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The exploration of new alloys with desirable properties has been a long-standing challenge in materials science because of the complex relationship between composition and microstructure. In this Research Article, we demonstrate a combinatorial strategy for the exploration of composition dependence of microstructure. This strategy is comprised of alloy library synthesis followed by high-throughput microstructure characterization. As an example, we synthesized a ternary Au-Cu-Si composition library containing over 1000 individual alloys using combinatorial sputtering. We subsequently melted and resolidified the entire library at controlled cooling rates. We used scanning optical microscopy and X-ray diffraction mapping to explore trends in phase formation and microstructural length scale with composition across the library. The integration of combinatorial synthesis with parallelizable analysis methods provides a efficient method for examining vast compositional ranges. The availability of microstructures from this vast composition space not only facilitates design of new alloys by controlling effects of composition on phase selection, phase sequence, length scale, and overall morphology, but also will be instrumental in understanding the complex process of microstructure formation in alloys.

Bulk metallic glasses synthesized at specialized facilities at Yale using magnetron cosputtering are sent to Southern Connecticut State University for elemental characterization. Characterization is done using a Zeiss Sigma VP SEM coupled with an Oxford EDS. Characterization is automated using control software provided by Oxford. Collected data is processed and visualized using computational methods developed internally. Processed data is then organized into a database suitable for web retrieval. This technique allows for the rapid characterization of a combinatorial wafer to be carried out in ~11 hours for a single wafer containing ~600 unique compounds. © 2015 World Scientific Publishing Company.

Combinatorial approaches comprised of combinatorial magnetron co-sputtering deposition and fast screening methods are introduced to study color as a function of composition in Au-based alloys. The microstructures of the thin films and bulk alloys are identified by X-ray diffraction, and their colors of the alloys are characterized by optical reflectivity. The results reveal that when comparing microstructures and reflectivity, thin films are similar to bulk alloys. In Au-Ag-Cu solid solutions, the color of the ternary alloy follows the rule of mixture. For colors resulting from AuAl2 intermetallic, the color of an alloy scales with the percentage of the intermetallic phase and the deviation from its ideal binary composition. In the Au-Al-Cu library, we found a ∼90 % AuAl2 area fraction compositional window where copper addition can be tuned to improve mechanical properties while keeping purple color, even though Al and CuAl2 phases exist. Moreover, when comparing the color in Au-Cu-Si-Ag amorphous and crystalline state solid solution for the same composition, the colors are essentially identical. © 2015 The Author(s).

A series of noble metal high entropy alloys with up to six constituent elements has been produced by casting. PtPdRhIrCuNi forms single-phase face-centered cubic solid solution, and its stability is confirmed by annealing experiments. This alloy deforms homogeneously to ~30% to a high ultimate compression strength of 1839MPa. We discuss rules for the formation of single-phase solid solution.

The implementation of electrochemical systems such as fuel cells has been hindered by the slow development of low cost high activity catalysts. Here we examine the oxygen reduction reaction performance of a combinatorial Pd-Au-Ag-Ti thin film library using high-throughput screening and correlate the electrochemical behavior to the crystallographic properties. We find compositions of ca. 40-60 at% Pd and 30-35 at% Au exhibit both a low overpotential of close to the value of pure Pt as well as high current density. We also observe a volcano-like relationship between the overpotential and the solid formation strain. This study provides compositional guidance towards the future synthesis of nanostructured quaternary Pd-Au-Ag-Ti alloys and suggests the potential for broader application of high-throughput electrochemical characterization by means of an automatic scanning droplet cell. © The Royal Society of Chemistry.

Metallic alloys are normally composed of multiple constituent elements in order to achieve integration of a plurality of properties required in technological applications. However, conventional alloy development paradigm, by sequential trial-and-error approach, requires completely unrelated strategies to optimize compositions out of a vast phase space, making alloy development time consuming and labor intensive. Here, we challenge the conventional paradigm by proposing a combinatorial strategy that enables parallel screening of a multitude of alloys. Utilizing a typical metallic glass forming alloy system Zr-Cu-Al-Ag as an example, we demonstrate how glass formation and antibacterial activity, two unrelated properties, can be simultaneously characterized and the optimal composition can be efficiently identified. We found that in the Zr-Cu-Al-Ag alloy system fully glassy phase can be obtained in a wide compositional range by co-sputtering, and antibacterial activity is strongly dependent on alloy compositions. Our results indicate that antibacterial activity is sensitive to Cu and Ag while essentially remains unchanged within a wide range of Zr and Al. The proposed strategy not only facilitates development of high-performing alloys, but also provides a tool to unveil the composition dependence of properties in a highly parallel fashion, which helps the development of new materials by design.

This chapter first reviews the research and applications of nonresonance and resonance Raman spectroscopy for analysis of human brain normal and abnormal tissues. Next, special emphasis is made on our recent achievements of visible resonance Raman (VRR) technique in primary human brain tumor disease investigation and diagnosis. Visible resonance Raman (VRR) spectroscopy technique uses excitation of visible light (532 nm) to evaluate the resonant and nonresonant molecular vibrational modes in biological tissues. The VRR signal intensities are enhanced by two to three orders of magnitude for faster use in medical applications in quasi real time. VRR opens up a new stainless “molecular optics based histopathology” diagnosis approach. © 2019 Elsevier Ltd. All rights reserved.

A new criterion was developed to characterize brain tissue using resonance Raman spectroscopy, by which, negative margins of cancer can be differentiated from normal tissues. This method may help a surgeon better decide surgical margins. © OSA 2017.

The unique advantage of visible resonance Raman (VRR) spectroscopy using 532 nm excitation wavelength for biological samples is the resonance enhancement of vibrational modes of chemical bonds from cells and tissues. The aim of this study is specifically to reveal the VRR characteristic spectra of different organs in mice, find the molecular alterations in the development of white matter and gray matter of mouse embryos at different ages and study the VRR spectral information of the mouse embryo head using VRR technology.

Convolutional neural network (CNN) based deep learning is used to analyze spectral data collected by visible resonance Raman (VRR) spectroscopy to distinguish human glioma tumors from healthy brain tissues using binary classification and identify the cancer grades of the glioma tumors using multi-class classification. Classification was performed using both raw spectral data and baseline-subtracted data for comparison. The classification using both datasets yielded high accuracy, with the results obtained from baseline subtracted spectra slightly better than that obtained from raw spectra. The study showed VRR combined with deep learning provides a robust molecular diagnostic tool for accurately distinguishing glioma tumors from normal tissues and glioma tumor tissues at different cancer grades. Deep learning aided VRR technique may be used for in-situ intraoperative diagnosis of brain cancer. It may help a surgeon to identify cancer margins and even cancer grades during surgery. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

Glioma is the most common brain neoplasm that features aggressive behavior with a dismal prognosis. Isocitrate dehydrogenase (IDH) gene mutation in glioma is an early genetic event in gliomagenesis that occurs in virtually every tumor cell and can cause profound metabolic changes. In this manuscript, we report for the first time the analysis of Raman optical signatures of IDH genotypes for human glioma using visible resonance Raman (VRR) spectroscopy. We demonstrated that VRR is a rapid, label-free, and objective method as an alternative to the existing methods for the rapid intraoperative determination of IDH mutation status with high accuracy. This study shows AI-assisted VRR has the potential to provide a new optical molecular biomarker and perform early diagnosis of glioma, which is of great importance for current guiding surgical strategies and even for targeting in situ therapies in the future. © 2026 Wiley-VCH GmbH.

There is still a lack of reliable intraoperative tools for glioma diagnosis and to guide the maximal safe resection of glioma. We report continuing work on the optical biopsy method to detect glioma grades and assess glioma boundaries intraoperatively using the VRR-LRRTM Raman analyzer, which is based on the visible resonance Raman spectroscopy (VRR) technique. A total of 2220 VRR spectra were collected during surgeries from 63 unprocessed fresh glioma tissues using the VRR-LRRTM Raman analyzer. After the VRR spectral analysis, we found differences in the native molecules in the fingerprint region and in the high-wavenumber region, and differences between normal (control) and different grades of glioma tissues. A principal component analysis–support vector machine (PCA-SVM) machine learning method was used to distinguish glioma tissues from normal tissues and different glioma grades. The accuracy in identifying glioma from normal tissue was over 80%, compared with the gold standard of histopathology reports of glioma. The VRR-LRRTM Raman analyzer may be a new label-free, real-time optical molecular pathology tool aiding in the intraoperative detection of glioma and identification of tumor boundaries, thus helping to guide maximal safe glioma removal and adjacent healthy tissue preservation.

Resonance Raman spectroscopy using 532nm excitation was used to distinguish normal brain tissue from different grades of glioma tissues. Principal component analysis was used to analyze the spectral data and achieved high accuracy. © 2018 The Author(s).

Machine learning algorithms were used to classify and analyze spectral data collected by visible resonance Raman spectroscopy to distinguish normal human brain tissue and glioma tumor tissues at different grades and show promising results. © OSA 2020 © 2020 The Author(s)

Recent reports pointed out that the application of optical spectroscopy in the field of liquid biopsy has aroused great interest among researchers and demonstrated the potential of its clinical application. We report a preliminary investigation on the visible resonance Raman (VRR) spectra of human brain blood liquid collected from the scalp and around the meningeal tumor during surgery and a set of venous blood samples from healthy people and glioma grade III patients using a portable VRR-LRRTM, HR800, HR-Evolution and WITec300 Raman systems in vivo and ex vivo. The biochemical fingerprints and molecular biomarkers were found. These findings indicate that if VRR spectroscopy technology is combined with polymerase chain reaction (PCR) or genetic molecular biomarker methods (VRR-PCR), it will greatly increase the possibility for its clinical application. © 2021 SPIE.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper. © 2018, The Author(s).

The Resonance Raman (RR) spectra of basal cell carcinoma (BCC) and normal human skin tissues were analyzed using 532nm laser excitation. RR spectral differences in vibrational fingerprints revealed skin normal and cancerous states tissues. The standard diagnosis criterion for BCC tissues are created by native RR biomarkers and its changes at peak intensity. The diagnostic algorithms for the classification of BCC and normal were generated based on SVM classifier and PCA statistical method. These statistical methods were used to analyze the RR spectral data collected from skin tissues, yielding a diagnostic sensitivity of 98.7% and specificity of 79% compared with pathological reports.