the task failure rate, especially when the system was overloaded.

Dynamic Task Offloading for Cloud-Assisted Vehicular Edge Computing Networks: A Non-Cooperative Game Theoretic Approach.

Vehicular edge computing (VEC) is one of the prominent ideas to enhance the computation and storage capabilities of vehicular networks (VNs) through task offloading. In VEC, the resource-constrained vehicles offload their computing tasks to the local road-side units (RSUs) for rapid computation. However, due to the high mobility of vehicles and the overloaded problem, VEC experiences a great deal of challenges when determining a location for processing the offloaded task in real time. As a result, this degrades the quality of vehicular performance. Therefore, to deal with these above-mentioned challenges, an efficient dynamic task offloading approach based on a non-cooperative game (NGTO) is proposed in this study. In the NGTO approach, each vehicle can make its own strategy on whether a task is offloaded to a multi-access edge computing (MEC) server or a cloud server to maximize its benefits. Our proposed strategy can dynamically adjust the task-offloading probability to acquire the maximum utility for each vehicle. However, we used a best response offloading strategy algorithm for the task-offloading game in order to achieve a unique and stable equilibrium. Numerous simulation experiments affirm that our proposed scheme fulfills the performance guarantees and can reduce the response time and task-failure rate by almost 47.6% and 54.6%, respectively, when compared with the local RSU computing (LRC) scheme. Moreover, the reduced rates are approximately 32.6% and 39.7%, respectively, when compared with a random offloading scheme, and approximately 26.5% and 28.4%, respectively, when compared with a collaborative offloading scheme.

Reaction Mechanism and Strategy for Optimizing the Hydrogen Evolution Reaction on Single-Layer 1T' WSe2 and WTe2 Based on Grand Canonical Potential Kinetics.

Transition-metal dichalcogenides (TMDs) in the 1T' phase are known high-performance catalysts for hydrogen evolution reaction (HER). Many experimental and some theoretical studies report that vacant sites play an important role in the HER on the basal plane. To provide benchmark calculations for comparison directly with future experiments on TMDs to obtain a validated detailed understanding that can be used to optimize the performance and material, we apply a recently developed grand canonical potential kinetics (GCP-K) formulation to predict the HER at vacant sites on the basal plane of the 1T' structure of WSe2 and WTe2. The accuracy of GCP-K has recently been validated for single-crystal nanoparticles. Using the GCP-K formulation, we find that the transition-state structures and the concentrations of the four intermediates (0-3 H at the selenium or tellurium vacancy) change continuously as a function of the applied potential. The onset potential (at 10 mA/cm-2) is -0.53 V for WSe2 (experiment is -0.51 V) and -0.51 V for WTe2 (experiment is -0.57 V). We find multistep reaction mechanisms for H2 evolution from Volmer-Volmer-Tafel (VVT) to Volmer-Heyrovsky (VH) depending on the applied potential, leading to an unusual non-monotonic change in current density with the applied potential. For example, our detailed understanding of the reaction mechanism suggests a strategy to improve the catalytic performance significantly by alternating the applied potential periodically.

Detecting autism spectrum disorder using machine learning techniques: An experimental analysis on toddler, child, adolescent and adult datasets.

Autism Spectrum Disorder (ASD), which is a neuro development disorder, is often accompanied by sensory issues such an over sensitivity or under sensitivity to sounds and smells or touch. Although its main cause is genetics in nature, early detection and treatment can help to improve the conditions. In recent years, machine learning based intelligent diagnosis has been evolved to complement the traditional clinical methods which can be time consuming and expensive. The focus of this paper is to find out the most significant traits and automate the diagnosis process using available classification techniques for improved diagnosis purpose. We have analyzed ASD datasets of toddler, child, adolescent and adult. We have evaluated state-of-the-art classification and feature selection techniques to determine the best performing classifier and feature set, respectively, for these four ASD datasets. Our experimental results show that multilayer perceptron (MLP) classifier outperforms among all other benchmark classification techniques and achieves 100% accuracy with minimal number of attributes for toddler, child, adolescent and adult datasets. We also identify that 'relief F' feature selection technique works best for all four ASD datasets to rank the most significant attributes.

Fuzzy Decision-Based Efficient Task Offloading Management Scheme in Multi-Tier MEC-Enabled Networks.

Multi-access edge computing (MEC) is a new leading technology for meeting the demands of key performance indicators (KPIs) in 5G networks. However, in a rapidly changing dynamic environment, it is hard to find the optimal target server for processing offloaded tasks because we do not know the end users' demands in advance. Therefore, quality of service (QoS) deteriorates because of increasing task failures and long execution latency from congestion. To reduce latency and avoid task failures from resource-constrained edge servers, vertical offloading between mobile devices with local-edge collaboration or with local edge-remote cloud collaboration have been proposed in previous studies. However, they ignored the nearby edge server in the same tier that has excess computing resources. Therefore, this paper introduces a fuzzy decision-based cloud-MEC collaborative task offloading management system called FTOM, which takes advantage of powerful remote cloud-computing capabilities and utilizes neighboring edge servers. The main objective of the FTOM scheme is to select the optimal target node for task offloading based on server capacity, latency sensitivity, and the network's condition. Our proposed scheme can make dynamic decisions where local or nearby MEC servers are preferred for offloading delay-sensitive tasks, and delay-tolerant high resource-demand tasks are offloaded to a remote cloud server. Simulation results affirm that our proposed FTOM scheme significantly improves the rate of successfully executing offloaded tasks by approximately 68.5%, and reduces task completion time by 66.6%, when compared with a local edge offloading (LEO) scheme. The improved and reduced rates are 32.4% and 61.5%, respectively, when compared with a two-tier edge orchestration-based offloading (TTEO) scheme. They are 8.9% and 47.9%, respectively, when compared with a fuzzy orchestration-based load balancing (FOLB) scheme, approximately 3.2% and 49.8%, respectively, when compared with a fuzzy workload orchestration-based task offloading (WOTO) scheme, and approximately 38.6%% and 55%, respectively, when compared with a fuzzy edge-orchestration based collaborative task offloading (FCTO) scheme.

Hydrazine Detection during Ammonia Electro-oxidation Using an Aggregation-Induced Emission Dye.

Ammonia electro-oxidation is an extremely significant reaction with regards to the nitrogen cycle, hydrogen economy, and wastewater remediation. The design of efficient electrocatalysts for use in the ammonia electro-oxidation reaction (AOR) requires comprehensive understanding of the mechanism and intermediates involved. In this study, aggregation-induced emission (AIE), a robust fluorescence sensing platform, is employed for the sensitive and qualitative detection of hydrazine (N2H4), one of the important intermediates during the AOR. Here, we successfully identified N2H4 as a main intermediate during the AOR on the model Pt/C electrocatalyst using 4-(1,2,2-triphenylvinyl)benzaldehyde (TPE-CHO), an aggregation-induced emission luminogen (AIEgen). We propose the AOR mechanism for Pt with N2H4 being formed during the dimerization process (NH2 coupling) within the framework of the Gerischer and Mauerer mechanism. The unique chemodosimeter approach demonstrated in this study opens a novel pathway for understanding electrochemical reactions in depth.

Elimination of Uremic Toxins by Functionalized Graphene-Based Composite Beads for Direct Hemoperfusion.

Conventional absorbents for hemoperfusions suffer from low efficiency and slow absorption with numerous side effects. In this research, we developed cellulose acetate (CA) functionalized graphene oxide (GO) beads (∼1.5-2 mm) that can be used for direct hemoperfusion, aiming at the treatment of kidney dysfunction. The CA-functionalized GO bead facilitates adsorption of toxins with high biocompatibility and high-efficiency of hemoperfusion while maintaining high retention for red blood cell, white blood cells, and platelets. Our in vitro results show that the toxin concentration for creatinine reduced from 0.21 to 0.12 µM (p < 0.005), uric acid from 0.31 to 0.15 mM (p < 0.005), and bilirubin from 0.36 to 0.09 mM (p < 0.005), restoring to normal levels within 2 h. Our in vivo study on rats (Sprague-Dawley, n = 30) showed that the concentration for creatinine reduced from 83.23 to 54.87 µmol L-1 (p < 0.0001) and uric acid from 93.4 to 54.14 µmol L-1 (p < 0.0001), restoring to normal levels within 30 min. Results from molecular dynamics (MD) simulations using free-energy calculations reveal that the presence of CA on GO increases the surface area for adsorption and enhances penetration of toxins in the binding cavities because of the increased electrostatic and van der Waals force (vdW) interactions. These results provide critical insight to fabricate graphene-based beads for hemoperfusion and to have the potential for the treatment of blood-related disease.

Reaction mechanism and kinetics for CO2 reduction on nickel single atom catalysts from quantum mechanics.

Experiments have shown that graphene-supported Ni-single atom catalysts (Ni-SACs) provide a promising strategy for the electrochemical reduction of CO2 to CO, but the nature of the Ni sites (Ni-N2C2, Ni-N3C1, Ni-N4) in Ni-SACs has not been determined experimentally. Here, we apply the recently developed grand canonical potential kinetics (GCP-K) formulation of quantum mechanics to predict the kinetics as a function of applied potential (U) to determine faradic efficiency, turn over frequency, and Tafel slope for CO and H2 production for all three sites. We predict an onset potential (at 10 mA cm-2) Uonset = -0.84 V (vs. RHE) for Ni-N2C2 site and Uonset = -0.92 V for Ni-N3C1 site in agreement with experiments, and Uonset = -1.03 V for Ni-N4. We predict that the highest current is for Ni-N4, leading to 700 mA cm-2 at U = -1.12 V. To help determine the actual sites in the experiments, we predict the XPS binding energy shift and CO vibrational frequency for each site.

Highly Reversible Sodiation/Desodiation from a Carbon-Sandwiched SnS2 Nanosheet Anode for Sodium Ion Batteries.

The further improvement of sodium ion batteries requires the elucidation of the mechanisms pertaining to reversibility, which allows the novel design of the electrode structure. Here, through a hydrogel-embedding method, we are able to confine the growth of few-layer SnS2 nanosheets between a nitrogen- and sulfur-doped carbon nanotube (NS-CNT) and amorphous carbon. The obtained carbon-sandwiched SnS2 nanosheets demonstrate excellent sodium storage properties. In operando small-angle X-ray scattering combined with the ex situ X-ray absorption near edge spectra reveal that the redox reactions between SnS2/NS-CNT and the sodium ion are highly reversible. On the contrary, the nanostructure evolution is found to be irreversible, in which the SnS2 nanosheets collapse, followed by the regeneration of SnS2 nanoparticles. This work provides operando insights into the chemical environment evolution and structure change of SnS2-based anodes, elucidating its reversible reaction mechanism, and illustrates the significance of engineered carbon support in ensuring the electrode structure stability.

Revealing the mechanism of DNA passing through graphene and boron nitride nanopores.

Nanopores on 2D materials have great potential for DNA sequencing, which is attributed to their high sequencing speed and reduced cost. However, identifying DNA bases at such a high speed with nanometer precision has remained a big challenge. Here, we implemented theoretical calculations to show the translocation of single-stranded DNA (ssDNA) through solid-state nanopores on a 2D hexagonal boron nitride (h-BN) and graphene sheet. A base-specific ssDNA sequencing technique was devised, based on the individual differences in the ion current responses for the (polyA)16, (polyG)16, (polyC)16, and (polyT)16 bases of ssDNA. Our sequential procedure for sequencing is built on a comparative approach between the current signals obtained from the nanopores to achieve base-specific detection. Our results indicate that at higher voltages (1.0, 1.2, 1.4, 1.6, 1.8 and 2.0 V nm-1), DNA translocation is tracked though the 1.5 and 2.0 nm nanopores, and at the 1.5 nm pore size, folded ssDNA close to the nanopore accounts for 93% and 81% of events for graphene and h-BN. Our calculations indicate charge transfer from the graphene to ssDNA, while the reverse happens in the case of the h-BN membrane. These results provide critical insights into our understanding of single molecule sequencing through solid-state nanopore research.

Detecting Child Autism Using Classification Techniques.

Autism spectrum disorder (ASD) is a brain development disorder that restricts a person's communication abilities and social interaction capabilities from natural growth. In this paper, we have applied various supervised classification techniques to detect the presence of child autism. Our findings show that the Sequential Minimal Optimization (SMO) classifier performs best to detect ASD cases with the highest accuracy and minimum execution time and error rate. We also identify the most dominant features in dectecting child autism.

Confinement-Enhanced Rapid Interlayer Diffusion within Graphene-Supported Anisotropic ReSe2 Electrodes.

To enhance interlayer lithium diffusion, we engineer electrodes consisting of epitaxially grown ReSe2 nanosheets by chemical vapor deposition, supported on three-dimensional (3D) graphene foam, taking advantage of its weak van der Waals coupling and anisotropic crystal structure. We further demonstrate its excellent performance as the anode for lithium-ion battery and catalyst for hydrogen evolution reaction (HER). Density functional theory calculation reveals that ReSe2 exhibits a low energy barrier for lithium (Li) interlayer diffusion because of negligible interlayer coupling and anisotropic structure with low symmetry that creates additional adsorption sites and leads to a reduced diffusion barrier. Benefitting from these properties, the 3D ReSe2/graphene foam electrode displays excellent cycling and rate performance with 99.6% capacity retention after 350 cycles and a capacity of 327 mA h g-1 at the current density of 1000 mA g-1. Additionally, it has exhibited a high activity for HER, in which an exchange current density of 277.8 µA cm-2 is obtained and only an overpotential of 106 mV is required to achieve a current density of -10 mA cm-2. Our work provides a fundamental understanding of the interlayer diffusion of Li in transition-metal dichalcogenide (TMD) materials and acts as a new tool for designing a TMD-based catalyst.

Determinants of overweight and obesity among urban school-going children and adolescents: a case-control study in Bangladesh.

BACKGROUND: Childhood overweight and obesity is a major public health concern all over the world. Overweight or obese children have a higher risk of becoming obese in adulthood and are at higher risk of associated chronic diseases. OBJECTIVE: The aim of this study was to determine the risk factors associated with overweight and obesity among urban school children and adolescents in Bangladesh. MATERIALS AND METHODS: A case-control study was conducted among students aged 10-16 years in 10 schools of Dhaka city. A structured questionnaire was used to collect socio-demographic information and students' exposure to various risk factors. Data were analyzed by using SPSS 23. RESULTS: Family income (p = 0.000), mother's weight (p = 0.036), school activity (p = 0.046), total physical activity (p = 0.008), sedentary activities (p = 0.014), eating fast food (0.008) and cakes/biscuits (p = 0.018) were found as potential determinants of overweight and obesity of children and adolescents. A multiple logistic regression revealed family income >50,000 per month [adjusted odds ratio (AOR) = 3.07, p = 0.001], no physical activity (AOR = 38.3, p = 0.004), more than 4 h of sedentary activities (AOR = 4.84, p = 0.02), regular consumption of fast food (AOR = 3.05, p = 0.042) are risk factors associated with childhood overweight/obesity. Whereas, having a normal weight mother (AOR = 0.51, p = 0.037) and eating cakes/biscuits less than 3 days a week (AOR = 0.33, p = 0.02) were found as protective factors. CONCLUSION: Findings from this study will be very useful for public health professionals to increase awareness regarding risk factors of overweight and obesity, in order to reduce the future burden of obesity-associated chronic diseases.

Selenium Edge as a Selective Anchoring Site for Lithium-Sulfur Batteries with MoSe2/Graphene-Based Cathodes.

For lithium-sulfur batteries (LSBs), the dissolution of lithium polysulfide and the consequent "shuttle effect" remain major obstacles for their practical applications. In this study, we designed a new cathode material comprising MoSe2/graphene to selectively adsorb polysulfides on the selenium edges and thus to mitigate their dissolution. More specifically, few-layered MoSe2 was first grown on nitrogen-doped reduced graphene oxide (N-rGO) using the chemical vapor deposition method and then infiltrated with sulfur as the cathode for LSBs. An initial capacity of 1028 mA h g-1 was achieved for S/MoSe2/N-rGO at 0.2 C, higher than 981 and 405.1 mA h g-1 for pure graphene and sulfur, respectively, along with enhanced cycling durability and rate capability. Moreover, the density functional theory simulation, in addition to the experimental adsorption test, X-ray photoelectron spectroscopy analysis, and transmission electron microscopy technique, reveals the dual roles that MoSe2 plays in improving the performance of LSBs by functioning as the binding sites for lithium polysulfides and as the platform that enables fast Li-ion diffusion by reducing its diffusion barrier. The reported finding suggests that the transition-metal selenides could be an efficient alternative material as the cathode for LSBs.

Modular functionalization of crystalline graphene by recombinant proteins: a nanoplatform for probing biomolecules.

Graphene, as well as other two-dimensional materials, is a promising candidate for use in bioimaging, therapeutic drug delivery, and bio-sensing applications. Here, we developed a protocol to functionalize graphene with recombinant proteins using genetically encoded SpyTag-SpyCatcher chemistry. SpyTag forms a covalent isopeptide bond with its genetically encoded partner SpyCatcher through spontaneous amidation under physiological conditions. The functionalization protocol developed is based on the use of short proteins as a linker, where two graphene-binding-peptides (GBPs) are attached to both ends of SpyTag (referred to as GStG), followed by the covalent conjugation with SpyCatcher-fusion proteins. The proposed method enables the decoration of crystalline graphene with various proteins, such as fluorescent proteins and affibody molecules that bind to cancerous cells. This scheme, which takes advantage of the cleanness of single-crystal graphene and the robustness of SpyTag-SpyCatcher chemistry, provides a versatile platform on which to study the biomolecule-surface and cell-substrate interactions and, indeed, may lead to a new way of designing biomedical devices. The interaction between peptides and graphene was clearly shown using molecular dynamics simulation and proven using specially designed experiments.