Sensing of clinically relevant biomolecules such as neurotransmitters at low concentrations can enable an early detection and treatment of a range of diseases. Several nanostructures are being explored by researchers to detect biomolecules at sensitivities beyond the picomolar range. It is recognized, however, that nanostructuring of surfaces alone is not sufficient to enhance sensor sensitivities down to the femtomolar level. In this paper, we break this barrier/limit by introducing a sensing platform that uses a multi-length-scale electrode architecture consisting of 3D printed silver micropillars decorated with graphene nanoflakes and use it to demonstrate the detection of dopamine at a limit-of-detection of 500 attomoles. The graphene provides a high surface area at nanoscale, while micropillar array accelerates the interaction of diffusing analyte molecules with the electrode at low concentrations. The hierarchical electrode architecture introduced in this work opens the possibility of detecting biomolecules at ultralow concentrations.
Surface segregation is a phenomenon common to all multicomponent materials and one that plays a critical role in determining their surface properties. Comprehensive studies of surface segregation versus bulk composition in ternary alloys have been prohibitive because of the need to study many different compositions. In this work, high-throughput low-energy He+ ion-scattering spectra and energy-dispersive X-ray spectra were collected from a Cu x Au y Pd1–x–y composition spread alloy film under ultrahigh vacuum conditions. These have been used to quantify surface segregation across the entire Cu x Au y Pd1–x–y composition space (x = 0 → 1 and y = 0 → 1 – x). Surface compositions at 164 different bulk compositions were measured at 500 and 600 K. At both temperatures, Au shows the greatest tendency for segregation to the top-most surface while Pd is always depleted from the surface. Higher temperatures enhance the Au segregation. Segregation at most of the binary alloy bulk compositions matches with observations previously reported in the literature. However, surface compositions in the CuPd B2 composition region reveal segregation profiles that are nonmonotonic in bulk alloy composition. These were not observable in prior studies because of their limited resolution of composition space. An extended Langmuir–MacLean model, which describes ternary alloy segregation, has been used to analyze experimental data from the ternary alloys and to estimate pair-wise segregation free energies and segregation equilibrium constants. The ability to study surface segregation across the ternary alloy composition space with high-throughput methods has been validated, and the impact of bulk alloy phase on surface segregation is demonstrated and discussed.
Simulation of the segregation profile of multicomponent alloys is important to investigate the catalytic properties of alloy catalysts. Density functional theory (DFT) is too expensive to use directly to evaluate the potential energies of the slab configurations during the simulations. In this work, we build a neural network (NN) based on 5278 DFT calculations as a surrogate model to evaluate the potential energies of the fcc(111) slabs for a ternary Cu–Pd–Au alloy. The trained NN is capable of predicting the Cu–Pd–Au potential energies across the whole ternary space with high accuracy. Combining the NN with Monte Carlo simulation, we obtained the segregation profile of Cu–Pd–Au at 600 K across the bulk composition space. The simulation results are qualitatively consistent with the experimental data for PdAu and CuAu, but they are incorrect along the PdCu line. Further DFT calculations show that the perfect fcc(111) slab is not capable of capturing the CuPd segregation behavior on undercoordinated surfaces under the realistic conditions.
Platinum group metal-free (PGM-free) catalysts present a promising opportunity to make hydrogen fuel cells more affordable; however, issues with stability and electrochemical activity continue to hinder their application. Recent studies point to the availability of nitrogen and a controlled mesoporous structure as avenues of improvement. To address this need, copolymer-templated nitrogen-enriched carbon (CTNC) was used as the precursor to prepare PGM-free catalysts for the oxygen reduction reaction (ORR). By employing its rich nitrogen content and interconnected mesoporous structure, a significant amount of Fe–N–C-active sites were formed by co-annealing CTNC with FeSO4. The formed N/Fe co-doped nanocarbon (CTNC-Fe) catalyst exhibits good electrochemical activity with a half-wave potential of 0.781 V vs normal hydrogen electrode (NHE), a total surface area of 400 m2/g, and a power density of 180 mW/cm2. The block copolymer (BCP) was made by atom transfer radical polymerization (ATRP), which allows control and tunability of the block copolymer. This work opens new opportunities to improve the electrochemical activity and stability of PGM-free catalysts by controlled polymerization techniques that precisely tune the pore structure and maximize nitrogen content to improve the formation of active sites in the catalysts.
To identify superior thermal contacts to graphene, we implement a high-throughput methodology that systematically explores the Ni–Pd alloy composition spectrum and the effect of Cr adhesion layer thickness on thermal interface conductance with monolayer graphene. Frequency domain thermoreflectance measurements of two independently prepared Ni–Pd/Cr/graphene/SiO2 samples identify a maximum metal/graphene/SiO2 junction thermal interface conductance of 114 ± (39, 25) MW/m2 K and 113 ± (33, 22) MW/m2 K at ∼10 at. % Pd in Ninearly double the highest reported value for pure metals and 3 times that of pure Ni or Pd. The presence of Cr, at any thickness, suppresses this maximum. Although the origin of the peak is unresolved, we find that it correlates with a region of the Ni–Pd phase diagram that exhibits a miscibility gap. Cross-sectional imaging by high-resolution transmission electron microscopy identifies striations in the alloy at this particular composition, consistent with separation into multiple phases. Through this work, we draw attention to alloys in the search for better contacts to two-dimensional materials for next-generation devices.
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