The Application of a Random Forest Classifier to ToF-SIMS Imaging Data DOI Creative Commons
Mariya A. Shamraeva, Theodoros Visvikis,

Stefanos Zoidis

и другие.

Опубликована: Авг. 1, 2024

Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) imaging is a potent analytical tool that provides spatially resolved chemical information of surfaces at the microscale. However, hyperspectral nature ToF-SIMS datasets constitutes can be challenging to analyze and interpret. Both supervised unsupervised Machine Learning (ML) approaches are increasingly useful help data. Random Forest (RF) has emerged as robust powerful algorithm for processing mass spectrometry This machine learning approach offers several advantages, including accommodating non-linear relationships, robustness outliers in data, managing high-dimensional feature space, mitigating risk overfitting. The application RF facilitates classification complex compositions identification features contributing these classifications. tutorial aims assist non-experts either or apply datasets.

Язык: Английский

A novel oxidation reagent scheme to realize efficient flotation separation of chalcopyrite and pyrrhotite DOI
Qifang Zheng, Liuyang Dong,

Peilun Shen

и другие.

Applied Surface Science, Год журнала: 2025, Номер unknown, С. 162794 - 162794

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

6
Proton Conducting Neuromorphic Materials and Devices DOI
Yifan Yuan, Ranjan Kumar Patel, Suvo Banik

и другие.

Chemical Reviews, Год журнала: 2024, Номер 124(16), С. 9733 - 9784

Опубликована: Июль 22, 2024

Neuromorphic computing and artificial intelligence hardware generally aims to emulate features found in biological neural circuit components enable the development of energy-efficient machines. In brain, ionic currents temporal concentration gradients control information flow storage. It is therefore interest examine materials devices for neuromorphic wherein electronic can propagate. Protons being mobile under an external electric field offers a compelling avenue facilitating functionalities synapses neurons. this review, we first highlight interesting analog protons as neurotransmitters various animals. We then discuss experimental approaches mechanisms proton doping classes inorganic organic proton-conducting advancement architectures. Since hydrogen among lightest elements, characterization solid matrix requires advanced techniques. review powerful synchrotron-based spectroscopic techniques characterizing well complementary scattering detect hydrogen. First-principles calculations are discussed they help provide understanding migration structure modification. Outstanding scientific challenges further our its use emerging electronics pointed out.

Язык: Английский

Процитировано

8
Insights from Quasi-in situ Cryogenic-Transfer Atom Probe Tomography for Analyzing Hydrogen Diffusion in Metallic Alloys DOI Creative Commons
Venkata Bhuvaneswari Vukkum, Zehao Li, V. Shutthanandan

и другие.

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Янв. 8, 2025

Abstract Cryogenic-transfer atom probe tomography (APT) has emerged as a powerful technique for nanoscale compositional analysis of hydrogen segregation in materials, offering critical insights into embrittlement mechanisms. However, accurate quantification concentration materials requires careful handling sample exposure during the cryogenic transfer-APT process. Therefore, we describe quantitative changes surface composition and oxygen an austenitic FeCrNi model alloy ultrahigh vacuum transfer using state-of-the-art LEAP 6000 XR APT, employing both deep UV laser-assisted voltage pulsed modes analysis. These were applied to interpret deuterium desorption from at room temperature after electrochemical deuterium-charging. The findings underscore importance managing throughout cryogenic-transfer APT process introduce novel quasi-in situ approach analyzing out-diffusion kinetics, which could be extended broader range metallic alloys.

Язык: Английский

Процитировано

0
Review on hydrogen embrittlement behavior of Ni-based superalloys DOI
Han Jiang, Yuchen Du, Shanquan Sun

и другие.

Ranqi wolun shiyan yu yanjiu., Год журнала: 2025, Номер unknown

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0
Tailoring the Physicochemical Properties of Nb Thin Films via Surface Engineering Methods DOI
Jeffrey A. Dhas, Ekta Bhatia, Krishna Prasad Koirala

и другие.

ACS Applied Materials & Interfaces, Год журнала: 2025, Номер unknown

Опубликована: Апрель 8, 2025

The modification of surface oxide layers formed on niobium (Nb) thin films via chemical mechanical planarization (CMP) and accelerated neutral atom beam (ANAB) processing provides a promising route toward tailoring their emergent properties performance when used as superconducting qubits. Here we show that CMP- ANAB-formed Nb oxides are significantly thinner smoother than the native oxide, revealed by transmission electron microscopy (TEM) atomic force microscopy. Scanning TEM energy-dispersive X-ray spectroscopy along with photoelectron identified an oxidation gradient within surface-engineered oxides. topside layer is dominated Nb5+ (Nb2O5), various suboxides present closer to oxide/metal interface. Time-of-flight secondary ion mass spectrometry (ToF-SIMS) depth profiling confirmed presence oxygen content demonstrated enhanced resistance exchange subsequent diffusion 18O2 isotopic labeling experiments. ToF-SIMS also interfacial containing trapped hydrogen (H)-containing species at In situ migration H/OH coinciding decomposition oxide. Furthermore, our density functional theory calculations indicated both H from moisture in ambient air bulk tend segregate These findings underscore importance understanding mechanisms, incorporation, impact designed functionalities Nb-based devices.

Язык: Английский

Процитировано

0
Nanospectroscopic Imaging of 2D Materials by Tip-Enhanced Raman Spectroscopy DOI
Zhendong Dai,

Qingqing Zhang,

Yang Zhao

и другие.

The Journal of Physical Chemistry C, Год журнала: 2025, Номер unknown

Опубликована: Апрель 15, 2025

Язык: Английский

Процитировано

0
Corrosion behavior of Mg72Zn27Pt1 alloy in Hanks’ solution: comparison between amorphous and crystalline structures DOI Creative Commons
J. Lelito, Aleksandra Pierwoła, H. Krawiec

и другие.

npj Materials Degradation, Год журнала: 2025, Номер 9(1)

Опубликована: Май 14, 2025

Язык: Английский

Процитировано

0
The Application of a Random Forest Classifier to ToF-SIMS Imaging Data DOI Creative Commons
Mariya A. Shamraeva, Theodoros Visvikis,

Stefanos Zoidis

и другие.

Journal of the American Society for Mass Spectrometry, Год журнала: 2024, Номер unknown

Опубликована: Окт. 25, 2024

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging is a potent analytical tool that provides spatially resolved chemical information on surfaces at the microscale. However, hyperspectral nature of ToF-SIMS datasets can be challenging to analyze and interpret. Both supervised unsupervised machine learning (ML) approaches are increasingly useful help data. Random Forest (RF) has emerged as robust powerful algorithm for processing This approach offers several advantages, including accommodating nonlinear relationships, robustness outliers in data, managing high-dimensional feature space, mitigating risk overfitting. The application RF facilitates classification complex compositions identification features contributing these classifications. tutorial aims assist nonexperts either or apply datasets.

Язык: Английский

Процитировано

2
The Application of a Random Forest Classifier to ToF-SIMS Imaging Data DOI Creative Commons
Mariya A. Shamraeva, Theodoros Visvikis,

Stefanos Zoidis

и другие.

Опубликована: Авг. 1, 2024

Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) imaging is a potent analytical tool that provides spatially resolved chemical information of surfaces at the microscale. However, hyperspectral nature ToF-SIMS datasets constitutes can be challenging to analyze and interpret. Both supervised unsupervised Machine Learning (ML) approaches are increasingly useful help data. Random Forest (RF) has emerged as robust powerful algorithm for processing mass spectrometry This machine learning approach offers several advantages, including accommodating non-linear relationships, robustness outliers in data, managing high-dimensional feature space, mitigating risk overfitting. The application RF facilitates classification complex compositions identification features contributing these classifications. tutorial aims assist non-experts either or apply datasets.

Язык: Английский

Процитировано

1