Three-dimensional platinum nanoparticle-based bridges for ammonia gas sensing

Abstract

This study demonstrates the fabrication of self-aligning three-dimensional (3D) platinum bridges for ammonia gas sensing using gas-phase electrodeposition. This deposition scheme can guide charged nanoparticles to predetermined locations on a surface with sub-micrometer resolution. A shutter-free deposition is possible, preventing the use of additional steps for lift-off and improving material yield. This method uses a spark discharge-based platinum nanoparticle source in combination with sequentially biased surface electrodes and charged photoresist patterns on a glass substrate. In this way, the parallel growth of multiple sensing nodes, in this case 3D self-aligning nanoparticle-based bridges, is accomplished. An array containing 360 locally grown bridges made out of 5 nm platinum nanoparticles is fabricated. The high surface-to-volume ratio of the 3D bridge morphology enables fast response and room temperature operated sensing capabilities. The bridges are preconditioned for ~ 24 h in nitrogen gas before being used for performance testing, ensuring drift-free sensor performance. In this study, platinum bridges are demonstrated to detect ammonia (NH₃) with concentrations between 1400 and 100 ppm. The sensing mechanism, response times, cross-sensitivity, selectivity, and sensor stability are discussed. The device showed a sensor response of ~ 4% at 100 ppm NH₃ with a 70% response time of 8 min at room temperature.

Figure 1

Gas-phase electrodeposition procedure schematics, bridge growth process, and nanobridge gas sensor test setup details. (a) Programmable gas-phase electrodeposition being carried out in a PMMA based reactor with a cylindrical cross-section. A spark discharge between platinum electrodes produces charged nanoparticles which are collected on a patterned substrate. During a particular time instant, platinum nanoparticles (green) are synthesized and collected on the biased domain pair until a bridge forms; all other domain pairs have a floating potential. This process is repeated 6 times to produce an array of Pt nanoparticle-based bridges on a single chip. (b) SEM images of a single bridge formation.

Figure 2

Schematic illustration of the sensor measurement set-up. Ammonia gas is diluted with Nitrogen 6.0 to concentrations of up to 100 ppm. During gas flow through the sensing chamber, the subsequent change in the electrical resistance of a bridge for a given externally applied voltage is recorded. The real-time performance is plotted on a computer with the help of a LabVIEW program.

Figure 3

Nanobridge-based sensor array with multiple sensing nodes—fabrication overview. (a) A patterned photoresist with 2 µm openings is aligned to independent gold domain pairs. A negative bias of − 125 V is applied to the gold domains and 360 bridges are deposited in parallel. (b) While the openings are places in the x–y plane, the bridges grow out of the openings in the z-direction as shown in the SEM image with a 5-degree tilt. Such sensing nodes grown adjacent to each other cover the entire sensing area of the chip. (c) A single sensing bridge with its nanoparticulate sensing surface. All bridges are connected in parallel to the external circuit providing a bias voltage of 1.5 V.

Figure 4

Particle size distribution and material characterization studies. At 6 slm gas flow through the spark discharge, primary Pt particles are collected on a Copper TEM grid and observed under a STEM Microscope. A total number of 75 particles are studied for this statistics. (a) Pt nanoparticles form a log-normal distribution with an average particle size of 5.3 nm. (b) Nanomaterial is deposited onto Si (111) substrates to perform XPS measurements using a surface analytics chamber with a monochromated Al Kα source. Pt 4f and Pt 4d XPS peaks (b₁) are calibrated with adventitious carbon C–C bonds at 284.8 eV as shown in (b₂).

Figure 5

Gas sensor preconditioning and Joule heating simulations. Gas-sensitive bridges need to be placed in a constant flow of background gas for ~ 24 h to stabilize the gas sensor response. (a₁) Pt sensing bridges decrease their overall bridge resistance during the preconditioning cycle. After this step, the bridge resistance is stable over time and hence is expected to reach stationary state with ambient conditions of temperature and gas species. If the NH₃ response test starts without preconditioning, we obtain a sensor drift as shown in (a₂), which decreased by over 80% after stabilization in (a₃). (b) COMSOL Multiphysics simulations of the temperature of Pt nanoparticle-based bridges due to Joule heating at an external bias of 1 V, 1.5 V, and 2 V in 1 slm N₂ flow are shown. It is assumed that the array of bridges (b₁) is composed out of solid Pt nanowires with a 0.3 µm cross-section which is represented by a black polygon in (b₂) where the temperature profile is shown as a heat map for an external bias of 1.5 V. This temperature profile is plotted as a function of position (white dashed line corresponds to the z-axis) in (b₃). The nanowire temperatures are higher than the room temperatures which is the assumed reason for the decrease in bridge resistance during the pre-conditioning step.

Figure 6

Gas sensor results. Bridges of Pt are employed for sensing varying concentrations of ammonia gas. Each NH₃ concentration is exposed to the bridges for 30 min, and then they recover for 180 min in background of 6.0 N₂ gas with no humidity. In (a) cycles of NH₃ in N₂ gas is introduced in gas sensor setup. In (b) 70% response times are plotted for different NH₃ concentrations. Long-time sensor exposure to NH₃ is undertaken to attain saturation value of resistance increase; the time taken to reach 70% of this saturation value is termed as T₇₀ response time. (c) Normalized gas response of Pt bridges to various gases is plotted to show the extent of sensor cross sensitivity/selectivity. (d) Effect of background gas without humidity (N₂ and air) is studied by observing the NH₃ sensor response cycle. The gas sensor response is shown to improve owing to additional oxygen species helping in the NH₃ adsorption and thus the sensing mechanism.