Physical properties of gold electrodeposits and their effect on thickness measurement

Hemsley, S.

doi:10.1007/bf03214738

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…691. Thickness of coatings was roughly calculated from sheet weight before and after electrodeposition and assuming that gold density is 19.3 g cm À3 , but some authors [12][13][14] noticed that density of electrodeposited gold is lower than theoretical. Faradic efficiency was calculated as a ratio between the charge useful for Au deposition and the total charge.…”

Section: Methodsmentioning

confidence: 99%

Electroformed objects for jewelry: secondary ion mass spectrometry characterization of Au films from CN-free electrolytes

Fabrizio

Piccirillo

Daolio

1998

Rapid Commun. Mass Spectrom.

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CN-free electrolytic baths were studied in order to deposit thick Au films on different cathodic substrates. Secondary ion mass spectrometry characterization of electrodeposited gold for jewelry is presented, analyzing surface composition and element distribution along the thickness. It was verified that additives used to improve the deposits are included in the coatings during the electrolysis. Cathodic substrates utilized for the electrodeposition were detected on the surface spectra, suggesting intermetallic diffusion along the deposits. #

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Section: Methodsmentioning

confidence: 99%

Electroformed objects for jewelry: secondary ion mass spectrometry characterization of Au films from CN-free electrolytes

Fabrizio

Piccirillo

Daolio

1998

Rapid Commun. Mass Spectrom.

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“…Among those metals, gold is mostly preferred since it is the most chemically stable when exposed to the atmosphere. However, gold does not show SPR when the wavelength used is less than 0.5 µm [61] and [62].…”

Section: Optical Sensors Technologiesmentioning

confidence: 97%

Deep Learning for Optical Sensor Applications: An Insight Review

Al‐Ashwal¹,

Soufy²,

Hamza³

et al. 2023

Preprint

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Over the past decade, Deep Learning (DL) had been applied in a large number of optical sensors’ applications. DL algorithms can improve the accuracy and reduce the noise level in optical sensors. Optical sensors are considered as promise technology for the modern intelligent sensing platforms. These sensors are widely used to process monitoring, quality prediction, pollution, defense, security and many other applications. However, they suffer major challenges such as the large generated data and low processing speed for that data and moreover the much cost of these sensor. These challenges can be mitigated by integrating deep learning system with the optical sensor technologies. This paper presents recent studies that integrate DL algorithms with optical sensors applications. This paper also highlights several of DL algorithms directions that promise a considerable impact on use for optical sensor applications. Moreover, this study provides new directions for the future related research development.

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“…Among these metals, gold is mostly preferred since it is the most chemically stable when exposed to the atmosphere. However, gold does not show SPR when the wavelength used is less than 0.5

m [ 61 , 62 ].…”

Section: Optical Sensor Technologiesmentioning

confidence: 99%

Deep Learning for Optical Sensor Applications: A Review

Al‐Ashwal

Soufy

Hamza

et al. 2023

Sensors

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Over the past decade, deep learning (DL) has been applied in a large number of optical sensors applications. DL algorithms can improve the accuracy and reduce the noise level in optical sensors. Optical sensors are considered as a promising technology for modern intelligent sensing platforms. These sensors are widely used in process monitoring, quality prediction, pollution, defence, security, and many other applications. However, they suffer major challenges such as the large generated datasets and low processing speeds for these data, including the high cost of these sensors. These challenges can be mitigated by integrating DL systems with optical sensor technologies. This paper presents recent studies integrating DL algorithms with optical sensor applications. This paper also highlights several directions for DL algorithms that promise a considerable impact on use for optical sensor applications. Moreover, this study provides new directions for the future development of related research.

show abstract

Physical properties of gold electrodeposits and their effect on thickness measurement

Cited by 4 publications

References 9 publications

Electroformed objects for jewelry: secondary ion mass spectrometry characterization of Au films from CN-free electrolytes

Electroformed objects for jewelry: secondary ion mass spectrometry characterization of Au films from CN-free electrolytes

Deep Learning for Optical Sensor Applications: An Insight Review

Deep Learning for Optical Sensor Applications: A Review

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