Graphene Hybrid Metasurfaces for Mid-Infrared Molecular Sensors

This research was funded by the ERDF PostDoctoral Research Project No. 1.1.1.2/VIAA/4/20/740 (Towards a Universal Lab-on-Chip Sensor from a Single Graphene Sheet: from Photodetection to Biosensing), EU CAMART2 project (European Union’s Horizon 2020 Framework Programme H2020-WIDESPREAD-01-2016-2017-TeamingPhase2 under grant agreement No. 739508) and Sweden’s innovation agency Vinnova (Large area CVD graphene-based sensors/IR-photodetectors 2020-00797). The APC was funded by the ERDF Project No. 1.1.1.2/VIAA/4/20/740.We integrated graphene with asymmetric metal metasurfaces and optimised the geometry dependent photoresponse towards optoelectronic molecular sensor devices. Through careful tuning and characterisation, combining finite-difference time-domain simulations, electron-beam lithography-based nanofabrication, and micro-Fourier transform infrared spectroscopy, we achieved precise control over the mid-infrared peak response wavelengths, transmittance, and reflectance. Our methods enabled simple, reproducible and targeted mid-infrared molecular sensing over a wide range of geometrical parameters. With ultimate minimization potential down to atomic thicknesses and a diverse range of complimentary nanomaterial combinations, we anticipate a high impact potential of these technologies for environmental monitoring, threat detection, and point of care diagnostics. © 2023 by the authors. --//-- Yager T., Chikvaidze G., Wang Q., Fu Y.; Graphene Hybrid Metasurfaces for Mid-Infrared Molecular Sensors; (2023) Nanomaterials, 13 (14), art. no. 2113; DOI: 10.3390/nano13142113. Published under the CC BY 4.0 licence.Sweden’s innovation agency Vinnova 2020-00797; ERDF PostDoctoral Research Project No. 1.1.1.2/VIAA/4/20/740; Institute of Solid State Physics, University of Latvia as the Center of Excellence has received funding from the European Union’s Horizon 2020 Framework Programme H2020-WIDESPREAD-01-2016-2017-TeamingPhase2 under grant agreement No. 739508, project CAMART2

Poly[diaqua­(μ2-5-carboxy­pyridine-3-carboxyl­ato-κ2 N:O 3)hemi(μ2-oxalato-κ4 O 1,O 2:O 1′,O 2′)(μ4-pyridine-3,5-dicarboxyl­ato-κ4 N:O 3:O 3′:O 5)silver(I)terbium(III)]

In the title coordination polymer, [AgTb(C7H3NO4)(C7H4NO4)(C2O4)0.5(H2O)2]n, the TbIII ion is eight-coordinated by three O atoms from three different pydc (H2pydc = pyridine-3,5-dicarboxylic acid) ligands, one O atom from one Hpydc ligand, two O atoms from one oxalate ligand and two water mol­ecules in a distorted square-anti­prismatic geometry. The AgI ion is coordinated in an almost linear fashion by two pyridyl N atoms from one pydc and one Hpydc ligand and has weak inter­actions with two carboxyl­ate O atoms. The carboxyl­ate groups of pydc and Hpydc ligands link Tb centers, forming a one-dimensional chain. The oxalate adopts a tetra­dentate bis-chelating coordination mode, connecting the chains into a two-dimensional layer. These layers are further assembled via [Ag(pydc)(Hpydc)] pillars and O—H⋯O and C—H⋯O hydrogen bonds into a three-dimensional coordination framework

4-Amino-3,5-dichloro­benzene­sulfonamide

In the title compound, C6H6Cl2N2O2S, the O atoms of the sulfonamide group lie on one side of the benzene ring and the amino group lies on the opposite side. An inter­molecular N—H⋯Cl inter­action occurs. In the crystal, adjacent mol­ecules are linked by N—H⋯O hydrogen bonds, forming a three-dimensional structure with supporting π–π stacking inter­actions [centroid–centroid distance = 3.7903 (12) Å]. A short Cl⋯Cl contact [3.3177 (10) Å] also occurs

WristSketcher: Creating Dynamic Sketches in AR with a Sensing Wristband

Restricted by the limited interaction area of native AR glasses (e.g., touch bars), it is challenging to create sketches in AR glasses. Recent works have attempted to use mobile devices (e.g., tablets) or mid-air bare-hand gestures to expand the interactive spaces and can work as the 2D/3D sketching input interfaces for AR glasses. Between them, mobile devices allow for accurate sketching but are often heavy to carry, while sketching with bare hands is zero-burden but can be inaccurate due to arm instability. In addition, mid-air bare-hand sketching can easily lead to social misunderstandings and its prolonged use can cause arm fatigue. As a new attempt, in this work, we present WristSketcher, a new AR system based on a flexible sensing wristband for creating 2D dynamic sketches, featuring an almost zero-burden authoring model for accurate and comfortable sketch creation in real-world scenarios. Specifically, we have streamlined the interaction space from the mid-air to the surface of a lightweight sensing wristband, and implemented AR sketching and associated interaction commands by developing a gesture recognition method based on the sensing pressure points on the wristband. The set of interactive gestures used by our WristSketcher is determined by a heuristic study on user preferences. Moreover, we endow our WristSketcher with the ability of animation creation, allowing it to create dynamic and expressive sketches. Experimental results demonstrate that our WristSketcher i) faithfully recognizes users' gesture interactions with a high accuracy of 96.0%; ii) achieves higher sketching accuracy than Freehand sketching; iii) achieves high user satisfaction in ease of use, usability and functionality; and iv) shows innovation potentials in art creation, memory aids, and entertainment applications

A new prognostic scale for the early prediction of ischemic stroke recovery mainly based on traditional Chinese medicine symptoms and NIHSS score: a retrospective cohort study

TCM symptoms & signs with appearance rate no less than 5 %. In practical analysis we selected 57 TCM symptoms with the appearance rate ≥5 % from 157 TCM symptoms& signs except tongue and pulse. (CSV 1 kb

Feature-level data fusion for energy consumption analytics in additive manufacturing

The issue of Additive Manufacturing (AM) energy consumption is attracting attention in both industry and academia, particularly with the trending adoption of AM technologies in the manufacturing industry. It is crucial to analyze, understand, and manage the energy consumption of AM for better efficiency and sustainability. The energy consumption of AM systems is related to various correlated attributes in different phases of an AM process. Existing studies focus mainly on analyzing the impacts of different processing and material attributes, while factors related to design and working environment have not received the same amount of attention. Such factors involve features with various dimensions and nested structures that are difficult to handle in the analysis. To tackle these issues, a feature-level data fusion approach is proposed to integrate heterogeneous data to build an AM energy consumption model to uncover energy-relevant information and knowledge. A case study using real-world data collected from a selective laser sintering (SLS) system is presented to validate the proposed approach, and the results indicate that the fusion strategy achieves better performances on energy consumption prediction than the individual ones. Based on the analysis of feature importance, the design-relevant features are found to have significant impacts on AM energy consumption

Deep fusion for energy consumption prediction in additive manufacturing

Owing to the increasing trend of additive manufacturing (AM) technologies being employed in the manufacturing industry, the issue of AM energy consumption attracts attention in both industry and academia. The energy consumption of AM systems is affected by various factors. These factors involve features with different dimensions and structures which are hard to tackle in the analysis. In this work, a data fusion approach is proposed for energy consumption prediction based on CNN-LSTM (convolutional neural network and long short-term memory) model. A case study was conducted on an SLS system by using the proposed methodology, achieving the RMSE of 8.143 Wh/g in prediction

Differential microRNA expression between shoots and rhizomes in Oryza longistaminata using high-throughput RNA sequencing

AbstractPlant microRNAs (miRNAs) play important roles in biological processes such as development and stress responses. Although the diverse functions of miRNAs in model organisms have been well studied, their function in wild rice is poorly understood. In this study, high-throughput small RNA sequencing was performed to characterize tissue-specific transcriptomes in Oryza longistaminata. A total of 603 miRNAs, 380 known rice miRNAs, 72 conserved plant miRNAs, and 151 predicted novel miRNAs were identified as being expressed in aerial shoots and rhizomes. Additionally, 99 and 79 miRNAs were expressed exclusively or differentially, respectively, in the two tissues, and 144 potential targets were predicted for the differentially expressed miRNAs in the rhizomes. Functional annotation of these targets suggested that transcription factors, including squamosa promoter binding proteins and auxin response factors, function in rhizome growth and development. The expression levels of several miRNAs and target genes in the rhizomes were quantified by RT-PCR, and the results indicated the existence of complex regulatory mechanisms between the miRNAs and their targets. Eight target cleavage sites were verified by RNA ligase-mediated rapid 5′ end amplification. These results provide valuable information on the composition, expression and function of miRNAs in O. longistaminata, and will aid in understanding the molecular mechanisms of rhizome development