The uranyl ion (UO22+), characterized by its heavy metal toxicity and radioactivity, poses significant threats to environmental safety and public health. In this study, a ratiometric fluorescent probe named TargetedNaphthalimide-Sensor (TNS) with self-calibrating capability was designed for UO22+ detection. Based on the 1,8-naphthalimide fluorophore and specific heterocyclic ligands, TNS exhibits unique intramolecular proton transfer characteristics in the excited state (ESIPT), demonstrating excellent sensitivity and anti-interference ability. Under high water ratio conditions, TNS coordinates with UO22+, resulting in ratiometric fluorescence changes. It shows a good linear relationship in the presence of low-concentration UO22+, and the detection limit is calculated to be as low as 4.6 & times; 10_8 M. Experiments and theoretical calculations suggest that the sensing mechanism of the probe might be attributed to the alteration of the electronic transfer state caused by UO22+ coordination. Cell imaging experiments demonstrate that the morpholine-functionalized TNS exhibits excellent lysosomal targeting capabilities, enabling precise localization and quantitative imaging of UO22+ within lysosomes. Further in vivo zebrafish imaging studies reveal that TNS accumulates in the digestive system of zebrafish, facilitating the detection and imaging of UO22+ within the digestive tract. The quantitative test strip fabricated from TNS enables the visual and portable detection of UO22+ in practical samples. Further practical testing demonstrates that TNS exhibits excellent utility across diverse environmental water samples.
Weak molecular interactions and subtle conformational variations can directly modulate CH bond strength, yet their spectroscopic signatures as a probe of polymorphism remain largely unexplored. Here, chiral phenylalanine in anhydrous Form I, hemihydrated fiber, and hydrogel states were investigated by Raman, IR, and vibrational circular dichroism (VCD) spectroscopy combined with DFT calculations, focusing on the CH stretching region. Isotopic substitution combined with VCD analysis reveals coexistence of gauche (G-) and trans (T) conformers in the fiber and hydrogel. The methine CD stretching vibration of the T conformer exhibits a marked blue shift relative to the G- conformer, indicating strengthening of the CH bond induced by close-range electrostatic repulsion from a neighboring NH₃+ group. Raman analysis of the hydrogel demonstrates preservation of the local G-/T hydrogen-bonded pairing motif and associated π-π interactions despite the absence of long-range crystalline order. These results demonstrate that CH stretching vibrations serve as highly sensitive reporters of weak molecular interactions and local structural organization in condensed phases.
Heavy metal cadmium (Cd) pollution poses a serious threat to agricultural safety, and the application of selenium (Se) to mitigate Cd stress introduces additional complexity for Cd nondestructive detection. This study investigated the feasibility of employing fluorescence hyperspectral imaging (F-HSI) for detecting Cd pollution in lettuce leaves under Se influence. The raw spectra were preprocessed using savitzky-golay filter to effectively suppress noise and enhance subtle spectral features, thereby highlighting weak fluorescence spectral information. Subsequently, a multi-modal differential fusion selector (M2DFS) was proposed to extract sensitive features closely associated with Cd by sorting out the complex fluorescence spectra-Se-Cd relationships under SeCd interaction conditions. Combined with a one-dimensional convolutional neural network (1D-CNN) based on the VGG architecture, the quantitative prediction model for Cd content in lettuce leaves under the background of SeCd interaction was developed. Compared with classical methods, M2DFS demonstrated superior feature extraction performance in both machine learning and deep learning models, significantly enhanced the predictive capability of Cd content under Se influence. Ultimately, the 1D-CNN model built using M2DFS-features achieved the highest prediction performance (Rp = 0.9173, RMSEP = 0.0272 mg/kg, RPD = 2.5108). In summary, the method system of F-HSI combined with savitzky-golay filter, M2DFS and 1D-CNN provides a reliable approach for nondestructive detection of Cd pollution in lettuce leaves under Se influence, offering a foundation for future research on nondestructive detection of heavy metal under complex agronomic environments.
Ultraviolet-visible (UV-Vis) absorption spectroscopy has been widely adopted in water quality monitoring due to its non-destructive, rapid, and pollution-free characteristics. However, high-dimensional spectral data are often contaminated by system noise and scattering from suspended solids, which adversely affects the accuracy and robustness of chemical oxygen demand (COD) prediction models. To address this issue, this study proposes a hybrid feature wavelength selection method-Competitive Adaptive Reweighted Sampling combined with Successive Projections Algorithm (CARS_SPA)-which performs two-stage screening to effectively eliminate redundant and irrelevant variables. Using real water sample spectra preprocessed by Savitzky-Golay smoothing, the CARS_SPA-LSSVM model significantly outperforms full-spectrum and single-algorithm approaches: on the validation set, it reduces root mean square error (RMSE) by 28.31%, 15.68%, and 14.91% compared to full-spectrum, CARS, and SPA, respectively, achieving a coefficient of determination (R2) of 0.9588. Further optimization of LSSVM hyperparameters using the Dung Beetle Optimizer (DBO) yields an improved RMSE of 0.1525 mg·L-1 and R2 of 0.9664. The results demonstrate that the proposed CARS-SPA-DBO-LSSVM framework achieves high prediction accuracy and robustness with minimal feature dimensionality, offering a promising solution for rapid and reliable COD monitoring in practical water quality applications.
Antioxidants such as ascorbic acid, glutathione, gallic acid, and caffeic acid are crucial for food quality and human health, yet their simultaneous discrimination in complex food matrices remains challenging due to overlapping reactivities. In this study, we developed a robust colorimetric sensor array based on trimetallic FeMnZn‑nitrogen‑carbon (FeMnZn-NC) nanozymes. Synthesized via high-temperature pyrolysis, the introduction of Zn as a sacrificial template generates abundant structural defects and exposes more active sites, resulting in significantly enhanced oxidase-like activity compared to mono- and bimetallic analogues. The nanozymes catalyze the rapid oxidation of TMB and ABTS substrates within 2 min, a process differentially inhibited by distinct antioxidants via varying electron-transfer and chelating mechanisms. Leveraging these unique kinetic "fingerprints," a dual-channel sensor array was constructed to identify ascorbic acid, glutathione, gallic acid, and caffeic acid, exhibiting linear responses over 0-40 μM with detection limits of 0.22, 0.43, 0.21, and 0.14 μM, respectively. The sensor demonstrated excellent anti-interference capability and was effectively applied to identify antioxidants in apples, pears, and kiwifruits. The integration of a smartphone-assisted colorimetric platform enables rapid, on-site detection, highlighting its strong potential for food quality monitoring and nutritional assessment.
Tangerine peel, known as Citri reticulatae pericarpium (CRP), is a well-documented medicinal and food-homologous material. CRP has garnered significant global attention due to its versatile properties and proven bioactivities. However, the price and pharmacological activity of CRP vary significantly depending on its geographical origin and storage age. Therefore, it is essential to develop a fast and simple discrimination method for CRP authentication. In this study, a Cu@ZIF-67 nanozyme featuring excellent laccase-like (LAC) and peroxidase-like (POD) activities was successfully fabricated via a mild etching strategy. Enzymatic activity assays revealed that the optimal dual enzymatic activities were achieved at a Cu2+ concentration of 175 mM. By leveraging the distinct multi-wavelength signal responses (370 nm, 652 nm, and 504 nm) generated from the catalytic oxidation of TMB, 4-AP, and 2,4-DP, respectively, a multi-wavelength three-channel sensing array was constructed. Mechanistic investigations indicated that the five flavonoids present in CRP exhibit competitive inhibition toward TMB oxidation, whereas they display non-competitive inhibition toward the oxidation of 4-AP and 2,4-DP. This divergence resulted in significant differences in inhibitory effects between the peroxidase-like and laccase-like channels. By integrating Linear Discriminant Analysis (LDA), precise qualitative discrimination of the five flavonoids was realized. Furthermore, a concentration-independent model based on the K-Nearest Neighbors (KNN) algorithm was established, enabling the accurate geographical authentication of CRP samples from 21 cities in 8 provinces and the reliable discrimination of samples with six different storage ages. Thus, this colorimetric sensor array-based strategy offers a rapid and low-cost tool for authenticating CRP, ensuring its quality control.
Raman spectroscopy has been explored for planetary exploration, particularly through the SHERLOC instrument for Mars habitability and astromaterial search tasks. The SHERLOC and other Raman-based astromaterial search tasks mostly rely on comparative Raman peak analysis and expert interpretation with respect to known spectral peaks. It is desirable to employ machine learning (ML) and deep learning (DL) methods for high-performance automated astromaterial identification from Raman spectra. In this work, for the first time, we benchmarked three ML and two DL models for the identification of six mineral classes from SHERLOC Raman spectra. Using the expert-labeled spectra of two campaigns (Crater Floor and Upper Fan), the best-performing DL model yielded identification accuracies of 89.3%, while maintaining competitive class-wise performance under class-imbalanced conditions. The best-performing DL model was further applied to non-diagnostic and unlabeled SHERLOC Raman spectra, and the resulting predictions were broadly consistent with material characteristics reported in the literature. These results suggest that DL methods may provide supportive information for automated Raman-based astromaterial identification and spectral interpretation in planetary exploration.
Phosphate is an essential nutrient in aquatic systems, however elevated phosphate discharge are a primary driver of eutrophication which in turn causes notable deterioration of water quality. Therefore, there is a critical need for simple, rapid, and low-cost methods suitable for on-site phosphate monitoring.In this work, a disposable paper-based surface-enhanced Raman spectroscopy (SERS) sensor was developed for sensitive detection of phosphate. The sensing strategy is based on an AgNPs-R6G system. Specifically, under acidic conditions,phosphate reacts with ammonium molybdate to generate phosphomolybdic acid, which subsequently interacts with rhodamine 6G (R6G).This process reduces the surface adsorption of free R6G on silver nanoparticles, resulting in the suppressed SERS signal intensity. The resulting signal decrease correlates quantitatively with phosphate concentration, enabling indirect quantitative detection of phosphate quantification. The paper-based sensor was fabricated on Whatman No. 1 filter paper where Alkyl ketene dimer (AKD) was employed to construct a hydrophobic isolation zone and a hydrophilic detection zone, thereby facilitating localized sample loading and stable SERS measurement. The proposed sensor exhibited a linear response to phosphate over the concentration range of 10-7 M to 10-2 M, with a detection limit of 6.5 × 10-8 M. The assay consumed only 50 μL of reagent per test and can be completed through a simple three-step operation. Owing to its low cost, disposability, facile fabrication, and satisfactory analytical performance, fabrication, and satisfactory analytical performance, this paper-based SERS platform represents a promising potential for rapid on-site phosphate detection in water-quality assessment.
As fundamental building blocks of living organisms, amino acids impose stringent demands on the sensitivity and specificity of biosensing technologies owing to their low physiological concentrations and similar molecular structures. In this work, two THz metamaterials are designed and fabricated. The "S-shaped" resonator exhibits a resonance peak at 1.01 THz via electric dipole resonance, which closely matches the absorption peak of arginine (Arg) at 0.99 THz. The "X-shaped" resonator operates at 0.51 THz through a parallel magnetic dipole resonance. We performed chiral discrimination and concentration detection of Arg solutions using a terahertz time-domain polarization spectroscopy (THz-TDPS) system. Through the analysis of orthogonal polarization components and the calculation of chiral optical parameters, we achieved multi-dimensional sensing characterization that encompasses both polarization and chiral parameters. Experimental results demonstrate three key findings: (1) Across all parameter characterizations, the S-shaped sensor exhibits consistently higher sensitivity than its X-shaped counterpart for both D-Arg and L-Arg. (2) The sensitivity derived from the characterization of both polarization and chiral parameters is superior to that from frequency-shift. (3) Superior sensitivity is observed for chiral parameters over polarization parameters. Moreover, chiral characterization shows a pronounced response to enantiomeric differences. In contrast, frequency shift and amplitude parameters show no significant ability to discriminate between D- and L-Arg, and both sensors achieve a limit of detection (LOD) of 10-5 mg/μL for the enantiomers. This study offers a reliable approach for chiral recognition and quantitative analysis of amino acids, which is vital for early biomedical screening and reliable quality control in the pharmaceutical industry.
In this study, a novel approach was developed for the simultaneous determination of levonorgestrel (LEV) and ethinyl estradiol (EE) in synthetic mixtures and pharmaceutical tablet formulations using spectrophotometric analysis combined with intelligent system and signal processing technique. Two strategies were employed: a fuzzy inference system (FIS), and a hybrid method integrating discrete wavelet transform (DWT)-a core signal processing tool-with principal component analysis (PCA) and FIS. The FIS method yielded average recoveries of 101.77% and 97.84% for LEV and EE, respectively, with root mean square error (RMSE) of 1.795 and 2.654. In DWT approach, wavelet families such as Daubechies 4 (db4), Demeyer, and Haar were used to decompose the absorbance spectra, and PCA was applied for dimensionality reduction. The extracted features were then processed using the FIS. Among all wavelets, the Demeyer family demonstrated superior performance, achieving mean recoveries of 100.24% for LEV and 99.68% for EE, with RMSE values of 0.4944 and 0.6074, respectively. Application to commercial tablet formulation showed that the DWT-PCA-FIS method outperformed the standalone FIS model, providing mean recoveries above 97% and relative standard deviation (RSD) below 1% for both compounds. Results were compared with high-performance liquid chromatography using ANOVA, and no significant differences were found. These results indicate that the proposed intelligent and signal processing-based approaches can serve as effective alternatives to conventional chromatographic methods due to their simplicity, low cost, and ability to operate without the need for sample preparation.
5-Hydroxymethylfurfural (5-HMF) is a toxic compound formed during the improper processing or storage of honey, endangering human health. Thus, rapid and accurate detection of 5-HMF is essential. In this study, we fabricated uniform gold nanorod (AuNR) array surface-enhanced Raman spectroscopy (SERS) substrates with high-density plasmonic hot spots by optimizing the AuNR aspect ratio and employing a novel three-phase self-assembly technique, significantly enhancing detection sensitivity and reproducibility. The AuNR array (aspect ratio of 3.5) showed the strongest signals due to the close match between its 781 nm longitudinal LSPR wavelength and the 785 nm excitation laser wavelength, enabling efficient resonant coupling. Leveraging the dual-resonance of the AuNR arrays, tip-induced field focusing and nanogap-mediated plasmon coupling synergistically produced ultrahigh electromagnetic enhancement for SERS detection. Using AuNR array-3.5 as the SERS substrate, the signal intensity demonstrated a positive correlation with a concentration of 5-HMF in the range of 1-50 mg/L, with a limit of detection (LOD) as low as 0.093 mg/L. The substrate revealed excellent uniformity (relative standard deviation, RSD = 3.86%), reproducibility (inter-batch RSD = 2.51%), reusability (RSD = 13.2%) and stability (stored at 4 °C for 30 days). In spiked honey samples, this method achieved an LOD of 35.4 mg/kg, well below the international maximum residue limit (40 mg/kg), and recovery values ranging from 73.32% to 95.92%, confirming its high sensitivity and accuracy. This work provided a rapid, efficient approach for detection of 5-HMF in honey and highlights its application potential in SERS-based food safety monitoring technologies.
The pollution of plastic materials represents one of the most important environmental challenges, due to the rapid and uncontrolled increase in their production, consumption, and use. Therefore, the recycling and valorization of post-consumer plastics are strategic solutions to mitigate this phenomenon. These processes are subject to rigorous international regulations that require recycled polymers to exhibit physical properties and quality consistent with those of their virgin counterparts. To achieve this goal, a thorough and accurate classification of plastic materials before and after recycling is essential. Infrared (IR) spectroscopy has emerged as a fundamental technique for this purpose, enabling non-destructive identification of polymers through their characteristic vibrational features. However, the high spectral similarity between different plastics often leads to misclassification. To overcome this limitation, in this paper, we integrate IR spectroscopy with advanced Machine Learning (ML) algorithms. We develop and validate a rapid, automated ML-based classifier for the identification of four prevalent plastic types: HDPE-B, HDPE-P, LDPE, and PP. The classification models were trained on a dedicated IR spectral database generated from original experimental measurements. Using the open-source Quasar platform, we performed a comparative analysis between traditional ML algorithms and standard Convolutional Neural Network (CNN) architecture. The study shows that both CNN and classical algorithms, in particular Random Forest (RF), achieve optimal performance in the classification task, with accuracies of 1.000 and 0.998, respectively. Our results demonstrate that the integration of deep learning architectures with IR data significantly enhances the accuracy and reliability of plastic recognition, providing a robust tool to support the industrial transition toward high-quality recycled polymers.
Phosphate additives are widely used to improve water-holding capacity in fish fillets, yet their effectiveness is strongly influenced by muscle structure, particularly after freeze-thaw treatments. In this study, the potential of hyperspectral imaging (HSI) was investigated for predicting phosphate dosage levels in snakehead fillets among different freeze-thaw cycles during the water retention stage. With shortwave infrared (SWIR) spectra and physicochemical properties as predictors, ordinal logistic regression (OLR) and light gradient boosting machine (LightGBM) models were developed to predict ordered responsiveness categories. LightGBM achieved the best performance for soaking weight gain (SWG) and centrifugal loss (CL) with prediction overall accuracies (OA) of 0.77 and 0.74 (RMSE of 0.45 and 0.51) respectively, while OLR showed greater stability for bound water (BW) and immobilized water (IW), reaching accuracies up to 0.79 and 0.74 with RMSE of 0.45 and 0.51. According to confusion matrices, most samples were correctly classified, especially at lower dosage 1levels. The pixel-wise prediction maps based on LightGBM further revealed pronounced intra-fillet heterogeneity associated with freeze-thaw cycles, highlighting spatially non-uniform phosphate responsiveness. This research demonstrates that SWIR hyperspectral imaging could be used to characterize and predict phosphate responsiveness in fish fillets, providing an insight of freeze-thaw history induced heterogeneity and its impact on water retention behavior for phosphate addition.
In this study, a novel near-infrared fluorescent probe was designed and constructed based on a cyano-substituted BODIPY dye through a one-step synthesis approach. The amidoxime group on the probe structure enables a rapid and specific reaction with HOBr, regenerating the cyano-containing BODIPY dye accompanied by a significant fluorescence quenching effect. The quenching intensity exhibits a positive correlation with increasing HOBr concentration, thereby achieving quantitative detection of HOBr with a detection limit of 1.17 μM. At the cellular level, the probe demonstrates excellent biocompatibility and stability, successfully enabling the visualization of both endogenous and exogenous HOBr in living cells. Furthermore, the probe exhibits superior lipid droplet-targeting capability, providing a powerful molecular tool for investigating the physiological functions of lipid droplets, particularly the role of HOBr in lipid metabolism.
Low- and high-frequency Raman spectroscopy combined with Raman mapping was employed to investigate the structural evolution of annealed polyamide 6 (PA6) films during heating and photo degradation. Temperature-dependent Raman measurements revealed systematic changes in both low-wavenumber modes associated with molecular chain dynamics and high-wavenumber bands related to crystalline conformations and hydrogen-bonded functional groups. Complementary intensity variations were observed between low-wavenumber bands at ∼60 and ∼105 cm-1, as well as between the amide III and amide I modes in the high-wavenumber region, reflecting their distinct vibrational directions relative to hydrogen bonding. Raman mapping was further employed to visualize spatially resolved structural evolution during the photodegradation simulation. While complementary spatial patterns were observed between low- and high-wavenumber vibrational modes, the carbonyl index exhibited a markedly different distribution from that of the crystallinity-related bands, suggesting a spatial decoupling between chemical oxidation and higher-order structural evolution. The present study demonstrates that low-frequency Raman mapping provides a sensitive and non-destructive approach for detecting subtle early-stage degradation processes that are difficult to resolve using conventional spectroscopic methods, offering new mechanistic insight into polymer degradation behavior.
The solvothermal synthesis, TD-DFT calculations, and spectroscopic/electrochemical characterization of new ruthenium naphthalimide complexes, namely [Ru(bpy)2(py-NI-Br)Cl]Cl (1), [Ru(bpy)2(py-NI-NO2)Cl]Cl (2), and [Ru(bpy)2(py-NI-NH2)Cl]Cl (3) (where py-NI-Br, py-NI-NO2, and py-NI-NH2 = 4-bromo-N-(4-pyridyl)-1,8-naphthalimide, 4-nitro-N-(4-pyridyl)-1,8-naphthalimide, and 4-amino-N-(4-pyridyl)-1,8-naphthalimide, respectively) are presented. Complexes 1-3 lose the chlorido ligand when a positive potential is applied, to yield the solvate complex in solution. The Ru-bpy chromophore dominates the photoluminescence profile because the MLCT (metal-to-ligand charge transfer) levels have lower energy than the NI (naphthalimide) IL (intraligand) levels. In aqueous solutions, the chlorido ligand in the ground state is slightly labile. On the other hand, photosubstitution reactions can be tuned by choosing the appropriate solvent. In coordinating solvents with relatively high dielectric constants, the chlorido ligand is substituted, and a charged photoproduct arises. In non-polar solvents, the NI ligand is photolabile, and neutral products emerge. All the complexes investigated herein are solvatochromic. Although the NI IL transitions have charge-transfer character, which is due to the substituent groups, the MLCT transitions of complexes 1-3 feature higher solvatochromic responses. The exception is free py-NI-NH2, in which the amino group inductive effect produces Δv∼ = 1574 cm-1. Complexes 1-3 intercalate into DNA better than the corresponding free NI ligands and display moderate Ksv constants (∼104 M-1). Complex 3 increases the DNA melting temperature by 10 oC and has the highest intercalation ability in the series.
Large electronic and structural reorganization upon photoexcitation endows organic π-conjugated molecules with complex and unconventional photophysical behavior. Herein, we report the synthesis, structural characterization, and excited-state dynamics of a new disubstituted N,N'-dihydrophenazine derivative (DPP-(CHO)2) functionalized with formyl (-CHO) groups. Single-crystal X-ray diffraction and theoretical calculations reveal a planar ground-state conformation. The introduction of -CHO substituents induces a strong intramolecular charge transfer (ICT) character, resulting in a pronounced solvent-dependent emission, with fluorescence intensity maxima shifting from 523 nm in non-polar solvents to 610 nm in polar protic media. Femtosecond-time-resolved spectroscopy shows an ultrafast ICT process (∼250 fs in DMF and ∼550 fs in MeOH), followed by the phenyls twisting that leads to a twisted ICT (TICT) state. The timescale of this torsional relaxation strongly depends on solvent hydrogen-bonding ability and viscosity, ranging from a few picoseconds in DMF to hundreds of picoseconds in alcohols and triacetin. The emission decays of ICT and TICT states govern the observed photobehavior, with lifetimes modulated by solvent polarity. Above 140 K, non-radiative decay becomes thermally activated, with an energy barrier of 1.2 and 1.9 kcal/mol associated with the ICT and TICT emitters, respectively. In addition, the solid-state behavior is examined in both powder and dispersed in a PMMA film, revealing spectroscopy and photodynamics features dictated by the formation of H- and J-aggregates. These results highlight the key role of substituent effects and solvent interactions in controlling the excited-state dynamics of N,N'-dihydrophenazine derivatives.
Continuous flow is widely used in chemical synthesis owing to its superior reaction control and high heat/mass transfer efficiency. However, current component concentration quantification techniques, including online instruments, still require extensive offline experiments to establish quantification models for each reaction system, which limits the analytical efficiency of continuous flow reactions. In this work, a quantitative analysis method for continuous flow reaction systems is established using online FTIR spectroscopy combined with dynamic flow rate control. A quantification model is constructed with artificial neural networks from the sampled infrared spectroscopic data generated by continuously adjusting flow rates, and is utilized to monitor reactions and predict reactant and product concentrations in real time. Additionally, kinetic analysis and reaction condition optimization are performed via continuous flow experiments under varying flow rates. Results demonstrate that this method enables rapid and continuous quantitative analysis of continuous flow reactions, offering an effective approach for reaction monitoring and process optimization.
Ophicalcite, which is enriched in serpentine and carbonate phases that are pervasive across the Martian surface, serves as an optimal terrestrial analogue for investigating the evolution of hydrated minerals on Mars. In this study, the optical and electrical properties of ophicalcite were systematically investigated within the 0.2-1.0 THz range using Terahertz Time-Domain Spectroscopy (THz-TDS) under simulated Martian surface conditions involving proton irradiation and low-temperature environments. The optical parameters of ophicalcite exhibited significant variations following proton irradiation. By applying the Effective Medium Theory (EMT), the intrinsic complex permittivity of the samples was extracted to characterize their optical properties in the terahertz frequency range. Furthermore, within the temperature range of 80 K to 280 K, both the refractive index and the real part of the complex permittivity of ophicalcite exhibited a substantial increase after proton irradiation. By utilizing ophicalcite as a terrestrial analogue, this study demonstrates the distinct response characteristics of serpentine under simulated Martian conditions. Furthermore, the results demonstrate that Terahertz Time-Domain Spectroscopy (THz-TDS) can provide critical data support for future in situ exploration and remote sensing missions on Mars.
