Efficient photon capture on germanium surfaces using industrially feasible nanostructure formation

Nanostructured surfaces are known to provide excellent optical properties for various photonics devices. Fabrication of such nanoscale structures to germanium (Ge) surfaces by metal assisted chemical etching (MACE) is, however, challenging as Ge surface is highly reactive resulting often in micron-level rather than nanoscale structures. Here we show that by properly controlling the process, it is possible to confine the chemical reaction only to the vicinity of the metal nanoparticles and obtain nanostructures also in Ge. Furthermore, it is shown that controlling the density of the nanoparticles, concentration of oxidizing and dissolving agents as well as the etching time plays a crucial role in successful nanostructure formation. We also discuss the impact of high mobility of charge carriers on the chemical reactions taking place on Ge surfaces. As a result we propose a simple one-step MACE process that results in nanoscale structures with less than 10% surface reflectance in the wavelength region between 400 nm and 1600 nm. The method consumes only a small amount of Ge and is thus industrially viable and also applicable to thin Ge layers.Comment: 8 pages, 4 figures. Full citation details and link to manuscript published in Nanotechnology were adde

In Situ Monitoring of Catalytic Molecular Transformations on Noble Metal Nanocatalysts Using Surface-Enhanced Raman Spectroscopy

Noble metal nanoparticles have long been of tremendous interest in the nanophotonics and nanocatalysis communities owing to their intriguing size- and shape-dependent plasmonic and catalytic properties. The combination of tunable plasmon resonances with superior catalytic activities on the same noble metal nanoparticle, however, has long been challenging because the research on nanoplasmonics and nanocatalysis deals with nanoparticles in two drastically different size regimes. While tunable plasmon resonances are a unique feature of metallic nanoparticles in the sub-wavelength size regime, heterogeneous catalysis requires the use of substrate-supported sub-5 nm nanoparticulate catalysts. In this mini-review article, we share with the readers several approaches we recently developed toward the realization of plasmonic-catalytic dual-functionalities on a single noble metal nanoparticle. Our approaches involve judicious tailoring of the atomic-level surface structures of sub-wavelength plasmonic nanoparticles through either kinetically controlled seed-mediated nanocrystal growth or regioselective surface etching. These structurally tailored, dual-functional nanoparticles serve as both substrates for surface-enhanced Raman spectroscopy (SERS) and free-standing nanoparticulate catalysts. Using SERS as a molecular finger-printing spectroscopic tool, we have been able to track detailed structural evolution of molecular adsorbates in real time during catalytic reactions. The quantitative insights gained from the in situ SERS measurements shed light on the detailed relationships between interfacial molecule-transforming behaviors and the atomic-level surface structures of noble metal nanocatalysts

Reliability of the 30-15 intermittent fitness test for elite wheelchair rugby players

Purpose: The purpose of this study was to investigate the test–retest reliability of a modified 30-15 intermittent fitness test (30-15) with elite wheelchair rugby (WR) players. Materials and methods: Ten elite WR players from the Australian Wheelchair Rugby team attended two testing sessions separated by a minimum of 48\ua0h. In each session, participants completed the 30-15. Participants’ final velocity (V), peak heart rate (HR), blood lactate ([La]) and rating of perceived exertion (RPE) were recorded. Results: There was high reliability for V (intraclass correlation coefficient [ICC]\ua0=\ua00.99, typical error of measurement [TE]\ua0=\ua01.02, coefficient of variation [CV]\ua0=\ua01.9%), HR (ICC\ua0=\ua00.95, TE\ua0=\ua01.05, CV\ua0=\ua04.5%), [La] (ICC\ua0=\ua00.98, TE\ua0=\ua01.05, CV\ua0=\ua05.5%) and RPE (ICC\ua0=\ua00.97, TE\ua0=\ua01.03, CV\ua0=\ua03.5%). The smallest worthwhile change was 0.2\ua0km·h which represents less than one level on the test. Significant agreement was found for V, HR and [La] outcome measures through 95% limits of agreement. Conclusions: The 30-15 was found to be a reliable test to monitor anaerobic, aerobic and change of direction performance in WR players and can be used to monitor athletes’ performance and determine the effectiveness of a training program

Black ultra-thin crystalline silicon wafers reach the 4n2 absorption limit–application to IBC solar cells

Cutting costs by progressively decreasing substrate thickness is a common theme in the crystalline silicon photovoltaic industry for the last decades, since drastically thinner wafers would significantly reduce the substrate-related costs. In addition to the technological challenges concerning wafering and handling of razor-thin flexible wafers, a major bottleneck is to maintain high absorption in those thin wafers. For the latter, advanced light-trapping techniques become of paramount importance. Here we demonstrate that by applying state-of-the-art black-Si nanotexture produced by DRIE on thin uncommitted wafers, the maximum theoretical absorption (Yablonovitch's 4n2 absorption limit), that is, ideal light trapping, is reached with wafer thicknesses as low as 40, 20, and 10 µm when paired with a back reflector. Due to the achieved promising optical properties the results are implemented into an actual thin interdigitated back contacted solar cell. The proof-of-concept cell, encapsulated in glass, achieved a 16.4% efficiency with an JSC = 35 mA cm-2, representing a 43% improvement in output power with respect to the reference polished cell. These results demonstrate the vast potential of black silicon nanotexture in future extremely-thin silicon photovoltaics.Peer ReviewedPostprint (published version

Optimize Individualized Energy Delivery for Septic Patients Using Predictive Deep Learning Models: A Real World Study

Background and Objectives: We aim to establish deep learning models to optimize the individualized energy delivery for septic patients. Methods and Study Design: We conducted a study of adult septic patients in Intensive Care Unit (ICU), collecting 47 indicators for 14 days. After data cleaning and preprocessing, we used stats to explore energy delivery in deceased and surviving patients. We filtered out nutrition-related features and divided the data into three metabolic phases: acute early, acute late, and rehabilitation. Models were built using data before September 2020 and validated on the rest. We then established optimal energy target models for each phase using deep learning. Results: A total of 277 patients and 3115 data were included in this study. The models indicated that the optimal energy targets in the three phases were 900kcal/d, 2300kcal/d, and 2000kcal/d, respectively. Excessive energy intake increased mortality rapidly in the early period of the acute phase. Insufficient energy in the late period of the acute phase significantly raised the mortality of septic patients. For the rehabilitation phase, too much or too little energy delivery both associated with high mortality. Conclusion: Our study established time-series prediction models for septic patients to optimize energy delivery in the ICU. This approach indicated the feasibility of developing nutritional tools for critically ill patients. We recommended permissive underfeeding only in the early acute phase. Later, increased energy intake may improve survival and settle energy debts caused by underfeeding

Metabolomics in the Development and Progression of Dementia: A Systematic Review

Dementia has become a major global public health challenge with a heavy economic burden. It is urgently necessary to understand dementia pathogenesis and to identify biomarkers predicting risk of dementia in the preclinical stage for prevention, monitoring, and treatment. Metabolomics provides a novel approach for the identification of biomarkers of dementia. This systematic review aimed to examine and summarize recent retrospective cohort human studies assessing circulating metabolite markers, detected using high-throughput metabolomics, in the context of disease progression to dementia, including incident mild cognitive impairment, all-cause dementia, and cognitive decline. We systematically searched the PubMed, Embase, and Cochrane databases for retrospective cohort human studies assessing associations between blood (plasma or serum) metabolomics profile and cognitive decline and risk of dementia from inception through October 15, 2018. We identified 16 studies reporting circulating metabolites and risk of dementia, and six regarding cognitive performance change. Concentrations of several blood metabolites, including lipids (higher phosphatidylcholines, sphingomyelins, and lysophophatidylcholine, and lower docosahexaenoic acid and high-density lipoprotein subfractions), amino acids (lower branched-chain amino acids, creatinine, and taurine, and higher glutamate, glutamine, and anthranilic acid), and steroids were associated with cognitive decline and the incidence or progression of dementia. Circulating metabolites appear to be associated with the risk of dementia. Metabolomics could be a promising tool in dementia biomarker discovery. However, standardization and consensus guidelines for study design and analytical techniques require future development

Decreasing Interface Defect Densities via Silicon Oxide Passivation at Temperatures Below 450 degrees C

Low-temperature (LT) passivation methods (700 degrees C). Therefore, the LT passivation of SiOx/Si has long been a research topic to improve application performance. Here, we demonstrate that an LT (<450 degrees C) ultrahigh-vacuum (UHV) treatment is a potential method that can be combined with current state-of-the-art processes in a scalable way, to decrease the defect densities at the SiOx/Si interfaces. The studied LT-UHV approach includes a combination of wet chemistry followed by UHV-based heating and preoxidation of silicon surfaces. The controlled oxidation during the LT-UHV treatment is found to provide an until now unreported crystalline Si oxide phase. This crystalline SiOx phase can explain the observed decrease in the defect density by half. Furthermore, the LT-UHV treatment can be applied in a complementary, post-treatment way to ready components to decrease electrical losses. The LT-UHV treatment has been found to decrease the detector leakage current by a factor of 2

Effects of post oxidation of SiO2/Si interfaces in ultrahigh vacuum below 450 °C

Growing SiO2 layer by wet-chemical oxidation of Si surfaces before growth of another insulating film(s) is a used method to passivate Si interfaces in applications (e.g., solar cell, photodiode) at low temperatures (LT) below 450 °C. We report on potential of LT ultrahigh-vacuum (UHV) treatments combined with the wet-chemical oxidation, by investigating effects of LT-UHV oxidation after the wet-chemical growth of SiO2 and before growing Al2O3 film on top of SiO2/Si. This method modifies the SiO2/Si and is found to (i) decrease defect-level density, (ii) increase negative fixed charge density, and (iii) increase carrier lifetime for Al2O3/SiO2/p-Si, as compared to state-of-the-art SiO2/p-Si reference interfaces without LT-UHV. X-ray photoelectron spectroscopy shows that the LT-UHV treatment decreases amount of Si+3 oxidized atoms in chemically grown SiO2 and also amount of carbon contamination. In order to pave the way for further tests of LT-UHV in silicon technology, we present a design of simple UHV instrument. The above-described benefits are reproduced for 4-inch silicon wafers by means of the instrument, which is further utilized to make LT-UHV treatments for complementary SiO2/Si interfaces of the native oxide at silicon diode sidewalls to decrease the reverse bias leakage current of the diodes.​​​​​​​</ul

Interfacial Ligand Transformation on Plasmonic Nanostructures: Mechanistic Insights Unraveled by Plasmon-Enhanced Spectroscopy

We employ surface-enhanced Raman scattering (SERS) as an in situ spectroscopic tool, in conjunction with density functional theory (DFT) calculations, to develop a detailed mechanistic understanding of plasmon-driven photocatalytic reactions and heterogeneous transfer hydrogenolysis reactions of molecular adsorbates on metallic nanostructure surfaces. The excitation and decay of plasmon resonances in metallic nanostructures give rise to intriguing photophysical and photochemical effects, including the generation of hot charge carriers, local field enhancements, and photothermal transduction. These effects can be intentionally harnessed to drive or enhance interfacial molecular transformations along unconventional reaction pathways. The first part of the thesis focuses on the plasmon-driven photocatalytic dimerization of aniline and nitrobenzene derivatives, elucidating the relationship between the chemical nature of adsorbate-metal interactions and reaction reactivities. Additionally, we utilize the dimerization reaction of para-nitrothiolphenyl as a model system to assess the relative contributions of photothermal and nonthermal effects. As a highly sensitive vibrational spectroscopic technique, SERS provides fingerprints of molecular structures, offering valuable chemical and structural information. With sufficient temporal resolution, SERS serves as an ideal tool to identify reaction intermediates and resolve the reaction kinetics of heterogeneous catalytic reactions. Similarly, to the first part, we investigate the impact of adsorbate-metal interactions on chemical reactions. However, instead of plasmon-driven photocatalytic reactions, we focus on studying the metal-surface catalyzed transfer hydrogenolysis of nitrobenzene derivatives. We further demonstrate the feasibility of plasmonically enhancing the reaction, leading to an increased reaction rate and shortened induction time