High Peak Research Articles

Beam-shaping in laser-based powder bed fusion of metals: A computational analysis of point-ring intensity profiles

Laser-based powder bed fusion has the potential to enable manufacturing of local material properties and locally varying alloys. One promising approach under research is shaping of the applied laser beam. The current commercial standard using a Gaussian laser beam profile is associated with a high energy intensity peak and strong gradients. A potential alternative beam shape is the point-ring profile where laser energy is distributed between a central spot and an outer ring. We conduct a numerical study of beam shape impact on melt-pool shapes, surface temperatures, evaporation and flow dynamics of single-melt-tracks. To that end, we apply a Weakly Compressible Smoothed Particle Hydrodynamics solver coupled with a ray-tracing laser scheme. A point-ring profile is compared to a standard Gaussian profile. The profiles are studied at two different beam sizes to find the influence of beam size on the shape effect. We find good agreement between numerical results and experimental data for both narrow and deep, as well as wide and shallow melt-tracks. The simulation results show that beam size and thus energy density influence the effect beam-shaping has on the melt-pool dynamics. Both the distribution of evaporation and surface tension forces over the melt-pool surface are found to depend on the used beam shape. The study highlights the importance of distinguishing between size and shape effects when investigating laser beam shapes. Both energy density and its distribution need to be tuned for the desired melt-pool behavior.

Deep learning for ultrasound surface echo detection

Ultrasound array imaging techniques are often applied using a coupling medium to transmit waves from the transducer to the scanned component. In this type of tests, the echoes generated by the wave reflection at the component surface contain information that can be used to estimate the surface shape and its position relative to the probe, which are needed to compute the imaging focal laws. The most interesting feature to extract from surfaces echoes is the Time of Flight (TOF) for each reception channel. TOF measurement in acoustic signals is a problem that frequently arises not only in Non Destructive Testing (NDT), but also in medical imaging, geophysics and other fields. The surface echo in a signal is usually identified as the first high amplitude peak, and is traditionally detected with standard methods as threshold crossing, peak search or matched filters. All these methods are easily affected by Signal to Noise Ratio, which might be high in some channels, for example if the ray arrives at the corresponding element at a low sensibility angle. Moreover, spurious echoes from bubbles, the emission signal tail or echoes from other parts of the component can be incorrectly labelled as surface echoes. This kind of outliers are difficult to filter by conventional means and, thus, there is a need for more robust surface estimation methods. In this work, we study the application of a deep 3D Convolutional Neural Network (CNN) to detect surface echoes in Full Matrix Capture (FMC) ultrasound data. The CNN is trained using signals acquired with a matrix array and a reference component, using a robotic arm set-up to measure the probe position and orientation relative to the component. Thus, with knowledge of the set-up geometry, a model is used to compute the theoretical surface echo TOFs, which are used to label the data for training. The model was tested with a validation set, presenting good agreement with the ground truth. The results were compared with the threshold crossing method observing a significant reduction of outliers in the TOF estimation.

Experimental research on the flow flied of the nacelle and inlet of a civil aircraft with wind-mounted engines at low speed

Abstract Modern civil transport aircraft with wing-mounted engines are exposed to the upwash induced by the wing, resulting in the local angle of attack at the nacelle leading edge, notably greater than that of aircraft. Large local angles of attack can be combined with different engine airflow conditions, causing adversary aerodynamic phenomena to occur at different locations. Total and static pressures obtained in a wind tunnel test using a full-span aircraft model and simulating required engine airflow with an ejector indicate that during the around with landing configuration, the high suction peak will form on the bottom of the inlet with local air flow being supersonic and could lead to inlet distortion. In contrast, the engine that fails during takeoff climb will have a significant suction peak form on the top of the nacelle and might experience external separation and additional drag. Test results also demonstrate that well-designed nacelles can postpone these adversary phenomena until the aircraft stalls first.

Experimental research of bubble pulsation and jet under a double plate with circular hole

The pulsation and jet of underwater explosion bubble heavily affected by the boundary conditions. To explore the pulsation behavior and jet load of underwater explosion bubble near a damaged double structure, some double plates with circular hole were designed. The electric spark-generated bubble was employed to act as the underwater explosion bubble, and the experiment research on bubble pulsation and jet below the double plate with circular hole was carried out. The bubble pulsation and jet process were recorded by a high-speed camera, and the bubble jet load was measured by a wall pressure sensor. The results show that with the growth of the standoff distance, the bubble jet changes from the type I “gas-liquid two-phase jet” to the type II “gas-liquid two-phase jet”, and finally into the water jet. The load of the type I “gas-liquid two-phase jet” are characterized with high peak pressure with short duration in the initial pressure stage, and low pressure with long duration in the standing pressure stage. Whereas the type II “gas-liquid two-phase jet” feature high peak pressure with short duration in the initial pressure stage without standing pressure stage.

Production and characterization of low-density silicon nitride reinforced zinc nanocomposite coatings on mild steel for applications in marine and automotive industries

In today's automotive, marine and petrochemical industries, the desire for lightweight materials has increased. Hence, necessitating the production of components with low density. In this work, lightweight Zn–Si3N4 coatings were developed by including Si3N4 in the zinc matrix. The optimal coatings were produced on steel samples at 45 °C and varied Si3N4 particles and voltages following ASTM A53/A53M standard. The deterioration (corrosion) property i.e. corrosion rate (CR) and current density (jocorr) of the uncoated (control) and coated samples were examined in 0.5 M of sulphuric acid using a potentiodynamic polarization technique following ASTM G3/G102 standard. The microstructure of the samples was studied via the SEM micrographs and XRD patterns, while the wear performance resistance (following ASTM G99 standard) and electrical conductivity of the samples were examined with a pin-on-disc tribometer and ammeter-voltmeter. The corrosion experiment indicated that the uncoated mild steel specimen possessed a CR of 12.345 mm year−1 and jocorr of 1060 μA/cm2, while the CR and jcorr of the coated samples ranged from 2.6793 to 4.7975 mm year−1 and 231−413 μA/cm2, respectively. The lower CR and jcorr values of the coated specimens, relative to the coated sample showed that the coatings possessed superior passivation ability in the test medium. The SEM micrographs of the samples showed refined morphology, while the XRD patterns revealed high peak intensity crystals such as Zn4SiN, ZnNSi, Zn4N and Zn2NSi, which could be beneficial to the mechanical properties and corrosion resistance of the steel. Moreover, the wear resistance study indicated that the COF of the uncoated sample ranged from 0.1 to 0.5, while those for coated specimens ranged from 0.05 to 0.35. Similarly, the uncoated steel exhibited a wear volume (WV) of 0.00508 mm3, while the WV of the coated specimens ranged from 0.00266 to 0.0028 mm3, indicating the existence of high strengthening mechanisms between the interface of the protecting device and the steel. Also, the electrical conductivity of the mild steel sample reduced from 12.97 Ω−1cm−1 to 0.64 Ω−1cm−1, indicating that the electrical resistivity of the steel was enhanced by the coatings.

High-performance negative differential resistance characteristics in homoepitaxial AlN/GaN double-barrier resonant tunneling diodes

In this work, high-performance negative differential resistance (NDR) characteristics are demonstrated in homoepitaxial AlN/GaN double-barrier resonant tunneling diodes (RTDs). The devices are grown by plasma-assisted MBE on bulk GaN substrates and exhibit robust and repeatable NDR at RT. A high peak current density of 183 kA cm−2 is simultaneously demonstrated with a large peak-to-valley current ratio of 2.07, mainly benefiting from the significantly reduced dislocation density and improved hyper-abrupt heterointerfaces in the active region, which boosts the electron quantum transport in the resonant tunneling cavity. The achievement shows the promising potential to enhance the oscillation frequency and output power of GaN-based RTD oscillators, imperative for next-generation high-power solid-state compact terahertz oscillators application.

Non-Gaussianity consistency relations and their consequences for the peaks

Strong deviations from scale invariance and the appearance of high peaks in the primordial power spectrum have been extensively studied for generating primordial black holes (PBHs) or gravitational waves (GWs). It is also well-known that the effect of non-linearities can be significant in both phenomena. In this paper, we advocate the existence of a general single-field consistency relation that relates the amplitude of non-Gaussianity in the squeezed limit f NL to the power spectrum and remains valid when almost all other consistency relations are violated. In particular, it is suitable for studying scenarios where scale invariance is strongly violated.We discuss the general and model-independent consequences of the consistency relation on the behavior of f NL at different scales.Specifically, we study the size, sign and slope of f NL at the scales where the power spectrum peaks and argue that generally the peaks of f NL and the power spectrum occur at different scales.As an implication of our results, we argue that non-linearities can shift or extend the range of scales responsible for the production of PBHs or GWs, relative to the window as determined by the largest peak of the power spectrum, and may also open up new windows for both phenomena.

Symmetrical vortices and laminar dust flow induced by an intense electron beam interacting with a strongly coupled dusty plasma

A strongly coupled quasi-two-dimensional dusty plasma confined electrostatically in the plasma sheath of a radio frequency (RF) plasma is irradiated by a collimated and mono-energetic pulsed electron beam (e-beam) with an energy of 13 keV and a high peak current per pulse of 30 mA. A stream of rapidly moving charged dust particles is created inside the dust crystal due to the drag force of the electrons in the e-beam. The dust flow is split into two symmetrical branches when it reaches the boundary of the round dust crystal, each following the limit of the circular confining region. This results in the formation of a double vortex flow pattern with the dust particles being transported along the irradiation direction and then aside, eventually back to the entrance position of the e-beam. The observed flow regime is laminar at all times, with the speed in the central region increasing up to 12 mm s−1 in the first 200 ms and then diminishing gradually to a steady value of about 5–6 mm s−1 during a stress relaxation time period of 360 ms. The vorticity follows a similar trend with peak values −3.8 and 3.8 s−1 and steady state values between −2.5 and 2.5 s−1 in the two symmetrical vortices. Time-resolved particle-image-velocimetry and particle-tracking-velocimetry are used to characterize the flow. Molecular dynamics simulations confirm qualitatively the experimental observations showing dust stream and double vortex formation.

Physico-chemical characterization of cupola slag: Enhancing its utility in construction

Cupola slag is a waste material of the steel and iron industries. Its composition is determined by the cupola furnace and other elements used in steel and iron manufacturing. This paper investigates the characterization behavior of various cupola slag materials. As a result, x-ray fluorescence (XRF), x-ray diffraction (XRD), thermogravimetry differential thermal analysis, and scanning electron microscopy (SEM) methods were used to characterize three cupola slag samples from distinct origins. In addition, various physical properties were used to compare different cupola slags. The specific gravity values of CS-1 (cupola slag-1 sample), CS-2 (cupola slag-2 sample), and CS-3 (cupola slag-3 sample) are 1.36, 2.5, and 2.917, respectively. The density and water absorption for CS-1, CS-2, and CS-3 are 1414.86, 1477.71, and 1796 kg/m3, and 0.37%, 0.32%, and 0.26%, respectively. Cupola slag also includes a larger percentage of lime, according to XRF data, which contributes to its improved binding characteristics. A higher calcium oxide content in CS-3 could facilitate the pozzolanic process. The presence of angular particles that aid in material binding is seen in the SEM image. Compounds with a nanostructure are then flawlessly blended into the mixture and grouped with calcium alumina silicates formed by cement hydration. The XRD pattern of cupola slag exhibits high peaks, indicating that the material is crystalline in character and can be utilized as sand. It also shows the presence of other chemical compounds, such as silica, which ranges from 30% to 45%. CS-1 and CS-2 have comparable XRD patterns. However, CS-3 has a somewhat different pattern because of the greater CaO content. Weight loss begins at higher temperatures, which shows that the material is stable at higher temperatures, according to a thermo-gravimetric study. The differential thermal analysis curve of CS-3 indicates that the material remains stable up to a temperature of 600 °C. The physical characteristics of all cupola slag samples show that cupola slag may be utilized to make sustainable building materials because of its lower specific gravity, density, and water absorption.

Nanofiltration membranes in asymmetric flow field-flow fractionation for improved organic matter size fractionation

Size fractionation of organic matter (OM) by asymmetric flow field-flow fractionation (FFFF) with ultrafiltration (UF) is limited by solute loss and peak resolution, particularly for low molecular weight fractions (< 10 kDa). Nanofiltration (NF) with a molecular weight cut-off (MWCO) of ≤ 1 kDa can achieve significant OM retention and may improve OM fractionation. NF membranes with MWCOs ranging from 0.3 to 3 kDa were used to evaluate the retention and fractionation of nine OMs in the humic, polyphenol, biopolymer, and low molecular weight organic classes in stirred cell NF and FFFF. Membranes with an MWCO of 0.2–0.3 kDa (with a pure water permeability of 5–16 L.m-2.h-1.bar-1) with low OM interaction exhibited high peak recovery and resolution and hence improved OM fractionation. Loose NF (MWCO 3 kDa) exhibited a poor peak recovery because of a high OM loss due to both adhesion and permeation, particularly at high ionic strengths (> 10 mM). A mixture of nine types of OM and natural organic matter samples from rivers, swamps and lakes achieved good fractionation with a 0.3 kDa (NF270) membrane at low ionic strength. The performance of NF for the analysis of OM and colloids in the smallest size range (≤ 1 kDa MW) could be further improved by optimizing the salt composition of the mobile phase and by overcoming the pressure limitations of FFFF channels.

Cortical Acetylcholine Response to Deep Brain Stimulation of the Basal Forebrain.

Deep brain stimulation (DBS), the direct electrical stimulation of neuronal tissue in the basal forebrain to enhance release of the neurotransmitter acetylcholine, is under consideration as a method to improve executive function in patients with dementia. While some small studies indicate a positive response in the clinical setting, the relationship between DBS and acetylcholine pharmacokinetics is incompletely understood. We examined the cortical acetylcholine response to different stimulation parameters of the basal forebrain. 2-photon imaging was combined with deep brain stimulation. Stimulating electrodes were implanted in the subpallidal basal forebrain, and the ipsilateral somatosensory cortex was imaged. Acetylcholine activity was determined using the GRABACh-3.0 muscarinic acetylcholine receptor sensor, and blood vessels were imaged with Texas red. Experiments manipulating pulse train frequency demonstrated that integrated acetylcholine induced fluorescence was insensitive to frequency, and that peak levels were achieved with frequencies from 60 to 130 Hz. Altering pulse train length indicated that longer stimulation resulted in higher peaks and more activation with sublinear summation. The acetylcholinesterase inhibitor donepezil increased the peak response to 10s of stimulation at 60Hz, and the integrated response increased 57% with the 2 mg/kg dose, and 126% with the 4 mg/kg dose. Acetylcholine levels returned to baseline with a time constant of 14 to 18 seconds in all experiments. These data demonstrate that acetylcholine receptor activation is insensitive to frequency between 60 and 130 Hz. High peak responses are achieved with up to 900 pulses. Donepezil increases total acetylcholine receptor activation associated with DBS but did not change temporal kinetics. The long time constants observed in the cerebral cortex add to the evidence supporting volume in addition to synaptic transmission.

Wavelet-based Filter Bank Approach for Image Compression and Enhancement in Alzheimer\'s Disease Diagnosis Using Medical Imaging

Abstract: This research presents a wavelet-based filter bank approach for image compression and enhancement in the context of Alzheimer's disease diagnosis using medical imaging. The proposed system leverages the PyWavelets and OpenCV libraries in Python to implement Discrete Wavelet Transform (DWT) and Inverse Discrete Wavelet Transform (IDWT) algorithms. The primary objective is to develop an efficient image compression technique while preserving visual quality and enhancing relevant features for improved diagnostic accuracy. The system utilizes filter banks to process wavelet coefficients, enabling selective enhancement of patterns or biomarkers associated with Alzheimer's disease. Results show that the decompressed images closely resemble the originals, with low Mean Squared Error (MSE) and high Peak Signal-to-Noise Ratio (PSNR) values. Visual inspection and quantitative metrics confirm the successful preservation of image quality and structural details. The wavelet coefficients visualization highlights the captured significant features crucial for reconstruction

Electronic coupling effect of multi-metallic heterostructure to enhance oxygen evolution reaction for quasi-solid-state Zn-air batteries

It remains an urgent task to design electrocatalysts with fast oxygen evolution reaction (OER) kinetics and enriched active sites for flexible Zn-air batteries. In this paper, a structure engineering strategy is proposed to design an electrocatalyst with a heterostructure comprising Co@NC and NiMo-LDH on carbon cloth, which serves as an air electrode for a quasi-solid-state Zn-air battery. This unique heterostructure significantly enhances electrocatalytic activity, producing low OER overpotentials of 198 mV at 10 mA cm-2. Moreover, the assembled rechargeable Zn-air battery exhibits a high peak density and long cycle stability. Density functional theory calculations indicate that the multimetallic heterostructure induced synergistic and electronic coupling effects among the metal active sites, facilitating rapid ion and electron transport during the OER process. In addition, the migration of oxygen species at the Ni and Mo sites reduces the reaction potential barrier, resulting in a low OER overpotential of 160 mV.

Ultrahigh-response flexible photothermoelectric photodetectors based on a graded Bi2Te3-carbon nanotube hybrid

Photothermoelectric (PTE) detectors can work in a broad band range without using a cooling unit and external bias and therefore have attracted intense research interest. However, the performance of PTE detectors constructed using traditional TE materials has been limited by their poor optical absorption and intrinsic brittleness. We report a flexible graded TE material comprised of Bi2Te3 nanocrystals adsorbed on a single-wall carbon nanotube (SWCNT) network, which has excellent flexibility, high photo-thermal conversion ability and good TE properties. Flexible PTE detectors were constructed, and had an ultrahigh responsivity of 0.71 V/W, a high peak detectivity up to 2.1 × 108 Jones, and a broad response band ranging from the infrared to the terahertz band. The excellent PTE performance of the detectors is attributed to the broadband optical absorption of SWCNTs, the high TE performance of Bi2Te3 nanocrystals, and the graded layer structure of the hybrid.

Electrode-based floating treatment wetlands: Insights into design operation factors influencing bioenergy generation and treatment performance

Exponential increases in energy consumption and wastewater have often irreversible environmental impacts. As a result, bio-electrochemical devices like microbial fuel cells (MFCs), which convert chemical energy in organic matter to electricity using exoelectrogenic bacteria, have gained interest. However, operational factors affecting efficiency and energy output need further study. This research investigated bioenergy production and COD, TN, and TP removal in mesoscale floating treatment wetlands (FTW-MFC) using Phragmites australis, Iris pseudacorus, and a mix of both. The Iris FTW-MFC achieved a high voltage peak of 2100 mV. The maximum power densities of 484 mW/m2, 1196 mW/m2, and 441 mW/m2 were observed for Phragmites, Iris, and mixed FTW-MFCs, respectively. Despite promising bioenergy yields, pollutant removal was unsatisfactory. A low area/height ratio (0.38 m2/0.8 m) and high loading rate (18.1 g/m2·d COD) boosted bioenergy output but hindered treatment performance and stressed plants, causing root decay. No significant pollutant removal differences were found between FTW-MFC and FTW. Higher relative plant growth rates occurred in the FTW-MFC. Microbial analysis shown that representatives of Pseudomonas and Clostridium species were consistently found across all samples, involved in both organic compound transformation and electricity generation, contributed to successful microscale results. A supporting microscale MFC experiment showed wastewater composition's impact on bioenergy yield and pollutant removal. Pre-inoculated reactors improved organic matter transformation and electricity generation, while aeration increased voltage and treatment performance. The role of plants requires further verification in future experiments.

Molecular mechanisms promoting long-term cytopenia after BCMA CAR-T therapy in multiple myeloma

AbstractHematologic toxicity is a common side effect of chimeric antigen receptor T-cell (CAR-T) therapies, being particularly severe among patients with relapsed or refractory multiple myeloma (MM). In this study, we characterized 48 patients treated with B-cell maturation antigen (BCMA) CAR-T cells to understand kinetics of cytopenia, identify predictive factors, and determine potential mechanisms underlying these toxicities. We observed that overall incidence of cytopenia was 95.7%, and grade >3 thrombocytopenia and neutropenia, 1 month after infusion, was observed in 57% and 53% of the patients, respectively, being still present after 1 year in 4 and 3 patients, respectively. Baseline cytopenia and high peak inflammatory markers were highly correlated with cytopenia that persisted up to 3 months. To determine potential mechanisms underlying cytopenias, we evaluated the paracrine effect of BCMA CAR-T cells on hematopoietic stem and progenitor cell (HSPC) differentiation using an ex vivo myeloid differentiation model. Phenotypic analysis showed that supernatants from activated CAR-T cells (spCAR) halted HSPC differentiation, promoting more immature phenotypes, which could be prevented with a combination of interferon γ, tumor necrosis factor α/β, transforming growth factor β, interleukin-6 (IL-6) and IL-17 inhibitors. Single-cell RNA sequencing demonstrated upregulation of transcription factors associated with early stages of hematopoietic differentiation in the presence of spCAR (GATA2, RUNX1, CEBPA) and a decrease in the activity of key regulons involved in neutrophil and monocytic maturation (ID2 and MAFB). These results suggest that CAR-T activation induces HSPC maturation arrest through paracrine effects and provides potential treatments to mitigate the severity of this toxicity.

Comparative analysis of hyperspectral Image reconstruction using deep learning for agricultural and biological applications

Hyperspectral imaging (HSI) has become a key technology for non-invasive quality evaluation in various fields, offering detailed insights through spatial and spectral data. Despite its efficacy, the complexity and high cost of HSI systems have hindered their widespread adoption. This study addressed these challenges by exploring deep learning-based hyperspectral image reconstruction from RGB (Red, Green, Blue) images, particularly for agricultural products. Specifically, different hyperspectral reconstruction algorithms, such as Hyperspectral Convolutional Neural Network - Dense (HSCNN-D), High-Resolution Network (HRNET), and Multi-Scale Transformer Plus Plus (MST++), were compared to assess the dry matter content of sweet potatoes. Among the tested reconstruction methods, HRNET demonstrated superior performance, achieving the lowest mean relative absolute error (MRAE) of 0.07, root mean square error (RMSE) of 0.03, and the highest peak signal-to-noise ratio (PSNR) of 32.28 decibels (dB). Some key features were selected using the genetic algorithm (GA), and their importance was interpreted using explainable artificial intelligence (XAI). Partial least squares regression (PLSR) models were developed using the RGB, reconstructed, and ground truth (GT) data. The visual and spectra quality of these reconstructed methods was compared with GT data, and predicted maps were generated. The results revealed the prospect of deep learning-based hyperspectral image reconstruction as a cost-effective and efficient quality assessment tool for agricultural and biological applications.

Bridgman growth and characterizations of nonlinear optical crystal La3Ga5.5Nb0.5O14

La3Ga5.5Nb0.5O14 (LGN) crystal is a promising material for optical parametric chirped-pulse amplification (OPCPA) with high laser damage threshold and wide transmission range which is able to achieve high peak power laser. LGN with a diameter of 25 mm was successfully grown by the vertical Bridgman method for the first time and a series of characterizations were performed. The 1.7 µm absorption peak in transmittance spectra of LGN crystal which was caused by the Czochralski technique can be effectively eliminated by the vertical Bridgman method. LGN crystal possesses a high transmittance of 81.3 % in the range of 0.294 ∼ 7.500 μm. The experimental optical band gap of LGN is 4.16 eV which is positively correlated with laser damage threshold. The experimental laser damage threshold of LGN was determined to 1.416 GW/cm2 by Nd:YAG. The outstanding thermal properties of LGN indicate that it possesses thermal stability in high energy laser field. This work demonstrates that LGN crystal holds great potential for OPCPA applications and the vertical Bridgman method is proved to be a effective and excellent technique to grow high quality LGN crystal.

Exploring single rare earth doped nanophosphor for efficient white emission through codoping of Na+ and Bi3+ ions into Dy3+ activated LaPO4

Using the simple hydrothermal technique, high-purity nanocrystalline Na+ and Bi3+ co-doped Dy3+ activated LaPO4 was effectively prepared. The structural characterisation of the prepared samples was carried out using XRD, EDS, SEM and FTIR analysis. The existence of Na+, Dy3+, La3+, P5+, O2– and Bi3+ was demonstrated using EDS analysis. The direct band gap value of 4.72 eV is obtained from UV visible absorption spectrum and infer that the doped samples are suitable for optical applications. The photoluminescence excitation spectrum monitored at 474 nm and 570 nm emission, the Na+ and Bi3+ co-doped Dy3+ activated LaPO4 exhibit narrow, and intense characteristics f-f peak of Dy3+ ion at 349 nm, 363 nm, 386 nm and 452 nm. Photoluminescence emission spectrum, monitored under 349 nm, 363 nm and 386 nm shows high intense sharp blue emission peaks of Dy3+ with stark splitting at 475 nm, 478 nm, and 486 nm, yellow emission at 571 nm and 580 nm and a very feeble peak at 658 nm in the red region. When the sample is excited at 452 nm only a yellow band is present. Since the intensity of blue and yellow emission in this host matrix is so high and nearly equal, it is appropriate for cool white emission under 350 nm, 363 nm and 386 nm (near UV) excitation and under 452 nm (blue LED) excitation the prepared phosphor may emerge as highly efficient warm white light emitter suitable for general solid-state lighting applications, which can be further confirmed by CIE chromaticity analysis of the prepared sample. In photoluminescence excitation and emission spectra intensities of peaks of the samples with Bi3+ are more intense, this confirms the positive impact of co-doping of Bi3+ into the sample. The Q value from the Dexter’s formula is calculated as an average of 6.4 and is very close to 6 indicates that the interaction is dipole − dipole, which causes the non-radiative energy migration among the Dy3+ ions in the sample. The lifetime of characteristic yellow emission of Dy3+ in the Na+ and Bi3+ co-doped Dy3+ activated LaPO4 host matrix monitored under 350 nm is calculated as 0.642 ms from decay analysis, fitted by a bi-exponential function. The results support the idea that co-doping of Bi3+ and Na+ into the LaPO4:Dy3+ matrix could emerge as a potential white light emitting phosphor for phosphor-converted white LED application.

Modeling a game to shift peak water demands: WaterTime leaderboard

Residential water demands vary with a diurnal pattern, and peak hour demands lead to inefficiencies in the operation and management of urban water distribution systems. Peak hourly demands generate significant costs due to the energy requirements of producing and pumping large volumes of potable water. High peak demands also require large investments in infrastructure expansion to support urban growth and economic development. This research develops an agent-based model to assess a demand-side management strategy that shifts high hourly demands. This research envisions a gamification system that rewards players through a leaderboard for shifting water end uses for laundry, irrigation, and bathing from on-peak to off-peak periods. Gamification of demand shifting behaviors is enabled through advanced metering infrastructure, which measures hourly or sub-hourly demands at the account level, and can be analyzed to assess water use behaviors. This research develops an Agent-based Modeling approach based on the Opinion Dynamics theory to calculate the changing opinion of household agents toward shifting water demands, based on utility broadcasts, observations of neighbor water use, social media, and participation in a leaderboard. The model is applied for eight months of hourly demand data collected through smart meters at a participating utility. Results show that a high level of participation in the leaderboard increases the adoption rate of customers shifting the time of specific end uses and may account for up to a 37 % reduction of the peak volume. The diurnal curve is flattened as consumers shift water use from on-peak to off-peak hours. The Agent-Based Model - Opinion Dynamics approach can assist water utilities in designing and evaluating gamification approaches that mitigate peak demands to support water infrastructure management.