Additive Manufacturing (AM) through Imprinting Gold Nanoparticles on Glass Substrates By Spark Assisted Chemical Engraving (SACE)
Bibliographic record
Abstract
Glass is used as material for Micro-Electro-Mechanical Systems (MEMS) in industry and academia. This is mainly because of its unique properties, like transparency and chemically inertness. Among the micromachining processes Spark Assisted Chemical Engraving (SACE) is a promising technology for fabricating smooth, 3D microstructures in glass [1]. In this novel machining technique, an electrochemical process heats a tool electrode which promotes local etching of a glass substrate [1]. Although glass drilling is developed to an industrial level, it is believed that the fundamentals of this technology can be exploited much further. Until today, only subtractive manufacturing capabilities of this technology are well investigated and used for applications. A major challenge is to extend the capabilities of SACE technology as additive manufacturing (AM) technique. This integration of micro- and nanotechnology in AM is an important and promising domain in emerging technologies. The most important issue on nanoscale AM is agglomeration of the nanomaterials in printing media [2]. Innovative alternatives to address these problems are highly demanded. This study employs the expanding of the capabilities of SACE by developing the ability to control the deposition of gold nanoparticles (GNPs) on a glass layer on a specific location (micromachined structure). The motivation of integrating AM and SACE process is to develop a novel manufacturing technology having more flexibility and enhanced control for fabrication of devices integrating nano- and microstructures such as biosensing devices like localized surface Plasmon resonators (LSPRs) [3]. In a first step, a methodology to deposit the GNPs is developed and in a second step the ability to control the quantity and geometrical arrangement of the nanoparticles on the glass substrate is aimed for. To achieve GNP deposition by SACE, the following methodology is proposed (see figure 1): 1) machining the desired structure by SACE, 2) decorating tool-electrode with the to be deposited particles, 3) moving decorated tool to deposition location, 4) bonding the particles to the substrate, 5) releasing particles from tool, 6) nanoparticles are deposited on desired location on glass substrate. For decorating the tool with the particles, GNPs are dispersed in an electrolytic solution in the presence of stabilizers and surfactants, which allows control of particle shape and colloidal stability. The tool is decorated by GNPs using the principle of electrophoresis, where particles are attracted to the tool by appropriate electrical fields. To achieve bonding of the metal particles to the glass, a recent finding in SACE is exploited [4]. Experimental results show the unique feature of SACE that a chemical bond can be formed, similar to anodic bonding, between the tool-electrode and glass substrate when an appropriate force and an appropriate temperature, typically around 300 degree Celsius, is applied [4]. Leaving the decorated tool-electrode several seconds pressed on the glass structure, the bonding phenomenon can be used for bonding the gold particles to the substrate. Subsequent, reversing the voltage between the two electrodes release the nanoparticles from the tool, keeping them on their deposited place. In this way, a controlled GNP pattern can be created in the machined structures on the sample at micro-scale level, which allow the creation of multiple ‘sampling spots’ on the same microfluidic system. This preliminary study shows the first steps for nanoparticle deposition on glass by SACE. [1] R.Wüthrich, William Andrew, Norwich, 2009 [2] O.Ivanova, C.Williams, T.Campbell, Rap.Protot.J., 19, 5, 2013, 353-364 [3] C. Escobedo, Lab Chip 13, 2013, 2445-2463 [4] J.D. Abou Ziki, R. Wüthrich, Preprint submitted to Manuf. Lett., 2014 Figure 1
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".