Two-dimensional Clusters Research Articles

An automated toolbox for microcalcification cluster modeling for mammographic imaging.

Mammographic imaging is essential for breast cancer detection and diagnosis. In addition to masses, calcifications are of concern and the early detection of breast cancer also heavily relies on the correct interpretation of suspicious microcalcification clusters. Even with advances in imaging and the introduction of novel techniques such as digital breast tomosynthesis and contrast-enhanced mammography, a correct interpretation can still be challenging given the subtle nature and large variety of calcifications. Computer simulated lesion models can serve to develop, optimize, or improve imaging techniques. In addition to their use in comparative (virtual clinical trial) detection experiments, these models have potential application in training deep learning models and in the understanding and interpretation of breast lesions. Existing simulation methods, however, often lack the capacity to model the diversity occurring in breast lesions or to generate models relevant for a specific case. This study focuses on clusters of microcalcifications and introduces an automated, flexible toolbox designed to generate microcalcification cluster models customized to specific tasks. The toolbox allows users to control a large number of simulation parameters related to model characteristics such as lesion size, calcification shape, or number of microcalcifications per cluster. This leads to the capability of creating models that range from regular to complex clusters. Based on the input parameters, which are either tuned manually or pre-set for a specific clinical type, different sets of models can be simulated depending on the use case. Two lesion generation methods are described. The first method generates three-dimensional microcalcification clusters models based on geometrical shapes and transformations. The second method creates two-dimensional (2D) microcalcification cluster models for a specific 2D mammographic image. This novel method employs radiomics analysis to account for local textures, ensuring the simulated microcalcification cluster is appropriately integrated within the existing breast tissue. The toolbox is implemented in the Python language and can be conveniently run through a Jupyter Notebook interface, openly accessible at https://gitlab.kuleuven.be/medphysqa/deploy/breast-calcifications. Validation studies performed by radiologists assessed the level of malignancy and realism of clusters tuned with specific parameters and inserted in mammographic images. The flexibility of the toolbox with multiple simulation methods is illustrated, as well as the compatibility with different simulation frameworks and image types. The automation allows for the straightforward and fast generation of diverse microcalcification cluster models. The generated models are most likely applicable for various tasks as they can be configured in a variety of ways and inserted in different types of mammographic images of multiple acquisition systems. Validation studies confirmed the capacity to simulate realistic clusters and capture clinical properties when tuned with appropriate parameter settings. This simulation toolbox offers a flexible means of simulating microcalcification cluster models with potential use in both technical and clinical research in mammography imaging. The 3D generation methods allow for specifying many characteristics regarding the calcification shape and cluster architecture, and the 2D generation method presents a novel manner to create microcalcification clusters tailored to existing breast textures.

Identification of anti-inflammatory and anti-cancer compounds targeting the NF-κB-NLRP3 inflammasome pathway from a phytochemical library of the Sideritis genus

Ethnobotanical relevanceFor centuries, the aerial parts of Sideritis species have been known for their medicinal properties as herbal teas. Although the antioxidant and anti-inflammatory properties of the genus have been widely documented, the underlying mechanisms are yet to be sufficiently clarified. Aim of the studyWe investigated the anti-inflammatory and anticancer activities of phytochemicals of the Sideritis genus. Material and methodsThrough literature mining, a chemical library containing 657 components of the Sideritis genus was formed. We studied these compounds for binding to NLRP3 and NF-κB proteins in silico by virtual drug screening and molecular docking, and in vitro by microscale thermophoresis (MST). Liquid chromatography-high-resolution mass spectrometry analysis (LC-HRMS) was performed in the Sideritis extracts. One of the identified compounds, verbascoside, was investigated for its cytotoxic activity by mining a panel of 49 tumor cell lines in the data repository of the National Cancer Institute (NCI, USA). ResultsVirtual screening and molecular docking results highlighted two compounds targeting both proteins of interest, i.e., verbascoside (acteoside) and apigenin 7,4′-bis(trans-p-coumarate), as both had lowest binding energies of less than −10 kcal/mol. Using MST, we then verified that both compounds bound to the target proteins. Verbascoside bound to NLRP3 and NF-κB with Kd values of 0.67 ± 0.18 μM and 0.01 ± 0.08 μM, while apigenin 7,4′-bis(trans-p-coumarate) had Kd values of 4.60 ± 1.66 μM and 0.27 ± 0.75 μM, respectively. Verbascoside was abundant in the Sideritis extracts, according to LC-HRMS analysis. Since inflammation is strongly related to carcinogenesis, we investigated the anticancer activity of verbascoside in the second part of this study. We investigated the activity of verbascoside in 49 tumor cell lines of the NCI. Comparing its activity with 81 standard anticancer drugs revealed numerous interactions with DNA-damaging agents (alkylators, topoisomerase I/II inhibitors, antimetabolites), indicating that verbascoside may also affect the DNA of tumor cells. We further investigated the involvement of verbascoside in several main drug resistance mechanisms, i.e., ABC transporters, oncogenes, tumor suppressors, cellular proliferation rates, and other parameters. Except for the correlation to the mutational status of NRAS, no other significant relationships were found, indicating that verbascoside is not involved in most of the common drug resistance mechanisms. Two-dimensional cluster analysis-based heatmap generation of a proteomic profile from 40 out of 3171 proteins revealed a significant correlation between the expression of these proteins in 49 tumor cell lines, and the cellular response to verbascoside. This indicates that the presence of these proteins is a determinant for sensitivity or resistance to this natural product. ConclusionThe database established here represents a valuable resource for the screening of bioactivites of the Sideritis genus. The experimental validation of the anti-inflammatory and cytotoxic activities of selected compounds proved that virtual drug screening and molecular docking are suitable tools for the identification of putative drug candidates. Verbascoside was among the top 10 compounds binding to two key anti-inflammatory proteins, NLRP3 and NF-kB. Additionally, data from the NCI indicate that verbascoside is not linked to main drug resistance mechanisms.

W-system: A system to understand words in Wordle

Abstract Wordle is a popular puzzle game where players share their daily scores on Twitter. Based on this data, we developed a W-system to analyze and predict Wordle game outcomes, providing insights for further game development. The number of reported results varies daily. To explain this variation, we first cleaned the data reported on Twitter and performed autocorrelation and ADF tests, revealing that the series was not stationary. After performing first-order differencing, we confirmed stationarity. Using the AIC criterion, we determined that the ARIMA (1, 1, 0) model was optimal. We performed residual and LB tests, and then predicted the number of reports for the next 60 days, identifying a decreasing trend. Additionally, we investigated whether any attributes of the words affected the percentage of scores reported in Hard Mode using a random forest model. We found that certain vowels, such as A and E, made words easier, while I and O made words harder. To predict the distribution of reported results for a given future solution word, we designed a 24-5-7 fully connected neural network. The input layer consists of a 24-dimensional vector representing letter occurrences, the hidden layer has five neurons, and the output layer is a 7-dimensional vector representing the distribution of scores. We trained the model using MSE error and the Adam optimizer, achieving a final test set loss of 7.22, indicating good generalization ability. For example, for the word EERIE on March 1, 2023, the model predicted distribution with reasonable confidence, accounting for associated uncertainties. We also developed a model to classify solution words by difficulty. We identified eight attributes of words and used the Analytic Hierarchy Process (AHP) to assign weights to each feature. Applying the k-means clustering algorithm, we classified 355 words into three categories: easy, medium, and hard. Using our model, the word EERIE was classified as medium difficulty. We validated our model by plotting a two-dimensional clustering scatter plot using the two features with the highest weights, showing good separation among the three categories, and indicating the accuracy of our classification model.

Characterizing the surface compositions of supported bimetallic PtSn clusters: Effects of cluster-support interactions and surface adsorbates

PtSn bimetallic clusters on TiO2(110) and highly oriented pyrolytic graphite (HOPG) surfaces have been characterized by scanning tunneling microscopy, low energy ion scattering (LEIS), X-ray photoelectron spectroscopy, and temperature programmed desorption (TPD); density functional theory (DFT) calculations have also been performed to better understand adsorption of CO and D2 on the PtSn surfaces. On TiO2 at coverages of 2 ML of Pt and 2 ML of Sn, exclusively bimetallic clusters are formed for both orders of deposition because clusters of the first metal completely cover the surface such that all atoms of the second metal are incorporated into the existing clusters. In contrast, on HOPG, the high mobility and weak cluster-support interactions on HOPG result in much larger 2 ML monometallic clusters (∼30 Å high) that do not completely cover the surface, and deposition of the second metal produces larger clusters as well as smaller ones. Despite the difference in cluster morphologies for the different orders of deposition and supports, the LEIS experiments demonstrate that in all cases, the PtSn clusters are rich in Sn at the surface, as expected based on the lower surface free energy for Sn compared to Pt. Furthermore, the +0.2 eV shift in the Sn(3d5/2) binding energy observed on all surfaces in the presence of Pt is consistent with PtSn alloy formation. Deposition of 2 ML of Sn on TiO2 produces two-dimensional clusters with oxidation of Sn and reduction of titania at the cluster-support interface, but addition of Pt to the Sn clusters causes Sn to diffuse away from this interface, leaving Sn in the metallic state. TPD experiments on 2 ML Pt/TiO2 with increasing coverages of Sn show that the number of adsorption sites for D2 sharply decreases to nearly zero at 0.5 ML, while CO adsorption decreases to zero only at much higher Sn coverages of 2 ML. DFT studies for Sn modified Pt surfaces and bulk structures demonstrate that for CO adsorption at low Sn coverages (≤0.25 ML), the strong Pt-CO interactions induce diffusion of Pt to the cluster surface and the formation of a bulk Pt3Sn alloy, whereas D2 adsorption does not lead to interactions with the Pt surface that are strong enough to induce alloy formation. A single Sn adatom prevents D2 adsorption on four neighboring Pt atoms via site-blocking and the donation of electron density to Pt.

Flexible quantum data bus for quantum networks

We consider multipath generation of Bell states in quantum networks, where a preprepared multipartite entangled two-dimensional cluster state serves as a resource to perform different tasks on demand. We show how to achieve parallel connections between multiple, freely chosen groups of parties by performing appropriate local measurements along a diagonal, staircase-shaped path on a two-dimensional cluster state. Remarkably, our measurement scheme preserves the entanglement structure of the cluster state such that the remaining state is again a two-dimensional cluster state. We demonstrate strategies for generating crossing, turning, and merging of multiple measurement lines along the two-dimensional cluster state. The results apply to local area as well as to long-distance networks. Published by the American Physical Society 2024

From singularity analysis to singularity avoidance: novel metric and convex allocation for spacecraft attitude control with control moment gyros

Singularities in robotics lead to a reduction in the number of degrees of freedom. When it comes to spacecraft employing control moment gyros (CMGs), singularities may occur due to the internal alignment of the gimbals, which inhibit the creation of torque in at least one direction. This translates into a loss of control authority that has a direct impact on the spacecraft’s pointing performance. In this work, a convex optimization-based allocation framework for singularity avoidance is put forward. The presented solution aims to provide a singularity-robust allocation scheme that can be used as an add-on to a conventional attitude controller. To this end, the proposed algorithm employs a novel, computationally efficient, and numerically robust singularity metric to assess the proximity to the singularities of a cluster of CMGs. With this information, a model predictive controller determines control actions that guide the system towards singularity-free configurations. A particularity about this allocation algorithm is that its formulation boils down to a simple set of convex equalities and inequalities, given that the new singularity metric can be written in a linear form, unlike most of the literature solutions, such as the condition number, which are highly complex and nonlinear. Lastly, the proposed approach is applied to a two-dimensional CMG cluster in a realistic simulation environment. The results confirm that the system effectively avoids all the internal singularities of the cluster at a relatively low computational expense.

Higher-Order Cellular Automata Generated Symmetry-Protected Topological Phases and Detection Through Multi Point Strange Correlators

In computer and system sciences, higher-order cellular automata (HOCA) are a type of cellular automata that evolve over multiple time steps and generate complex patterns, which have various applications, such as secret-sharing schemes, data compression, and image encryption. In this paper, we introduce HOCA to quantum many-body physics and construct a series of symmetry-protected topological (SPT) phases of matter, in which symmetries are supported on a great variety of subsystems embbeded in the SPT bulk. We call these phases HOCA-generated SPT (HGSPT) phases. Specifically, we show that HOCA can generate not only well-understood SPTs with symmetries supported on either regular (e.g., linelike subsystems in the two-dimensional cluster model) or fractal subsystems, but also a large class of unexplored SPTs with symmetries supported on more choices of subsystems. One example is that has either fractal and linelike subsystem symmetries simultaneously or two distinct types of fractal symmetries simultaneously. Another example is in which chaotic-looking symmetries are significantly different from and thus cannot reduce to fractal or regular subsystem symmetries. We also introduce a new notation system to characterize HGSPTs. We prove that all possible subsystem symmetries in a square lattice can be locally simulated by an HOCA-generated symmetry. As the usual two-point strange correlators are trivial in most HGSPTs, we find that the nontrivial SPT orders can be detected by what we call . We propose a universal procedure to design the spatial configuration of the multi point strange correlators for a given HGSPT phase. Specifically, we find deep connections between multi point strange correlators and the spurious topological entanglement entropy (STEE), both exhibiting long-range behavior in a short-range entangled state. Our HOCA approaches and multi point strange correlators pave the way for a unified paradigm to design, classify, and detect phases of matter with symmetries supported on a great variety of subsystems, and also provide potential useful perspective in surpassing the computational irreducibility of HOCA in a quantum mechanical way. Published by the American Physical Society 2024

Precursor Chemistry of Lead Bromide Perovskite Nanocrystals.

We investigate the early stages of cesium lead bromide perovskite formation through absorption spectroscopy of stopped-flow reactions, high-throughput mapping, and direct synthesis and titration of potential precursor species. Calorimetric and spectroscopic measurements of lead bromide complex titrations combined with theoretical calculations suggest that bromide complexes with higher coordination numbers than previously considered for nonpolar systems can better explain observed behaviors. Synthesis mapping of binary lead halides reveals multiple lead bromide species with absorption peaks higher than 300 nm, including a previously observed species with a peak at 313 nm and two species with peaks at 345 and 370 nm that also appear as reaction intermediates during the formation of lead bromide perovskites. Based on theoretical calculations of excitonic energies that match within 50 meV, we give a preliminary assignment of these species as two-dimensional magic-sized clusters with side lengths of 2, 3, and 4 unit cells. Kinetic measurements of the conversion of benzoyl bromide precursor are connected to stopped-flow measurements of product formation and demonstrate that the formation of complexes and magic-sized clusters (i.e., nucleation) is controlled by precursor decomposition, whereas the growth rate of 2D and 3D perovskites is significantly slower.

Nanozyme-Inhibited SERS Multichannel Paper-Based Sensor Array for the Quantification and Identification of Biothiols and Cancer Cells Based on Three Ag-Based Nanomaterials.

Biothiols play essential roles in maintaining normal physiological functions, resisting oxidative stress, and protecting cell health. Establishing an effective and reliable sensor array for the accurate quantification and discrimination of diverse biothiols is extremely meaningful. In this work, Ag/Mn3O4, Ag3PO4, and Ag3Cit with excellent oxidase-mimetic activity and surface-enhanced Raman scattering (SERS)-enhanced features have been prepared and loaded onto Whatman filter paper (WFP) to build SERS paper chips as three sensing channels, which can induce 3,3',5,5'-tetramethylbenzidine (TMB) oxidation to SERS-active reporters (TMBox) and concurrently generate prominent SERS signals. Nevertheless, the addition of biothiols can suppress conversion from TMB to TMBox, which can cause the reduction of the SERS signal from TMBox. Interestingly, each SERS sensing channel can generate different TMBox signals' variations due to differences in the oxidative inhibition abilities of diverse biothiols and exclusive properties of each paper chip, which can be plotted as specific fingerprint patterns of each biothiol and further translated into intuitive two-dimensional (2D) clustering profiles through linear discriminant analysis (LDA) and hierarchical cluster analysis (HCA) techniques for precise identification of these six biothiols with the minimum concentration of 1 μM. More importantly, this SERS sensor array is exploited for the precise quantification of intracellular glutathione (GSH), and can differentiate between normal and cancer cells based on different intracellular GSH contents and even identify different types of tumor cells, demonstrating its powerful application prospects in disease diagnosis.

Structure of methylaluminoxane (MAO): Extractable [Al(CH 3 ) 2 ] + for precatalyst activation

Methylaluminoxane (MAO) is used as a precatalyst activator for million ton–scale production of commercial polyolefins, but its precise structure and associated activation mechanisms have been a fundamental research puzzle for more than 40 years. We report here the crystallographic characterization of an active MAO component, which reveals a discrete two-dimensional sheet cluster [Al 33 O 26 (CH 3 ) 47 ][Al(CH 3 ) 3 ] 2 featuring two trimethylaluminum units, Al(CH 3 ) 3 , coordinated to two unsaturated aluminum sites. Our structural data are consistent with the hypothesis that the active sites bear coordinated Al(CH 3 ) 3 and provide [Al(CH 3 ) 2 ] + for precatalyst activation. Quantum chemical calculations revealed the most preferred sites for [Al(CH 3 ) 2 ] + abstraction (a change in Gibbs free energy of 0.0 kilocalories per mole). Olefin polymerization tests on metallocenes activated with the crystallized MAO show higher activities than with the commercial MAO.

Deterministic Generation of Qudit Photonic Graph States from Quantum Emitters

We propose and analyze deterministic protocols to generate qudit photonic graph states from quantum emitters. We show that our approach can be applied to generate any qudit graph state and we exemplify it by constructing protocols to generate one- and two-dimensional qudit cluster states, absolutely maximally entangled states, and logical states of quantum error-correcting codes. Some of these protocols make use of time-delayed feedback, while others do not. The only additional resource requirement compared to the qubit case is the ability to control multilevel emitters. These results significantly broaden the range of multiphoton entangled states that can be produced deterministically from quantum emitters. Published by the American Physical Society 2024

Unravelling K intercalation on graphene and its variants through two-dimensional cluster expansion modeling

In this study, we investigate K ordering and its impact on K intercalation and storage capacity in graphite, bilayer and single-layer graphene through two-dimensional cluster expansion implemented in first-principles calculations with van der Waals corrections. For graphite, similar to the Li counterpart, K intercalation exhibits a multistage feature as the K concentration increases from C2 to KC8. However, beyond KC8, K atoms become crowded in graphite, resulting in a negative discharge voltage. For bilayer and single-layer graphene, a noticeable displacement of K from original hexagonal sites occurs at higher K concentrations compared to graphite. K intercalation in graphite after the initial segment (from C2 to KC8) leads to an increase in interlayer distance, while in bilayer graphene causes much larger elongation in interlayer distance. The calculated overpotential for K intercalation on bilayer and single-layer graphene is found to be 0.17 and 0.13 V, respectively, which is lower than the overpotential (0.86 V) observed on graphite. The charge redistribution between K and carbon layers indicates that bilayer and single-layer graphene offer pseudo-capacitance, facilitating faster charge transfer and higher K capacity. The above observation suggests the graphene structures have a superior K-storage capacity compared to graphite.

Producing two-dimensional dust clouds and clusters using a movable electrode for complex plasma and fundamental physics experiments.

We report a Bidirectional Electrode Control Arm Assembly (BECAA) for precisely manipulating dust clouds levitated above the powered electrode in RF plasmas. The reported techniques allow the creation of perfectly 2D dust layers by eliminating off-plane particles by moving the electrode from outside the plasma chamber without altering the plasma conditions. The tilting and moving of electrodes using BECAA also allows the precise and repeatable elimination of dust particles one by one to achieve any desired number of grains N without trial and error. Simultaneously acquired top and side view images of dust clusters show that they are perfectly planar or 2D. A demonstration of clusters with N = 1-28 without changing the plasma conditions is presented to show the utility of BECAA for complex plasma and statistical physics experimental design. Demonstration videos and 3D printable part files are available for easy reproduction and adaptation of this new method to repeatably produce 2D clusters in existing RF plasma chambers.

Improving Shopping Mall Revenue by Real-Time Customized Digital Coupon Issuance

With the development of big data and deep learning technology, big data and deep learning technology have also been applied to the marketing field, which was a part of business administration. Customer churn management is one of the most important areas of marketing. In this paper, we proposed a method to prevent customer churn and increase purchase conversion rate by issuing customized discount coupons to customers with high churn rate based on big data in real time. After segmenting customer segments with two-dimensional segment analysis, a real-time churn rate estimation model based on clickstream data was generated for each segment. After that, we issued customized coupons to our customers. Finally, we tested the conversion rate and sales growth. A two-dimensional cluster analysis-based churn rate estimation combined with a recommendation system was found to be significantly more useful than the respective simple models. Using this proposed model, it is possible to increase sales by automatically estimating the customer’s churn probability and shopping propensity without the burden of marketing costs in the online shopping mall. Keyword: Customer churn prediction, deep learning, digital marketing, ecommerce, recommendation system.

Two-dimensional modulation instability and soliton clusters in nonlocal media with competing cubic–quintic nonlinearities

Using linear-stability analysis, two-dimensional modulation instability (MI) of plane wave is studied in nonlocal media with competing cubic–quintic (CQ) nonlinearities, which shows that MI can be effectively eliminated by strong nonlocality and competing quintic nonlinearity. Furthermore, propagation properties of higher-order soliton clusters, i.e., Hermite–Gaussian (HG) and Laguerre–Gaussian (LG) solitons are also investigated. Bifurcated solutions of these solitons are obtained analytically with variational approach. We also demonstrate in detail the propagation dynamics of the HG and LG solitons with split-step Fourier transform.

A micro-Doppler spectrogram denoising algorithm for radar human activity recognition

Radar signal recognition based on micro-Doppler spectrogram has been widely used in human action recognition tasks. However, in practical application scenarios, radar signals inevitably have noise, which leads to different degrees of deformation of the spectrogram graph structure and affects the accuracy of subsequent recognition algorithms. In this paper, we present “ACFL”, a novel algorithm for micro-Doppler spectrogram denoising, which aims to reduce the impact of noise on human action recognition. ACFL employs amplitude–frequency two-dimensional clustering and fuzzy logic clustering selection mechanism to remove noise elements from the spectrogram. Moreover, to address the issue of noise leakage or target missing under time-varying noise and action conditions, ACFL adopts spectrogram segmentation based on short-term Rényi entropy. By dividing the spectrogram into intervals with different time–frequency distributions, the dynamic spectrogram denoise over time is achieved. Simulation and measured data experiments demonstrate that the proposed algorithm not only achieves a higher-quality denoised spectrogram but also significantly improves the accuracy of human action recognition under noisy conditions.

Nanoantennas report dissipative assembly in oscillatory electric fields

Understanding driving forces for dissipative, i.e., out of equilibrium, assembly of nanoparticles from colloidal solution at liquid–solid interfaces provides the ability to design external cues for reconfigurable device response. Here electrohydrodynamic flow (EHD) at an electrode-liquid interface is investigated as a dissipative driving force for tuning optical response. EHD results from an oscillatory electric field in a liquid cell between two electrodes and drives assembly of gold nanoparticles (NP) into two-dimensional clusters on electrode surfaces. Clusters are chemically crosslinked during assembly to freeze assemblies for electron microscopy characterization in order to understand how to ‘nucleate’ cluster formation. Electron microscopy images show deposition with a potential having an amplitude of 5 V and frequency of 100 Hz produces surfaces with isolated NP, which can seed EHD flow. A second deposition step at 5 V and 500 Hz produces a high density of quadramers on surfaces. When exciting near the local surface plasmon resonance of the Au NP clusters formed during assembly, Au NPs serve as in situ nanoantenna reporters of assembly and disassembly. Surface enhanced Raman scattering (SERS) measurements of Au NP capped with 4-mercaptobenzoic acid show order of magnitude signal enhancements occur during cluster formation in the presence of an oscillatory field, which occurs on a time scale of seconds. Confocal fluorescence spectroscopy is used to monitor the dissipative assembly of Au NP over multiple cycles. Results provide insight on how electrical stimuli and seeding local perturbations affects formation of NP clusters and resultant optical response provides insight on how to tune response of optically active surfaces.

A Novel Electrochemical Sensor for Chloramphenicol Based on MXenes and Carbon Nanofiber from Bacterial Cellulose

In this study, three-dimensional porous MXene/carbon nanofiber (CNF) nanocomposites were prepared by assembling environmentally friendly and inexpensive bacterial cellulose (BC) gel sheets as a carbon source with novel two-dimensional MXenes nanoplate clusters and pyrolyzing the composite. The structure, morphology, and electrochemical properties of MXene/CNF was characterized using scanning electron microscopy, transmission electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy, and electrochemical techniques. The experimental results revealed that the MXene/CNF nanocomposites had excellent electrical conductivity, large specific surface area, abundant active sites, and excellent electrochemical properties. The sensitive electrochemical determination of chloramphenicol (CAP) was achieved by constructing an electrochemical sensor using the MXene/CNF composite. The response current values of the MXene/CNF sensor exhibited a good linear response with CAP concentration ranging from 0.03-25μM, with a low detection limit of 9 nM. In addition, the sensor demonstrated good repeatability and reproducibility with relative standard deviations of 2.94 and 3.29%, respectively. Remarkably, the developed sensor was successfully used for the real-time detection of CAP in milk and jasmine tea beverage and satisfactory rates were obtained.

Fine-tuning the electronic properties of Au toward two-dimensional clusters with higher activity for ethanol conversion

Efficient Au/m-ZrO2 catalysts for the conversion of ethanol to acetaldehyde and ethyl acetate were obtained by the deposition precipitation method. The present work investigates the impact of the electronic properties of Au supports on m-ZrO2 catalysts on ethanol dehydrogenation. The Au4f7/2 binding energy (BE) and the average Au-Au nearest-neighbor distances (RAu-Au) were associated with surface defects characterized by CO adsorption. The BE and RAu-Au properties are correlated among themselves, changing by Au loading and pretreatments in an inert atmosphere at different temperatures. Although the RAu-Au unveils a boost of the reaction rates for shorter distances, the coupling of Au and m-ZrO2 produced a bifunctional catalyst. Theoretical studies indicate that molecular ethanol adsorption and formation of the ethoxy intermediate represented a feasible pathway for the activation of ethanol on the Au-ZrO2 interface. The RAu-Au toward of 2D clusters conferred high catalytic activity and low apparent activation energy for ethanol dehydrogenation.

Two-dimensional few-atom noble gas clusters in a graphene sandwich.

The van der Waals atomic solids of noble gases on metals at cryogenic temperatures were the first experimental examples of two-dimensional systems. Recently, such structures have also been created on surfaces under encapsulation by graphene, allowing studies at elevated temperatures through scanning tunnelling microscopy. However, for this technique, the encapsulation layer often obscures the arrangement of the noble gas atoms. Here we create Kr and Xe clusters in between two suspended graphene layers, and uncover their atomic structure through transmission electron microscopy. We show that small crystals (N < 9) arrange on the basis of the simple non-directional van der Waals interaction. Larger crystals show some deviations, possibly enabled by deformations in the encapsulating graphene lattice. We further discuss the dynamics of the clusters within the graphene sandwich, and show that although all the Xe clusters with up to N ≈ 100 remain solid, Kr clusters with already N ≈ 16 turn occasionally fluid under our experimental conditions (under a pressure of ~0.3 GPa). This study opens a way for the so-far unexplored frontier of encapsulated two-dimensional van der Waals solids with exciting possibilities for fundamental condensed-matter physics research and possible applications in quantum information technology.