Spectral Differences Research Articles

Establishment of a clinical workflow for in vivo Raman spectroscopy during head and neck cancer surgery

As first part of an ongoing prospective feasibility trial (DRKS00028114) this work explored the integration of in vivo Raman spectroscopy (RS) into the routine setting workflow of head and neck cancer (HNC) surgery. In vivo RS was performed intraoperatively on 30 patients with HNC cell carcinoma and 10 patients with inflammatory diseases as a control group. A standardized process was established using a Raman system complied with stringent medical device regulatory standards. Spectra were collected in vivo from the tumor site, the tumor margins, and healthy tissue. The learning curve of the HNC team significantly improved measurement times from over 30 min initially to 2 min after 15 patients. Data from 35 patients were interpretable, demonstrating clear spectral differences between tumor and healthy tissues. The intraoperative in vivo RS workflow is now well established and is being used in the ongoing clinical trial.

Nondestructive Detection of Frankia in Alnus glutinosa With NIR Spectroscopy

ABSTRACTNitrogen (N) is essential for plant growth, yet excessive fertilizer use contributes to environmental degradation. Actinorhizal trees like Alnus glutinosa form symbiotic relationships with nitrogen‐fixing bacteria of the genus Frankia, reducing reliance on synthetic fertilizers. However, distinguishing between soil‐derived and symbiotically fixed nitrogen remains a challenge. This study investigates the potential of NIR spectroscopy as a nondestructive tool for differentiating N sources in A. glutinosa. Seedlings were grown in sterilized soil under controlled conditions with and without Frankia inoculation, and across a gradient of NH4NO3 fertilization (0–20 mM). We measured leaf chlorophyll, nitrogen content, biomass, and NIR reflectance (330–1100 nm) of the third fully expanded leaf. principal component analysis (PCA) and partial least squares (PLS) regression revealed that spectral signatures significantly differed between inoculated and uninoculated plants, particularly in the visible range around 555 nm. Despite similar leaf chlorophyll levels, Frankia‐inoculated plants and those fertilized with 20 mM NH4NO3 exhibited spectral differences that could otherwise not be detected by SPAD measurements. PLS regression explained up to 54.8% of spectral variance based on nitrogen source, even in the absence of unique spectral peaks. These findings highlight the potential of NIR spectroscopy for rapid, in vivo and in vitro assessment of symbiotic N‐fixation in trees, offering a novel and more precise approach than SPAD measurements.

The intermediate-mass black hole 2XMM J123103.2+110648: A varying disc accretion rate during possible X-ray quasi-periodic eruptions?

We fit the evolving X-ray spectra of the variable and fading source 2XMM J123103.2+110648 (J1231), which is an intermediate-mass black hole (IMBH) candidate. Recent X-ray timing studies propose that the quasi--periodic oscillation (QPO) of J1231, observed at the peak of its X-ray light curve, is a variant of the quasi-periodic eruptions (QPEs) observed in other sources. Here, we fit X--ray spectra from and using a slim disc model for the black hole accretion disc, obtaining a best-fit black hole mass of (6±3) M_⊙ and a spin of $>0.6$ at 2σ confidence. This mass is consistent with previous estimates, supporting the IMBH interpretation, and the spin measurement is new. Yet, the nature of J1231 remains uncertain: its long-term variability (decade-long continuum evolution) could signal a tidal disruption event or active galactic nucleus variability. We find that the spectral evolution within the first three years after the source's detection can be well explained by either a varying disc accretion rate, dot m, or a varying disc inclination, þeta. Meanwhile, we find that during the short-term variability (the QPO with a ∼3.8 hr period), each oscillation does not show the `hard-rise-soft-decay' typical of QPEs. We fit the average spectrum at the QPO light curve maxima and the average spectrum at its minima, finding that the spectral difference is well explained by dot m decreasing from peaks to valleys if þeta<30^̧irc and remaining constant between all data epochs. This result suggests that the short--term QPO behaviour might also be driven by a varying disc dot m.

Differential Diagnosis of Papillary Thyroid Carcinoma and Nodular Goiter With Papillary Hyperplasia Using Hyperspectral Imaging Technology.

Papillary thyroid carcinoma (PTC) and nodular goiter with papillary hyperplasia (NGPH) share similar histological features, complicating both preoperative and intraoperative diagnoses. We assessed hyperspectral imaging (HSI) combined with deep learning to differentiate PTC from NGPH. Forty-three paraffin-embedded PTC and 39 NGPH samples were imaged across 400-1000 nm, with reflectance calibration and Savitzky-Golay smoothing applied. Extracted spectral features were input into a one-dimensional convolutional neural network with a self-attention mechanism. HSI demonstrated sensitivity above 90% in the 500-600 nm and near-infrared regions for distinguishing PTC and NGPH. The model achieved an area under the ROC curve of 0.8635 and pixel-level classification accuracy of 87.07%, with both sensitivity and specificity at 87%. Spectral feature depth correlated significantly with histopathological parameters. These findings indicate that HSI combined with deep learning can accurately capture spectral differences between PTC and NGPH, supporting its potential for rapid intraoperative guidance and noninvasive preoperative screening.

The Discretization‐Corrected Particle Strength Method for the Barotropic Vorticity Equations

ABSTRACTWe present a novel meshless Lagrangian method for numerically solving the barotropic vorticity equation on a rotating sphere, an essential model in geophysical fluid dynamics. Our approach combines a particle‐based discretization with a Discretization Corrected Particle Strength Exchange (DCPSE) operator, offering a consistent and accurate approximation of differential operators on unstructured node distributions. The method is implemented in a fully Lagrangian framework, inherently conserving circulation and enabling straightforward adaptation to complex geometries. We validate the proposed scheme against standard test cases for global circulation and Rossby‐Haurwitz waves. The results demonstrate excellent agreement with reference solutions obtained from high‐resolution spectral and finite difference models. In particular, our method captures the essential dynamics of the vorticity field with high fidelity and low numerical diffusion, while exhibiting convergence and stability properties suitable for long‐term integrations. This study highlights the potential of meshless Lagrangian techniques in large‐scale geophysical simulations. These techniques provide an alternative to traditional grid‐based approaches and facilitate the natural handling of adaptive and irregular node distributions.

TD‐NMR Structural Profiling of Meat and Plant‐Based Meat Analog Burgers

ABSTRACTThe functional properties of food are closely linked to its atomic, molecular, and microstructural characteristics. This study evaluates the potential of time‐domain nuclear magnetic resonance (TD‐NMR) relaxometry as a non‐destructive technique for profiling the internal microstructure of Soy‐based plant‐based meat analogs (PBMA) compared to Angus beef burgers. TD‐NMR results were supported by measurements of water content, water release, and morphological analysis using visual and confocal microscopy. Microscopy revealed clear structural differences: Soy‐PBMA burgers exhibited a gel‐like, porous matrix, while Angus beef burgers showed compact, fibrous bundles characteristic of natural muscle tissue. Moisture analysis indicated lower total water content in Soy burgers, while water release tests demonstrated higher water‐holding capacity in Angus burgers, suggesting stronger protein–water interactions. TD‐NMR relaxation data reflected these structural distinctions. T₁ recovery times were longer in Angus burgers, while T₂ relaxation times were shorter and more narrowly distributed, consistent with tightly packed myofibrillar proteins. In contrast, Soy‐PBMA burgers exhibited broader T₂ distributions, reflecting a looser, more porous plant‐protein matrix. One‐dimensional (1D) and two‐dimensional (2D) T₁–T₂ spectral fingerprinting further highlighted differences in proton relaxation behavior, particularly between the myofibrillar proteins of meat and the globular proteins of plant‐based formulations. These findings confirm TD‐NMR relaxometry as a rapid, reliable, and non‐destructive method for differentiating the internal structure of meat and plant‐based products. This approach offers valuable insights for product development, quality assurance, and the design of next‐generation plant‐based meat alternatives.

Feature selection-oriented interactive visual analysis approach for hyperspectral images

Hyperspectral images (HSIs) have become increasingly prominent as they can maintain the subtle spectral differences of the imaged objects. Designing approaches and tools for analyzing HSIs presents a unique set of challenges due to their high-dimensional characteristics. Given the problems existing in the current visual analysis methods of HSIs, such as insufficient guidance and difficulty in achieving an accurate selection of specific spectral pixels, a universal interactive visual analysis approach is proposed in this article, which enables observers to visually interpret the rich information contained in HSIs with guidance, and pertinence through a graphical interface. The selection of the region of interest can realize the interactive screening from the spatial dimension of HSIs. Three information indicators are used to guide observers to select bands effectively. The clustering calculation and its scatter plot play an important guiding role in the selection and interpretation of feature classes for observers. Aiming at the precise selection of specific spectral pixels, a parallel coordinate method with reordering calculation of spectral bands is proposed to make it easier to distinguish spectral data curves and improve the clarity of target class expression. Finally, the usability and effectiveness of the proposed approach are analyzed through experiments.

An assessment of forest degradation in the Sonitpur East Forest division, Assam, India using object-based image analysis

ABSTRACT Forest ecosystems in Assam are increasingly threatened by anthropogenic activities, leading to significant forest degradation despite being granted protection by legislation. This study examined the extent of forest degradation in the Sonitpur East Forest division which includes the Behali Wildlife Sanctuary, Biswanath, Gohpur, Naduar and Singlijan Reserved Forests using object-based image analysis over 30 years (1990–2020). These analyses employed multi-resolution segmentation, spectral difference segmentation and a support vector machine classifier using Landsat imagery. The results revealed a significant decline in dense forest cover across the study area, except for Singlijan Reserved Forest. The most pronounced forest loss was observed in Biswanath Reserved Forest, followed by Gohpur and Naduar Reserved Forests and Behali Wildlife Sanctuary. Simultaneously, agricultural land and open forest areas expanded, with agricultural land in Gohpur Reserved Forest increasing by 60.28% at the expense of dense forest and barren land. These results underscore the crucial need for targeted conservation policies and sustainable land management practices to mitigate forest degradation. The application of remote sensing and object-based image analysis methodology offers a reliable framework for continuous forest monitoring supported by data-driven decision-making, facilitating effective conservation planning in northeastern India and other forested regions experiencing significant degradation.

Fourier-Transform Infrared Spectroscopy Analysis of 3D-Printed Dental Resins Reinforced with Yttria-Stabilized Zirconia Nanoparticles

Background/Objectives: This study investigates the chemical structure and molecular interactions in 3D-printed dental resins reinforced with varying concentrations of Yttria-Stabilized Zirconia (YSZ) nanoparticles, using Fourier-Transform Infrared Spectroscopy (FTIR) to assess the compatibility and bonding behavior at the molecular level. Methods: Three groups of 3D-printed methacrylate-based resin discs were fabricated: a control (0% YSZ), and experimental groups reinforced with 1% and 3% YSZ nanoparticles. Samples were produced using Digital Light Processing (DLP) technology and post-processed under standardized conditions. FTIR spectra were collected via ATR mode over a wavenumber range of 4000–600 cm−1. Spectral differences at key wavenumbers (1721.16, 1237.11, and 929.62 cm−1) were statistically analyzed using one-way ANOVA and Tukey’s post hoc test. Results: FTIR spectra showed no significant shifts in the ester carbonyl band at 1721.16 cm−1, suggesting the preservation of the core resin matrix. However, a statistically significant increase in absorbance at 1237.11 cm−1 was observed in the 1% YSZ group (p = 0.034), indicating dipolar interaction. A distinct new peak at 929.62 cm−1, corresponding to Zr–O vibrations, emerged in the 3% YSZ group (p = 0.002), confirming successful nanoparticle integration. Conclusions: YSZ nanoparticles enhance specific molecular interactions within methacrylate-based dental resins without compromising structural integrity. These findings support the potential application of YSZ-reinforced 3D-printed resins in durable, biocompatible permanent dental restorations.

Benchtop NMR Spectroscopy of In Vivo Multicellular Organisms.

NMR spectroscopy is a critical tool for environmental and biological research, but the physical and financial barriers of standard "high-field" NMR spectrometers can limit applications, especially in the environmental sciences. Low-field benchtop NMR (1H resonance frequencies generally ≤100 MHz) is more accessible, but its lower sensitivity and increased spectral overlap have limited the study of complex samples. Living organisms are among the most heterogeneous samples, and it is unclear if useful information can be extracted in vivo using benchtop NMR. Here, the potential of low-field (80 MHz) in vivo NMR is first assessed by analyzing 13C-labeling of unicellular green algae and then by monitoring a process within a multicellular organism (T. californicus). This is followed by studying live brine shrimp (A. franciscana) at 13C natural abundance. Adults are compared to brine shrimp cysts, with a number of spectral assignments possible and differences between the life stages clearly evident. High-field NMR is used to confirm peak assignments and provide a more comprehensive characterization of biomolecules present, ultimately making the low-field NMR data more useful. Standard experiments such as 1D 1H, 1D 13C and 2D HSQC are conducted, as well as more advanced experiments such as 13C-SSFP, which greatly enhances 13C sensitivity, and reverse HSQC, which decreases spectral overlap. Ultimately, this work demonstrates that low-field NMR can effectively analyze live organisms with or without isotopic enrichment and that it holds great potential for future work, such as in vivo analysis of organisms directly in the field if/when portable NMR spectrometers become available.

Out on a Limb: The Signatures of East–West Asymmetries in Transmission Spectra from General Circulation Models

Abstract In the era of JWST, observations of hot Jupiter atmospheres are becoming increasingly precise. As a result, the signatures of limb asymmetries due to temperature or abundance differences and the presence of aerosols can now be directly measured using transmission spectroscopy. Using a grid of general circulation models with varying irradiation temperatures (1500–4000 K) and prescriptions of cloud formation, we simulate 3D ingress/egress and morning-/evening-limb transmission spectra. We aim to assess the impact that clouds, 3D temperature structure, and nonuniform distribution of gases have on the observed spectra, and how these inhomogeneities can be identified. A second goal is to assess the relative merits of two separate methods (ingress/egress versus morning-/evening-limb spectroscopy) for isolating atmospheric asymmetries. From our models, it is evident that an east–west temperature difference is the leading-order effect for producing ingress/egress or morning-/evening-limb spectral differences. We additionally find that clouds contribute strongly to the observed limb asymmetry at moderate irradiation temperatures in our grid (∼2000 K < T irr < 3500 K). At lower temperatures clouds equally dominate the optical depth on both limbs, while at higher temperatures the entire terminator region remains cloud free. We develop limb asymmetry metrics that can be used to assess the degree of east–west asymmetry for a given planet and predict trends in these metrics with respect to irradiation temperature that are indicative of various physical processes. Our results are useful for predicting and diagnosing the signatures of limb asymmetries in JWST spectra.

Silage Maize Identification Using a Temporal Difference-Based Model with Sentinel-2 Data: Insights from a Harvest-Based and Temporally Transferable Approach

In response to the limited research on silage maize classification in China and the lack of data support for refined agricultural and livestock management, this study proposes a Temporal Difference-based Silage Maize Identification Model (TempDiff-SMID) using the Google Earth Engine (GEE) platform. By analyzing the phenological phases of silage maize and grain maize, we identified their critical harvest periods and established decision rules for classifying silage maize, grain maize, and other land cover types. Preprocessed Sentinel-2 imagery was smoothed using the Whittaker filter to construct the TempDiff-SMID model. After iterative threshold optimization, the decision tree model achieved an overall accuracy of 0.9291 and a Kappa coefficient of 0.8923, indicating robust classification performance. The user’s accuracies for silage maize, grain maize, and other land cover types were 0.9216, 0.9219, and 0.9404, respectively, while the producer’s accuracies reached 0.94, 0.9008, and 0.9467, demonstrating minimal omission and commission errors across all categories. Furthermore, the F1 scores for silage maize, grain maize, and other land cover types were 0.9307, 0.9112, and 0.9435, respectively, confirming the effectiveness of the TempDiff-SMID framework in leveraging harvest time differences for accurate silage maize identification. To evaluate performance, we compared the TempDiff-SMID with the RF Model for Silage Maize Classification (SMRF). The TempDiff-SMID outperformed the SMRF in both overall accuracy (0.9043 vs. 0.9291) and Kappa coefficient (0.8511 vs. 0.8923), while also providing an intuitive representation of spectral and phenological differences between silage maize and grain maize. When applied to multi-year data, TempDiff-SMID demonstrated strong temporal transferability, achieving overall accuracies of 0.8621 (2022) and 0.8816 (2021), thereby confirming its robustness across growing seasons. The proposed model offers simplicity in methodology, clear interpretability, and efficient deployment, making it a practical tool for agricultural and livestock management systems. Its ability to rapidly adapt to new regions or years underscores its significance in supporting precision agriculture and sustainable farming practices.

Application of Hyperspectral Imaging Technology in Nondestructive Testing of Core Chemical Components of Fruits.

Hyperspectral imaging (HSI), as a nondestructive testing method integrating spectral analysis and spatial imaging, has shown unique advantages in the dynamic monitoring of the core chemical components of fruits. In this paper, we focus on the application of HSI in the detection of core components of fruits, such as carbohydrates, organic acids, water, and polyphenols, and systematically analyze its chemical response mechanism and technical bottleneck. By comparison of the spectral differences of chemical bond vibration and electron jump features, the dynamic correlation between HSI and quality parameters such as sugar and acidity is revealed. Then, the problems and challenges faced by hyperspectral imaging technology in the nondestructive detection of the core chemical composition of fruits in terms of data processing, environmental interference, and equipment cost are discussed, and conclusions and outlook of the potential and prospect of hyperspectral imaging technology in the detection of the core chemical composition of fruits are presented to provide a powerful technical support for the detection of the core chemical composition of fruits.

Investigating the Influence of the Weed Layer on Crop Canopy Reflectance and LAI Inversion Using Simulations and Measurements in a Sugarcane Field

Recent research in agricultural remote sensing mainly focuses on how soil background affects canopy reflectance and the inversion of LAI, while often overlooking the influence of the weed layer. The coexistence of crop and weed layers forms two-layered vegetation canopies in tall crops such as sugarcane and maize. Although radiative transfer models can simulate the weed layer’s influence on canopy reflectance and LAI inversion, few experimental investigations use in situ measurement data to verify these effects. Here, we propose a practical background modification scheme in which black material with near-zero reflectance covers the weed layer and alters the background spectrum of crop canopies. We conduct an experimental investigation in a sugarcane field with different background properties (i.e., bare soil and a weed layer). Tower-based and UAV-based hyperspectral measurements examine the spectral differences in sugarcane canopies with and without the black covering. We then use LAI measurements to evaluate the weed layer’s impact on LAI inversion from UAV-based hyperspectral data through a hybrid inversion method. We find that the weed layer significantly affects the canopy reflectance spectrum, changing it by 13.58% and 42.53% in the near-infrared region for tower-based and UAV-based measurements, respectively. Furthermore, the weed layer substantially interferes with LAI inversion of sugarcane canopies, causing significant overestimation. Estimated LAIs of sugarcane canopies with a soil background generally align well with measured values (root mean square error (RMSE) = 0.69 m2/m2), whereas those with a weed background are considerably overestimated (RMSE = 2.07 m2/m2). We suggest that this practical background modification scheme quantifies the weed layer’s influence on crop canopy reflectance from a measurement perspective and that the weed layer should be considered during the inversion of crop LAI.

Optical spectroscopic signatures of the red giant evolutionary state

Abstract Modern spectroscopic surveys output large data volumes. Theoretical models provide a means to transform the information encoded in these data to measurements of physical stellar properties. However, in detail the models are incomplete and simplified, and prohibit interpretation of the fine details in spectra. Instead, the available data provide an opportunity to use data-driven, differential analysis techniques, as a means towards understanding spectral signatures. We deploy such an analysis to examine core helium-fusing red clump (RC) and shell hydrogen-fusing red giant branch (RGB) stars, to uncover signatures of evolutionary state imprinted in optical stellar spectra. We exploit 786 pairs of RC and RGB stars from the GALAH survey, chosen to minimise spectral differences, with evolutionary state classifications from TESS and K2 asteroseismology. We report sub-percent residual, systematic spectral differences between the two classes of stars, and show that these residuals are significant compared to a reference sample of RC−RC and RGB−RGB pairs selected using the same criteria. First, we report systematic differences in the Swan ($\rm {C}_2$) band and CN bands caused by stellar evolution and a difference in mass, where RGB stars at similar stellar parameters have higher masses than RC stars. Secondly, we observe systematic differences in the line-width of the Hα and Hβ lines caused by a difference in microturbulence, as measured by GALAH, where we measure higher microturbulence in RC stars than RGB stars. This work demonstrates the ability of large surveys to uncover the subtle spectroscopic signatures of stellar evolution using model-free, data-driven methods.

Near-Field Terahertz Nanoscopy Spatially Resolves Chiral Drug-Cell Interactions: Toward Precision Intracellular Pharmacology.

Chiral drug enantiomers frequently exhibit marked differences in pharmacological efficacy and toxicity profiles. However, the resolution limitations of traditional detection methodologies render them incapable of resolving the spatial heterogeneity of drug distribution within individual cells and obscure our understanding of subcellular drug distribution and mechanism of action. Here, we employ terahertz scattering-type scanning near-field optical microscopy (THz s-SNOM), a cutting-edge technique offering nanoscale spatial resolution and label-free imaging capabilities, to investigate single-cell response heterogeneity to chiral drug exposure. We systematically analyzed morphological and biochemical compositional differences between control cells and those treated with RS-ibuprofen, (R)-(-)-ibuprofen, and (S)-(+)-ibuprofen. Our high-resolution analysis revealed that the mean area of plasma membrane-derived extracellular vesicles (PEVs) in drug-treated cells increased by 24.6%, 25.4%, and 39.9%, respectively, compared to the representative control cell. Through principal component analysis and Euclidean distance distribution methods, we identified distinct spectral variations in both intracellular and nuclear regions. Notably, these three enantiomeric forms induced fundamentally different patterns of heterogeneous evolution within cellular compartments. RS-ibuprofen treatment specifically resulted in increased membrane heterogeneity concomitant with decreased nuclear heterogeneity. Quantitative analysis of spectral intensity differences within nuclear regions (p < 0.001), based on single-frequency imaging and clustering analyses at characteristic THz peak frequencies, provided direct evidence of drug-induced heterogeneity. These findings establish a novel paradigm for mapping drug-cell interactions at the nanoscale, conceptualizing these processes as spatially encoded phenomena, thereby advancing the field of subcellular chiral pharmacology.

Spatial in situ mapping of cellulose and other biopolymers reveals the 3D tissue architecture in the green algae Ulva fenestrata.

Spatial in situ mapping of cellulose and other biopolymers reveals the 3D tissue architecture in the green algae Ulva fenestrata.

Using VIS-NIR spectroscopy and multi-omics analysis to compare mango anthracnose under natural and inoculated conditions.

Using VIS-NIR spectroscopy and multi-omics analysis to compare mango anthracnose under natural and inoculated conditions.

Hyperspectral image mixed noised removal via jointly spatial and spectral difference constraint with low-rank tensor factorization

Hyperspectral image mixed noised removal via jointly spatial and spectral difference constraint with low-rank tensor factorization

JWST MIRI reveals the diversity of nuclear mid-infrared spectra of nearby type 2 quasars

Type 2 quasars (QSO2s) are active galactic nuclei (AGN) seen through a significant amount of dust and gas that obscures the central supermassive black hole and the broad-line region. Here, we present new mid-infrared spectra of the central kiloparsec of five optically selected QSO2s at redshift z ∼ 0.1 obtained with the Medium Resolution Spectrometer module of the Mid-Infrared Instrument (MIRI) aboard the James Webb Space Telescope (JWST). These QSO2s belong to the Quasar Feedback (QSOFEED) sample, and they have bolometric luminosities of log Lbol = 45.5 to 46.0 erg s−1, global star formation rates (SFRs) that place them above the main sequence, and practically identical optical spectra in terms of spectral shape and [OIII] luminosity, but their nuclear mid-infrared spectra exhibit an unexpected diversity in both continua and features. They show 9.7 μm silicate features going from emission (strength of S9.7 = 0.5) to relatively strong absorption (S9.7 = –1.0), and 18 and 23 μm silicates that are either in emission or flat (S18 = [0.2,0.0] and S23 = [0.1,0.0]). In addition, two of the QSO2s show absorption bands of CO, H2O, and aliphatic grains, indicating different levels of nuclear obscuration across the sample. Their [NeV]/[NeII] ratios range from 0.1 to 2.1 and [NeIII]/[NeII] from 1.0 to 3.5, indicating different coronal line and ionizing continuum strengths. They have warm molecular gas masses of 1–4 × 107 M⊙ and warm-to-cold gas mass ratios of 1–2%, with molecular gas excitation likely due to jet-induced shocks in the case of the Teacup (J1430+1339) and to UV heating and/or turbulence in J1509+0434. Finally, they show polycyclic aromatic hydrocarbon (PAH) emission features with equivalent widths ranging from less than 0.002 to 0.075 μm, from which we measure a larger contribution from neutral molecules (PAH 11.3/6.2 = 1.3–3.4) and SFRs ≤ 3–7 M⊙ yr−1. This unprecedented dataset allowed us to start exploring the role of various AGN and galaxy properties, including ionizing continuum, obscuration, electron density, and jet-interstellar medium interactions, in some of the spectral differences listed above. Larger samples observed with JWST/MIRI are now required to fully understand the diversity of QSO2s’ nuclear mid-infrared spectra.