Phase I final Report: Scalable and Broad-Spectrum Photodetectors based on Heterogeneous Integration for VIS and NIR Applications
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43 PARTICLE ACCELERATORS↗
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The DEHVILS in the details: Type Ia supernova Hubble residual comparisons and mass step analysis in the near-infrared
Measurements of type Ia supernovae (SNe Ia) in the near-infrared (NIR) have been used both as an alternate path to cosmology compared to optical measurements and as a method of constraining key systematics for the larger optical studies. With the DEHVILS sample, the largest published NIR sample with consistent NIR coverage of maximum light across three NIR bands ( Y, J , and H ), we check three key systematics: (i) the reduction in Hubble residual scatter as compared to the optical, (ii) the measurement of a “mass step” or lack thereof and its implications, and (iii) the ability to distinguish between various dust models by analyzing slopes and correlations between Hubble residuals in the NIR and optical. We produce SN Ia simulations of the DEHVILS sample and find that it is harder to differentiate between various dust models than previously understood. Additionally, we find that fitting with the current SALT3-NIR model does not yield accurate wavelength-dependent stretch-luminosity correlations, and we propose a limited solution for this problem. From the data, we see that (i) the standard deviation of Hubble residual values from NIR bands treated as standard candles are 0.007–0.042 mag smaller than those in the optical, (ii) the NIR mass step is not constrainable with the current sample size of 47 SNe Ia from DEHVILS, and (iii) Hubble residuals in the NIR and optical are correlated in the data. We test a few variations on the number and combinations of filters and data samples, and we observe that none of our findings or conclusions are significantly impacted by these modifications.
Astronomy & Astrophysics↗
Heterovalent Substitution of K 2 SrP 2 O 7 :Cr 3+ to Achieve Anti-Thermal-Quenching Broadband Near-Infrared Luminescence
Broadband near-infrared (NIR) light sources based on phosphor-converted light-emitting diodes are highly desirable for biochemical analysis and medical diagnosis applications. However, thermal quenching remains a demanding challenge for developing efficient NIR phosphors. Herein, we report the enhancement of both quantum efficiency and thermal stability in Cr 3+ -activated K 2 SrP 2 O 7 phosphors through a heterovalent substitution strategy by replacing Sr 2+ with Al 3+ in K 2 Sr 1–x Al x P 2 O 7 (0.05 ≤ x ≤ 0.2) to obtain optimized broadband NIR emission. Structural modulation via Al 3+ substitution leads to the optimized composition, K 2 Sr 0.88 Al 0.1 P 2 O 7 :0.02Cr 3+ , which emits across a broad NIR range of 650–1100 nm peaking at 807 nm with a full width at half-maximum of ∼130 nm under 448 nm excitation. Remarkably, its emission intensity at 150 °C remains 120% of the initial value at room temperature, demonstrating a rare antithermal-quenching behavior. Temperature-dependent XRD studies further reveal that Al 3+ substitution effectively suppresses lattice expansion at elevated temperatures, indicating enhanced lattice stability under thermal excitation. Detailed structural and spectral analyses show that the substitution enhances local site symmetry, reduces electron–phonon coupling, increases thermally induced absorption probability, and fortifies energetic barriers against nonradiative transitions. These synergistic effects collectively endow this NIR phosphor with a superior thermal stability. Furthermore, NIR light-emitting diodes fabricated with this phosphor exhibit strong potential for applications in information identification, nondestructive detection, and night vision technologies. This study demonstrates a local structure engineering strategy for designing thermally robust Cr 3+ -activated NIR phosphors, offering valuable insights into material discovery and NIR spectroscopy device development.
Cr3+↗
Extrapolation of Type Ia Supernova Spectra into the Near-infrared Using Principal Component Analysis
Abstract We present a method of extrapolating the spectroscopic behavior of Type Ia supernovae (SNe Ia) in the near-infrared (NIR) wavelength regime up to 2.30 μ m using optical spectroscopy. Such a process is useful for accurately estimating K-corrections and other photometric quantities of SNe Ia in the NIR. A principal component analysis is performed on data consisting of Carnegie Supernova Project I & II optical and NIR FIRE spectra to produce models capable of making these extrapolations. This method differs from previous spectral template methods by not parameterizing models strictly by photometric light-curve properties of SNe Ia, allowing for more flexibility of the resulting extrapolated NIR flux. A difference of around −3.1% to −2.7% in the total integrated NIR flux between these extrapolations and the observations is seen here for most test cases including Branch core-normal and shallow-silicon subtypes. However, larger deviations from the observation are found for other tests, likely due to the limited high-velocity and broad-line SNe Ia in the training sample. Maximum-light principal components are shown to allow for spectroscopic predictions of the color-stretch light-curve parameter, s BV , within approximately ±0.1 units of the value measured with photometry. We also show these results compare well with NIR templates, although in most cases the templates are marginally more fitting to observations, illustrating a need for more concurrent optical+NIR spectroscopic observations to truly understand the diversity of SNe Ia in the NIR.
Astronomy & Astrophysics↗
Self-supervised and multi-fidelity learning for extended predictive soil spectroscopy
Infrared spectroscopy is a cost-effective, non-destructive, and environmentally benign technology that is increasingly recognized as an important solution for meeting the global demand for soil data. While both near-infrared (NIR) and mid-infrared (MIR) diffuse reflectance spectroscopy enable rapid estimation of soil properties, they present a significant trade-off: NIR offers superior scalability and lower operational costs, whereas MIR provides higher analytical fidelity by capturing fundamental molecular vibrations. In this study, we propose a self-supervised, multi-fidelity learning framework designed to bridge this gap. Our approach leverages large-scale MIR spectral libraries to learn a compact, transferable latent representation, into which NIR spectra are subsequently aligned for downstream prediction. The workflow consists of pretraining a latent model on a large MIR library, adapting the representation using a smaller paired NIR–MIR dataset, and evaluating generalization on an independent external test set. Across a range of chemical and physical soil properties, we found that MIR-derived embeddings improved prediction accuracy relative to baseline models that used raw MIR inputs. Predictions derived from the spectrum conversion (NIR to MIR) task did not match the performance of the original MIR spectra but were similar or superior to predictive performance of NIR-only models, suggesting the unified spectral latent space can effectively leverage the larger and more diverse MIR dataset for prediction of soil properties not well represented in current NIR libraries.
54 ENVIRONMENTAL SCIENCES↗
Neuromorphic intermediate representation: A unified instruction set for interoperable brain-inspired computing
Abstract Spiking neural networks and neuromorphic hardware platforms that simulate neuronal dynamics are getting wide attention and are being applied to many relevant problems using Machine Learning. Despite a well-established mathematical foundation for neural dynamics, there exists numerous software and hardware solutions and stacks whose variability makes it difficult to reproduce findings. Here, we establish a common reference frame for computations in digital neuromorphic systems, titled Neuromorphic Intermediate Representation (NIR). NIR defines a set of computational and composable model primitives as hybrid systems combining continuous-time dynamics and discrete events. By abstracting away assumptions around discretization and hardware constraints, NIR faithfully captures the computational model, while bridging differences between the evaluated implementation and the underlying mathematical formalism. NIR supports an unprecedented number of neuromorphic systems, which we demonstrate by reproducing three spiking neural network models of different complexity across 7 neuromorphic simulators and 4 digital hardware platforms. NIR decouples the development of neuromorphic hardware and software, enabling interoperability between platforms and improving accessibility to multiple neuromorphic technologies. We believe that NIR is a key next step in brain-inspired hardware-software co-evolution, enabling research towards the implementation of energy efficient computational principles of nervous systems. NIR is available atneuroir.org
Science & Technology - Other Topics↗
Rapid monitoring of fermentations: a feasibility study on biological 2,3-butanediol production
2,3-butanediol (2,3-BDO) is an economically important platform chemical that can be produced by the fermentation of sugars using an engineered strain of Zymomonas mobilis . These fermentations require continuous monitoring and modification of fermentation conditions to maximize 2,3-BDO yields and minimize the production of the undesired coproducts glycerol and acetoin. Because of the time required for sampling and off-line chromatographic measurement of fermentation samples, the ability of fermentation scientists to modify fermentation conditions in a timely manner is limited. The goal of this study was to test if near-infrared spectroscopy (NIRS) along with multivariate statistics could reduce the time needed for this analysis and enable real-time monitoring and control of the fermentation. In this work we developed partial least squares (PLS) calibration models to predict the concentrations of glucose, xylose, 2,3-BDO, acetoin, and glycerol in fermentations via NIRS using two different spectrometers and two different spectroscopy modalities. We first evaluated the feasibility of rapid NIRS monitoring through experiments where we measured the signals from each analyte of interest and built NIRS-based PLS models using spectra from synthetic samples containing uncorrelated concentrations of these analytes. All analytes showed unique spectral signatures, and this initial modeling showed that all analytes could be detected simultaneously. We then began work with samples from laboratory fermentation experiments and tested the feasibility of regression model development across two spectral collection modalities (at-line and on-line) and two instruments: a laboratory-grade instrument and a low-cost instrument with a more limited spectral range. All modalities showed promise in the ability to monitor Z. mobilis fermentations of glucose and xylose to 2,3-BDO. The low-cost instrument displayed a lower signal-to-noise ratio than the laboratory-grade instrument, which led to comparatively lower performance overall, but still provided sufficient accuracy to monitor fermentation trends. While the ease of use of on-line monitoring systems was favored as compared to at-line systems due to the lack of sampling required and potential for automated process control, we observed some decrease in performance due to the additional complexity of the sample matrix. We have demonstrated that NIRS combined with multivariate analysis can be used for at-line and on-line monitoring of the concentrations of glucose, xylose, 2,3-BDO, acetoin, and glycerol during Z. mobilis fermentations. The decrease in signal-to-noise ratio when using a low-cost spectrometer led to greater prediction error than the laboratory-grade spectrometer for at-line monitoring. The on-line monitoring modality showed great promise for real time process control via NIRS.
09 BIOMASS FUELS↗
Sensitivity of Simulated Polar Climate to Improved Partitioning of Spectral Solar Irradiance Between Visible and Near‐Infrared Bands
The solar radiative processes that contribute to Earth's surface and atmospheric energy budgets are strongly dependent on wavelength. For example, snow and water vapor become more absorptive as incident radiation shifts from visible (VIS) to near-infrared (NIR) wavelengths. Some earth system models (ESMs) aggregate solar radiation into just two bands (VIS and NIR) partitioned at 0.7 μm for transmission between the atmospheric and surface components. In the widely used radiative transfer model RRTMG_SW this partition is near the center of the overlap spectral band spanning 0.625–0.778 μm, whose flux is often approximated as being evenly divided between the VIS and NIR surface bands. Using a hyperspectral radiative transfer model, we show that the fractional downwelling surface flux within the overlap band is usually distributed about 55.5%:44.5% VIS:NIR. This improved approximation shifts as much as 4.27 W m −2 from the NIR to the VIS band, leading to an instantaneous decrease in surface absorption of up to 0.91 W m −2 over snow-covered surfaces. Century-long fully coupled ESM simulations show surface absorption over snow decreases by over 1.6 W m −2 . The coupled response in sea ice regions amplifies the initial forcing ten-fold, and increases seasonal sea ice area by up to 4.9%. These results highlight the importance of accurately representing the spectral distribution of solar radiation in the cryosphere.
54 ENVIRONMENTAL SCIENCES↗
The Systems of Radiological Protection for Ionizing and Non-Ionizing Radiation
This paper summarizes the presentations and panel discussion held at Plenary Session 1 of the 16th IRPA International Congress/69th Health Physics Society Annual Meeting, in Orlando, FL, in July 2024. Plenary Session 1 discussed the basics of the systems of radiological protection (RP) for ionizing radiation (IR) and non-ionizing radiation (NIR) and included five presentations and a panel discussion. Rodney Croft, Chair of the International Commission on Non-Ionizing Radiation Protection (ICNIRP), delivered the first presentation. Croft introduced the System of RP for NIR and provided an overview of ICNIRP’s coverage and current areas of work. Werner Rühm, Chair of the International Commission on Radiological Protection (ICRP), delivered the second presentation. He gave an overview of the System of RP for IR and covered the key principles of justification, optimization, and dose limitation, including the current plans of ICRP toward the envisaged revision of the System of RP. The third speaker, Sigurður Magnús Magnússon, from the International Radiation Protection Association (IRPA), provided the perspective of the RP professionals on the development of the Systems of RP for IR and NIR. Emilie van Deventer, from the World Health Organization (WHO), presented WHO’s views of both Systems of RP and discussed the relevant current activities of WHO with regard to IR and NIR. Kathryn Higley, President of the National Council on Radiation Protection and Measurements (NCRP), delivered the final presentation. Higley outlined the history of NCRP, the differences between ICRP and NCRP, and discussed the role of the NCRP in the System of RP, including NCRP’s role to analyze mechanisms of interaction of NIR with biological systems, including humans. The session concluded with a fruitful panel discussion, where the audience had the opportunity to ask the five invited speakers questions.
61 RADIATION PROTECTION AND DOSIMETRY↗
Superconducting nanowire single-photon detector enhanced near-infrared II portable confocal microscopy for tissue imaging with indocyanine green
In this Letter a novel, to our knowledge, approach for near-infrared (NIR) fluorescence portable confocal microscopy is introduced, aiming to enhance fluorescence imaging of biological samples in the NIR-II window. By integrating a superconducting nanowire single-photon detector (SNSPD) into a confocal microscopy, we have significantly leveraged the detection efficiency of the NIR-II fluorescence signal from indocyanine green (ICG), an FDA-approved dye known for its NIR-II fluorescence capabilities. The SNSPD, characterized by its extremely low dark count rate and optimized NIR system detection efficiency, enables the excitation of ICG with 1 mW and the capture of low-light fluorescence signals from deep regions (up to 512 µm). Consequently, our technique was able to produce high-resolution images of bio samples with a superior signal-to-noise ratio, making a substantial advancement in the field of fluorescence microscopy and offering a promising opportunity for future clinical study.
Liu, Yifan (ORCID:0000000328877704)↗
Rapid and high-throughput determination of sorghum ( Sorghum bicolor ) biomass composition using near infrared spectroscopy and chemometrics
Compositional characterization of biomass is vital for the biofuel industry. Traditional wet chemistry-based methods for analyzing biomass composition are laborious, time-consuming, and require extensive use of chemical reagents as well as highly skilled personnel. In this study, near-infrared (NIR) spectroscopy was used to quickly assess the composition of above-ground vegetative biomass from 113 diverse, photoperiod-sensitive, biomass-type sorghum (Sorghum bicolor) accessions cultivated under field conditions in Central Illinois. Biomass samples were analyzed using NIR spectra collected in the spectral range of 867–2536 nm, with their chemical compositions determined following the National Renewable Energy Laboratory (NREL) protocol. Advanced spectral pre-treatment and band selection techniques were utilized to develop calibration models using partial least squares regression (PLSR). The models’ effectiveness was assessed through cross-validation and independent data tests. The predictions for moisture, ash, extractives, glucan, xylan, acid-soluble lignin (ASL), acid-insoluble lignin (AIL), and total lignin were accurate and reliable, demonstrating the capability of NIR spectroscopy to provide rapid and precise characterization of sorghum biomass. The results demonstrated that NIR spectroscopy is an efficient tool for rapidly characterizing sorghum biomass, making it a sustainable option for screening desirable feedstock for biofuel or bioproduct production.
09 BIOMASS FUELS↗
Sensitized Near-Infrared Emission of SiGe Nanocrystals via Heterostructuring with Quasi Two-Dimensional Perovskite
The near-infrared (NIR) emission of silicon–germanium alloy nanocrystals (SiGe NCs) was sensitized by heterostructuring with a quasi two-dimensional (Q-2D) perovskite (CsPbBr 3 blended with butylammonium bromide (BABr)). Colloidal SiGe NCs were synthesized by using a nonthermal plasma method with chloride precursors. As-synthesized SiGe NCs showed no detectable photoluminescence (PL). Embedding the SiGe NCs within the perovskite matrix via spin coating led to a heterostructure exhibiting dual emissions: green emission at 520 nm from the perovskite and NIR emission at 1035 nm from the SiGe NCs. Time-integrated and resolved PL measurements and transient absorption spectroscopy revealed energy transfer from the perovskite to the SiGe NCs, which sensitized the NIR emission from the SiGe NCs and quenched the green PL for the perovskite. These results demonstrate that Ge alloying can effectively tune the bandgap of Si NCs and highlight the feasibility of heterostructuring perovskites and photoluminescent NCs to enhance or activate their PL. In conclusion, this approach broadens their potential applications in the NIR region.
36 MATERIALS SCIENCE↗
Self-Assembly Driven Microlithography via Near-Infrared Light Activation
Current vat photopolymerization (VP) relies on UV or visible light to start the rapid crosslinking of liquid photocurable resins into 3D-printed structures. Here, we develop a self-assembly-driven photopatterning approach to photothermally generate polymeric solids by combining thermoplasmonic nanoparticles and thermoresponsive polymers, in which near-infrared (NIR) light activates thermoplasmonic heating of nanoparticles, triggering the irreversible self-assembly of thermoresponsive polymers into insoluble mesoglobules. A small amount of thermal initiator and crosslinker leads to irreversible self-assembly of polymer nanocomposites. NIR light offers deeper penetration and reduced scattering compared to UV, enabling more uniform curing of thicker or filled materials and expanded process control for composites or opaque systems. Thermoplasmonic heat generation is achieved using surface-modified gold nanorods (AuNRs) with a longitudinal localized surface plasmon resonance peak in the NIR region. Key variables such as polymer composition, molecular weight, physical interactions at the nanoparticle–polymer interface, which can be tuned by surface functionalization, AuNR concentration, and pH can be used to tailor the assembly behavior of these systems, including photothermal effect, flocculation, and cloud point temperature, and the mechanical properties of the final structures. Collectively, these results highlight a platform for photothermally-driven microlithography of polymer solids with diverse, tunable macroscopic properties, enabled by low-energy NIR light-activated self-assembly.
36 MATERIALS SCIENCE↗
Design of Zn Chalcogenide Shells for Emissive Ga-Rich In 1– X Ga X As Quantum Dots Synthesized in Molten Salts
Colloidal quantum dots (QDs) have seen expanded applications in optoelectronics from visible to near-infrared (NIR) wavelengths. However, the options for QDs with bright and size-tunable emission in the NIR region are mostly limited to heavy-metal-based (Pb, Cd, Hg) semiconductors. Here, Ga-rich In 1–X Ga X As QDs with zinc chalcogenide shells are demonstrated as candidates for NIR emitters. Based on new developments in inorganic molten salt chemistry, we synthesized colloidal In 1–X Ga X As QDs containing up to 85% gallium with high crystallinity based on Raman and XRD analyses. Zinc selenide and sulfide shells with different morphologies were grown on In 1–X Ga X As QDs by controlling the Zn precursor chemistry. Despite the nominally reduced lattice mismatch in the In 1–X Ga X As/ZnSe heterostructure, a ZnS shell was found to be a much more effective passivation material: In 1–X Ga X As/ZnS core–shell QDs show a photoluminescence quantum yield (PL QY) of about 30% versus 11% for ZnSe shells. In conclusion, based on the analysis of PL decay and transient absorption (TA) dynamics, we surmise that electron trapping is the major reason for efficiency loss, providing a clear heterostructure design principle for realizing efficient NIR-emitting In 1–X Ga X As QDs.
Electron trapping↗
Data for Rapid and High-Throughput Determination of Sorghum ( Sorghum bicolor ) Biomass Composition using Near Infrared Spectroscopy and Chemometrics
Compositional characterization of biomass is vital for the biofuel industry. Traditional wet chemistry-based methods for analyzing biomass composition are laborious, time-consuming, and require extensive use of chemical reagents as well as highly skilled personnel. In this study, near-infrared (NIR) spectroscopy was used to quickly assess the composition of above-ground vegetative biomass from 113 diverse, photoperiod-sensitive, biomass-type sorghum ( Sorghum bicolor ) accessions cultivated under field conditions in Central Illinois. Biomass samples were analyzed using NIR spectra collected in the spectral range of 867–2536 nm, with their chemical compositions determined following the National Renewable Energy Laboratory (NREL) protocol. Advanced spectral pre-treatment and band selection techniques were utilized to develop calibration models using partial least squares regression (PLSR). The models’ effectiveness was assessed through cross-validation and independent data tests. The predictions for moisture, ash, extractives, glucan, xylan, acid-soluble lignin (ASL), acid-insoluble lignin (AIL), and total lignin were accurate and reliable, demonstrating the capability of NIR spectroscopy to provide rapid and precise characterization of sorghum biomass. The results demonstrated that NIR spectroscopy is an efficient tool for rapidly characterizing sorghum biomass, making it a sustainable option for screening desirable feedstock for biofuel or bioproduct production.
Biomass Analytics↗
Near-infrared spectroscopy as a green analytical tool for sustainable biomass characterization for biofuels and bioproducts: An overview
Biomass, a widely used renewable energy source, requires characterization to optimize biofuel and bioproduct processes, customize feedstocks, and ensure economic and environmental sustainability. Conventional wet-chemistry methods for biomass analysis are slow, expensive, and require significant reagents and skilled personnel. In contrast, near-infrared (NIR) spectroscopy, a faster, cost-effective, and reagent-free green technology, enables non-destructive biomass analysis with minimal sample preparation. This study provides an overview of the fundamentals of NIR spectroscopy and explores its recent applications for analyzing various biomass properties important to the biofuel and bioproduct industry. The study also critically evaluates the challenges and opportunities of using NIR spectroscopy for biomass analysis. This review aims to guide future research for rapid and high throughput characterization of biomass in the biomass industry, supporting the United Nations’ sustainable development goal (SDG) 7: producing affordable and sustainable energy.
Biofuels↗
A magnetic‐directed micro‐particle with near‐ IR light triggered guest‐release property
Abstract In this study, guest carriers, composed of magnetic‐directed hydrogel particles with near‐infrared (NIR)‐light‐triggered guest‐release properties, are prepared via the one‐pot method. The gel particles contain photostable NIR‐responsive polypyrrole nanoparticles with temperature‐sensitive poly(N‐isopropyl acrylamide), the combination of which can induce the accelerated release of encapsulated drugs by 337% upon 5 minutes of NIR light irradiation based on the release profile of NRMG‐1 at 10–15 min. Additionally, with magnetic iron oxide particles crosslinking the polyvinyl alcohol frames, these gel particles can form a physical barrier that decreases the release rate by 20% under an external magnetic field over 24 h. The magnetic iron oxide particles also enable the gel particles to reach their target destination without extra drug losses.
60 APPLIED LIFE SCIENCES↗
Exploiting Intersubband Plasmons in Vertically Aligned Carbon Nanotubes for Near-Infrared Electrochromic Windows
Optically transparent materials with switchable near-infrared (NIR) transmissivity are of significant interest for energy-saving smart window technologies. To this end, we demonstrate that semitransparent films of vertically aligned carbon nanotubes (CNTs) incorporated into electrochemically gated devices exhibit NIR transmittance changes up to 47% and bistable optical states that are appealing for low-power, large-area operation. The tunable NIR electrochromic response is driven by a doping-induced intersubband plasmon (ISBP) absorption, an optical feature in CNTs that is selective to light polarized perpendicular to the CNT axis. Vertically aligned CNT films (as opposed to more conventional planar CNT mats) thus allow us to isolate and study the ISBP resonance changes with applied voltage, electrode material, and film thickness.
absorption↗