Near-infrared Bands Research Articles

A gas sensor scheme for CO based on optical-feedback linear-cavity enhanced absorption spectroscopy

Trace gases, especially some toxic trace gases, such as carbon monoxide, are of great value for high-precision detection of their concentration in environmental, safety, and health monitoring. To accurately measure CO gas, a novel spectral technique scheme is developed and demonstrated based on cavity-enhanced absorption spectroscopy with a high-quality linear cavity. The scheme can achieve the equal noise absorption sensitivity of 2.37 × 10−8 cm−1 at the cavity loss of 186 ppm, which can achieve the concentration measurement of 11.7 ppm of CO gas at 1564 nm in the near-infrared band. This approach has the potential for expansion to other gas concentration detection applications and can achieve ppb-ppt level concentration detection.

Distance estimation of gamma-ray-emitting BL Lac objects from imaging observations

The direct redshift determination of BL Lac objects is highly challenging as the emission in the optical and near-infrared bands is largely dominated by the non-thermal emission from the relativistic jet, which points very close to our line of sight. Therefore, the optical spectra of BL Lac objects often show no spectral lines from the host galaxy. In this work, we aim to overcome this difficulty by attempting to detect the host galaxy and derive redshift constraints based on assumptions on the galaxy magnitude (`imaging redshifts'). Imaging redshifts were derived by obtaining deep optical images under good seeing conditions, making it possible to detect the host galaxy as a weak extension of the point-like source. We then derived the imaging redshift by using the host galaxy as a standard candle, employing two different methods. We determine the imaging redshift for 9 out of 17 blazars that we observed as part of this programme. The redshift range of these targets is 0.28-0.60, and the two methods used to derive the redshift give very consistent results within the uncertainties. We also performed a detailed comparison of the imaging redshifts with those obtained using other methods, such as direct spectroscopic constraints or looking for groups of galaxies close to the blazar. We show that the constraints from the different methods are consistent and that combining the three constraints narrows down the redshift. For example, in the case of J2156.0+1818, which is the most distant source for which we detect the host galaxy, the redshift range is narrowed to $0.63<z<0.71$. This makes the source interesting for future studies of extragalactic background light in the Cherenkov Telescope Array Observatory era.

Development of soil water content retrieving method for irrigation agriculture areas using the red-edge band of Gaofen-6 satellite

Soil water content (SWC) is an essential index that reflects water conditions in the soil surface during drought, and changes in SWC are critical in guiding agricultural irrigation management. Remote sensing is a crucial means of monitoring SWC, and the red and near-infrared bands of optical remote sensing data are often used to monitor SWC through remote sensing retrieval. With the development of remote sensing technology, red-edge band has also been used to retrieve SWC. It exhibits higher sensitivity to changes in vegetation and SWC, relatively more precise differentiation between vegetation and soil, and more accurate SWC detection. However, there has been relatively little research regarding the use of the red-edge band for retrieving SWC. In this study, we developed a novel method, based on the principle of spectral space and the calculation of modified perpendicular drought index, to calculate the soil water index using the red-edge band of the Gaofen-6 (GF-6) satellite by comparing the retrieval accuracy of GF-6 using different combinations of red, near-infrared, and red-edge bands. Based on the measured SWC in the Shijin Irrigation District of Hebei Province, an SWC retrieval model was established using linear regression. Moreover, the new method was used to retrieve SWC from GF-6 and Sentinel-2 with the same central wavelength, and the reliability of the new method was compared and analyzed. The results revealed that the combination of red band and red-edge band 2 with a central wavelength of 0.74 μm substantial. improved the retrieval accuracy of SWC. Our study provides a new method for retrieving SWC using optical remote sensing, which can effectively improve retrieval accuracy and provide data support for agricultural irrigation monitoring and water management.

From Rangelands to Cropland, Land-Use Change and Its Impact on Soil Organic Carbon Variables in a Peruvian Andean Highlands: A Machine Learning Modeling Approach

Andean highland soils contain significant quantities of soil organic carbon (SOC); however, more efforts still need to be made to understand the processes behind the accumulation and persistence of SOC and its fractions. This study modeled SOC variables—SOC, refractory SOC (RSOC), and the 13C isotope composition of SOC (δ13CSOC)—using machine learning (ML) algorithms in the Central Andean Highlands of Peru, where grasslands and wetlands (“bofedales”) dominate the landscape surrounded by Junin National Reserve. A total of 198 soil samples (0.3 m depth) were collected to assess SOC variables. Four ML algorithms—random forest (RF), support vector machine (SVM), artificial neural networks (ANNs), and eXtreme gradient boosting (XGB)—were used to model SOC variables using remote sensing data, land-use and land-cover (LULC, nine categories), climate topography, and sampled physical–chemical soil variables. RF was the best algorithm for SOC and δ13CSOC prediction, whereas ANN was the best to model RSOC. “Bofedales” showed 2–3 times greater SOC (11.2 ± 1.60%) and RSOC (1.10 ± 0.23%) and more depleted δ13CSOC (− 27.0 ± 0.44 ‰) than other LULC, which reflects high C persistent, turnover rates, and plant productivity. This highlights the importance of “bofedales” as SOC reservoirs. LULC and vegetation indices close to the near-infrared bands were the most critical environmental predictors to model C variables SOC and δ13CSOC. In contrast, climatic indices were more important environmental predictors for RSOC. This study’s outcomes suggest the potential of ML methods, with a particular emphasis on RF, for mapping SOC and its fractions in the Andean highlands.

Modelling and mapping the abundance of lingonberry (Vaccinium vitis-idaea L.) in Norway

Lingonberry (Vaccinium vitis-idaea L.) grows in a range of nature types in the boreal zone, and understanding factors affecting the abundance of the plant, as well as mapping its spatial distribution, is important. The abundance of the species can be an indicator of ecosystem changes, and lingonberry can also be a source for commercial utilisation of berry resources. Using country-wide data from 6404 field plots of the Norwegian national forest inventory (NFI), we modelled the relationship between lingonberry cover and airborne laser scanning (ALS) and satellite metrics and bioclimatic variables describing the forest structure, terrain, soil properties and climate using a generalised mixed-effects model with a quasipoisson distribution. The validation carried out with an independent set of 2124 NFI plots indicated no obvious bias in predictions. The most important predictors were found to be interactions between dominant tree species, stand basal area and latitude, as well as the reflectance in the near-infrared band from Sentinel-2 satellite imagery, the dominant height based on the ALS variable and the long-term mean summer (June–August) temperature. The results provide an indicator of the effects of global warming, as well as the possibility of giving forest management prescriptions that favour lingonberry and locating the most abundant lingonberry sites in Norwegian forests.

Ultrafast near-infrared pyroelectric detector based on inhomogeneous plasmonic metasurface

Pyroelectric (PE) detection technologies have attracted extensive attention due to the cooling-free, bias-free, and broadband properties. However, the PE signals are generated by the continuous energy conversion processes from light, heat, to electricity, normally leading to very slow response speeds. Herein, we design and fabricate a PE detector which shows extremely fast response in near-infrared (NIR) band by combining with the inhomogeneous plasmonic metasurface. The plasmonic effect dramatically accelerates the light-heat conversion process, unprecedentedly improving the NIR response speed by 2−4 orders of magnitude to 22 μs, faster than any reported infrared (IR) PE detector. We also innovatively introduce the concept of time resolution into the field of PE detection, which represents the detector’s ability to distinguish multiple fast-moving targets. Furthermore, the spatially inhomogeneous design overcomes the traditional narrowband constraint of plasmonic systems and thus ensures a wideband response from visible to NIR. This study provides a promising approach to develop next-generation IR PE detectors with ultrafast and broadband responses.

A novel red-edge vegetable index for paddy rice mapping based on Sentinel-1/2 and GF-6 images

ABSTRACT Accurate paddy rice mapping is crucial for ensuring food security and guiding agricultural production. Vegetation indices are extensively employed to map paddy rice. However, most traditional normalized indices tend to be oversaturated during periods of lush vegetation due to normalization errors, resulting in uncertainties in paddy rice mapping. To address this issue, we introduce a novel red-edge rice index (RERI) in this study; this index comprises information from red, near-infrared, and red-edge bands without normalization. To extract single- and double-cropping rice features from potential rice areas, we employ GF-6 and Sentinel-2 images based on the proposed RERI and the random forest algorithm. The proposed method is validated in the Dingcheng District of Changde city, China, and the results are compared with those based on three normalized vegetation indices. The results show that the RERI yielded the highest levels of the accuracy for all the metrics, achieving an overall accuracy (OA) of 92.50% and a kappa coefficient of 0.8875. The RERI exhibited F1 scores of 92.26% for single-cropping rice, 93.00% for double-cropping rice, and 92.28% for non-rice areas. Our results highlight the potential of using the RERI for rice identification, and the effectiveness of our method for rice extraction is demonstrated.

Highly efficient tunable mid-infrared luminescence achieved by crystal field modulation of CsPb1-xHoxBr3 halide perovskite AlF3-based fluoride glass

Mid-infrared (MIR) light sources have gained significant importance across various applications in spectroscopy, sensing, astronomy, communications and medical surgery. The diverse spectral characteristics of rare earth ions, particularly lanthanide ions, stemming from their distinctive 4f intershell transitions, offer a multitude of potential transitions spanning the UV-visible-infrared spectrum. Despite recent advancements in MIR gain luminescence, the investigation of tunable MIR luminescence mechanisms remains a major technical challenge. Herein, an effective mechanism to modulate the local crystal field of rare earth ions by altering its crystal structure has been revealed, resulting in tunable broad-spectrum emission in the MIR luminescence range of 2800-3000 nm and multi-peak emission in the near-infrared band of Ho3+. Notably, the local crystal field of Ho3+ is adjusted by manipulating the lattice symmetry of CsPb1-xHoxBr3 perovskite through the incorporation of fluoride glass reticulation to control the crystal size of the perovskite and thereby modify the lattice symmetry of CsPb1-xHoxBr3 perovskite. The energy level transition of Ho3+ is influenced by adjusting the crystal field asymmetry, resulting in the splitting of the 5I6 energy level depending on the crystal field. This cleavage affects the transitions from the 5I5 level to 5I6 at 1480 nm and from 5I6 to 5I7 at 2880 nm. As 5I6 acts as the common upper level for the two emission peaks, the infrared peaks at 1480 nm and 2880 nm widen and develop into a dual-peak emission phenomenon. The infrared luminescence produced aligns closely with the distinctive infrared absorption peaks of carbon dioxide, leading to the development of a convenient, high-precision device for monitoring of CO2 concentration in hydrogen energy in real time. These findings are anticipated to pave the way for extensive utilization of novel tunable MIR luminescence.

Retina-Inspired Large-Area Solution-Processed flexible Multi-Band organic neuromorphic synaptic transistor arrays

The proliferation of high-efficacy, pliable organic neuromorphic synaptic transistors is quintessential for the integration into wearable artificial intelligence ecosystems. Nevertheless, their development is hindered by substantial energy demands and suboptimal charge carrier mobility. To surmount these obstacles, we exploit solution-processed poly(amic acid) (PAA) dielectrics and organic semiconductors to engineer pliable arrays of organic neuromorphic synaptic transistors atop a polyethylene terephthalate (PET) matrix. The polar group and nanogroove structure of PAA obtain excellent semiconductor crystal structure, and the excellent dielectric properties enable these engineered arrays to show a charge carrier mobility peak of 29.83 cm2 V−1 s−1 an aggregate mean mobility of 15.87 cm2 V−1 s−1 (standard deviation is 6.71) at a voltage of −3 V and the energy expenditure per synaptic transaction was only 0.26 fJ. Additionally, the transistors possess the aptitude for photodetection across ultraviolet–visible to near-infrared spectral bands. The successful execution of flexible imaging, Pavlovian classical conditioning reflex associative learning, coupled with a 93.9 % accuracy in handwritten numeral recognition, corroborates the viability of these high-mobility, low-power organic neuromorphic synaptic transistor arrays.

Broadband Near-Infrared Emission from Bi/Cr Co-Doped Aluminosilicate Glasses.

Bismuth-doped aluminosilicate glass has garnered significant attention due to its unique ultra-wide luminescence properties in the near-infrared (NIR) band. Enhancing the NIR luminescence of Bi-doped glass remains challenging. To achieve Bi-doped glass with more excellent luminescent properties, a series of Bi/Cr co-doped glasses were prepared, and the optical and structural properties of the samples were observed. The results indicate that low-concentration Cr doping broadens the luminescence range of Bi/Cr co-doped glass samples. The luminescence peak of Bi in the samples is at 1230 nm, while the peak of Cr is around 804 nm. The addition of an appropriate amount of Bi2O3 can enhance the NIR luminescence of Bi and Cr in the sample, realizing the energy conversion between Bi and Cr. Bi/Cr co-doped is a novel approach for achieving broadband NIR luminescence in glass materials.

A near-infrared metastructure to realizing polarization conversion and sensing based on the electromagnetic induced reflection

A multifunctional metastructure (MS) working in the near-infrared band is proposed that achieves linear to circular polarization conversion (LTCPC) and electromagnetically induced reflection (EIR). In addition, due to the use of reflected electric fields to overcome the influences of near-infrared band thermal radiation, MS has a good multifunctional sensing detection. The given MS is composed of a four-layer stacked structure. From the substrate to the top layer (along the +z-direction), it consists of silver, a silicon dioxide layer, and two layers of silicon (Si) resonators. Each layer of the Si resonator has four holes created by rotation. By breaking the symmetry of the upper and lower Si layers of MS, the deviation of the rotation angle of the upper and lower holes is introduced, so the MS achieved a near-infrared EIR with an ultrahigh quality factor (Q-factor). Under electromagnetic wave incidence, at 187.89 THz, the Q-factor of MS's reflection peak reaches 2441.25. Due to the unique shapes of the two resonator layers' holes, the axial ratio (AR) is less than 3 dB between 187.844 THz and 187.871 THz, realizing LTCPC. Utilizing the relationship between the optimal polarization conversion frequency point (the frequency corresponding to the lowest AR) and the environmental refractive index, the MS can be used for detecting cancer cells with a sensing sensitivity (S) of 37.24 THz/RIU (36.06 THz/RIU). By introducing the alcohol into the MS, the refractive index of the alcohol to be measured. MS achieved the detection function of the alcohol solution volume fraction, with an S reaching 33.32 THz/RIU, and a figure of merit as high as 154.202. Finally, the related parameters and polarization angle are discussed. The effects of different parameters on EIR and AR also are analyzed. The innovation of this paper lies in the realization of EIR, and a method to improve the Q-factor of near-infrared EIR by rotating the structure is proposed. The combination of a high Q-factor and polarization conversion overcomes the disadvantage that both are difficult to achieve together, and the EIR is easier to detect than the electromagnetically induced transparency. The design also has potential applications in biomedical sensors and industrial production.

A novel soybean mapping index within the global optimal time window

Efficient soybean mapping is critical for agricultural production and yield prediction. However, current sample-driven soybean mapping methods heavily rely on large representative sample datasets, limiting the interpretability of physical mechanisms. Besides, sample-free methods failed to exploit key features that differentiate soybean from other crops, especially Chlorophyll content. Misclassification errors persist and spatiotemporal generalization remains limited. Therefore, this study develops a novel Soybean Mapping Composite Index (SMCI) within a precise Global Optimal Time Window (GOTW). It integrates unique features of soybean Chlorophyll content, canopy water content, and canopy greenness by coupling three red-edge bands (RE2, RE3, and RE4), one near-infrared band, one shortwave infrared band, and two feature indices (Enhanced Vegetation Index and Green Chlorophyll Vegetation Index). The novel index was applied to soybean mapping at six sites in four major soybean producing countries (China, Argentina, Brazil, and the United States) from 2019 to 2021, using an optimal threshold of 3.25. Within the GOTW, the index responds better to spectral features and improves soybean separability. The average overall accuracy (OA: 91%) and average Kappa coefficient (Kappa: 0.83) for the novel index at all sites outperformed the traditional sample-driven Random Forest (RF) method (OA: 84%, Kappa: 0.70) and the existing sample-free index-based Greenness and Water Content Composite Index (GWCCI) (OA: 81%, Kappa: 0.64). Furthermore, interannual transfer experiments consistently showed high accuracy, demonstrating robust spatiotemporal transferability. The proposed SMCI index meets the need for a lightweight and stable soybean mapping tool and serves as a valuable reference for efficient global crop mapping.

Normalized Difference Red-NIR-SWIR: A new Sentinel-2 three-band spectral index for mapping freshly-opened swiddens in the tropics

Swidden agriculture is undergoing a rapid but overlooked transition and transformation in the tropics, complicating global carbon budgeting and sustainable livelihood assessment of swiddeners. Remotely sensed algorithms for accurately detecting swiddening practices have been slowly developed to generate annual updates on their dynamics. This is primarily because, using medium spatial resolution imagery (≥30 m), it is challenging to identify the exact boundary of swidden patches. Spectral-based approaches have by far dominated the detection and mapping of swidden agriculture, but the potential of Sentinel-2 has not been examined. To reconstruct annual information of swidden agriculture, a new Sentinel-2 three-band spectral index, i.e., the Normalized Difference Red, Near-infrared (NIR), and Shortwave-infrared (SWIR), or NDRII, has been developed to map freshly-opened swiddens in tropical regions. As Red (visible), NIR, and SWIR spectral band combinations (i.e., the VNIR-SWIR spectroscopy) are sensitive to vegetation-moisture variations and thermal anomalies caused by slash and burn in tropical uplands during the dry season, NDRII delineates exact patches of freshly opened swiddens. The latest 20-m map facilitates probing into the landscape patterns of newly opened swiddens and underlines their prevalence in Laos for the first time. Established within the VNIR-SWIR spectroscopy of Sentinel-2 and Landsat-8 Operational Land Imager, the NDRII algorithm contributes to reconstructing historical datasets of tropical swiddens via integrating state-of-the-art approaches that use temporally stacked observations with available VNIR/SWIR satellite imagery and further understanding the dynamics of landscape pattern and disturbance due to rapid transition and transformation.

Leveraging deep learning for coastal monitoring: A VGG16-based approach to spectral and textural classification of coastal areas with sentinel-2A data

Coastal ecosystems are vital for the planet's health, providing essential habitats for diverse species and supporting human communities. However, these complex environments face increasing threats from climate change and human activities. Effective monitoring of these areas requires large-scale, efficient, and accurate methods. This study explores the potential of deep learning for automated coastal land cover classification using Sentinel-2A satellite imagery on the Google Earth Engine (GEE) platform. We investigate the impact of transfer learning and spectral band combinations on classification accuracy for five coastal types: artificial, bedrock, sandy, muddy, and vegetation-covered. Our findings demonstrate that a pre-trained VGG16 Convolutional Neural Network (CNN) with transfer learning significantly improves classification accuracy (average 19.3% increase) compared to using default weights. Notably, including the near-infrared (NIR) band in training data leads to superior results, particularly for artificial and bedrock coastlines, where the NIR band's effectiveness in separating land-water boundaries enhances classification accuracy. These results highlight the potential of deep learning for large-scale, automated coastal monitoring, informing applications in sustainable fisheries management, coastal vulnerability assessment, and marine ecosystem conservation.

Carbonyl stretching band in amides as Lorentz oscillator. Insights into anharmonicity and local environment in the liquid phase from NIR and MIR spectra of N-methylformamide and di-N,N-methylformamide

We investigated the anharmonicity and intermolecular interactions of N-methylformamide (NMF) and di-N,N-methylformamide (DMF) in the neat liquid phase with particular interest in the amide bands. The vibrational spectra, complex refractive index, and complex electric permittivity were determined in in the mid- (MIR) and near-infrared (NIR) regions (11,500–560 cm−1; 870–17857 nm). Dispersion analysis was based on the Classical Damped Harmonic Oscillator (CDHO) and simultaneous modelling of the real and imaginary components of the spectra. This data delivered insights into the vibrational energy dissipation and self-association in liquid amides. Identification of the MIR and NIR bands was based on anharmonic GVPT2//B3LYP/6-311++G(d,p) calculations. DMF and NMF follow distinct self-association, evidenced in the MIR fingerprint by the two components of the νCO, the analog of the Amide I band. These conclusions are supported by the structural information derived from the NIR spectra. Furthermore, the contribution of overtones and combination bands in the MIR spectra of amides was examined. The conclusions on molecular interactions and structural dynamics of NMF and DMF contribute to a deeper understanding of the effects of changes in the local environment of the amide group.

Line-field dispersive interference ellipsometry based on an anisotropic crystal

We propose an anisotropic crystal-based line-field dispersive interference ellipsometry, capable of both transmission and reflection measurements. This technique is adept at characterizing the optical polarization properties of transparent materials and is applicable for real-time film thickness monitoring in industrial line-field configurations. Experimental demonstrations include the dynamic line-field birefringent phase of the quarter-wave plate and the Soleil-Babinet compensator in the near-infrared band. The developed method also accurately measured the thickness distribution of a conveyor-based film specimen with thicknesses of 250 nm and 510 nm. A phase stability of 9.5 × 10−4 rad under 200 Hz update rate was demonstrated.

A practical approach for extracting the photosystem II (PSII) contribution to near-infrared solar-induced chlorophyll fluorescence

Recent studies have indicated a good potential for using solar-induced chlorophyll fluorescence (SIF) to estimate photosynthetic CO2 assimilation. SIF can be emitted by both Photosystem I (PSI) and Photosystem II (PSII), but it is the SIF signals from PSII which are related to photosynthetic carbon fixation. However, since top-of-canopy SIF observations (SIFTOC) always contain contributions from both photosystems, to mechanistically estimate gross primary productivity (GPP) from SIF, it is essential to extract PSII SIF from SIFTOC. Based on the differences in the relative contribution of PSII across different wavelengths, we propose a practical approach for extracting PSII contribution to SIFTOC at the near-infrared (NIR) band (fPSII_760) using measurements of SIFTOC in the red and NIR spectral regions. A leaf-scale concurrent instrument was developed to assess the response of fPSII_760 under varying environments. For winter-wheat leaves, as light intensity increased from 0 to 400 μmol m−2 s−1, fPSII_760 rose from 0.6 to 0.8; with further increase in light intensity to 1800 μmol m−2 s−1, fPSII_760 consistently decreased to 0.65. There was a slight decreasing trend in fPSII_760 with rising temperatures, with values dropping from 0.65 at 15 °C to 0.61 at 40 °C. We found that variations in fPSII_760 are due to changes in the fluorescence yield of PSII, with the two having a positively proportional relationship. We also estimated canopy-scale fPSII_760 for a winter-wheat study site: fPSII_760 varied from 0.61 to 0.83, with a mean value of 0.78 during the peak growing season. A comparison with eddy covariance-derived GPP reveals that GPP estimated with dynamic fPSII_760 was more accurate than that calculated using fixed fPSII_760, with R2 increasing from 0.6 to 0.84. This study contributes to a deeper understanding of the link between SIF and photosynthetic CO2 assimilation, paving the way for more effective use of SIF to estimate GPP.

Study of Au nanoparticles adsorbed on InGaAs/InP thin films to improve optical absorption properties in the near-infrared band

Study of Au nanoparticles adsorbed on InGaAs/InP thin films to improve optical absorption properties in the near-infrared band

A 1.55 μm laser gain medium based on Yb3+ and Er3+ co-doped KBa0.94Ca0.06Y(MoO4)3 crystal with in-situ operating temperature self-monitoring capability

The 1.55 μm laser technology is widely applied in military, information communication, biomedicine and other fields. With the deepening development of these application areas, the demand for novel 1.55 μm laser gain media is becoming increasingly urgent. This study reports a novel Yb3+, Er3+ co-doped KBa0.94Ca0.06Y(MoO4)3 (KBCYM) crystal. In this crystal, Yb3+ serves as a sensitizer, significantly enhancing the emission intensity of Er3+ in both visible and near-infrared bands. Notably, when the concentration of Yb3+ reaches 6 mol%, the emission intensity peaks at 1.55 μm. Optical cross-section calculations reveal that the crystal exhibits a low laser pumping threshold at this concentration, demonstrating its potential as a laser gain medium. However, the crystal inevitably generates thermal effects during operation, which may adversely affect its performance. Therefore, real-time monitoring of the operating temperature is crucial. The thermal stability of the crystal was evaluated by measuring the temperature dependence of its luminescence intensity in the near-infrared band. Remarkably, even when the temperature rises to 553 K, the emission intensity at 1.55 μm only decreases by 10.9%. Additionally, the temperature sensing performance was evaluated using fluorescence intensity ratio techniques, yielding absolute and relative sensitivities of 0.00981 K–1 at 453 K and 1.32%/K at 303 K, respectively, highlighting its potential for optical temperature sensing. Finally, through leveraging the unique properties of Yb3+, Er3+: KBCYM crystals, we successfully developed 1.55 μm luminescent optical devices with practical applications. These devices not only exhibit efficient luminescent performance, but also possess a self-temperature measurement function, opening up new avenues for the further development of laser technology.

Inorganic Pb-Sn Perovskite Solar Cells with CF3-PA Internal Defect Passivation Strategy

Abstract The volatile organic components and heavy metal components in traditional perovskite solar cells (PSCs) have raised environmental and stability concerns for their large scale application. To solve the above two problems, the advancement of inorganic Pb-Sn PSCs technology has progressed at a remarkable pace over a relatively short period. Replacing Pb with Sn in PSCs not only significantly improves the environmental problems caused by the high toxicity of Pb, but also forms solar cells with lower bandgap, which is beneficial for light absorption and utilization, especially in the near-infrared band. In this work, a composite inorganic perovskite was utilized, CsPb0.6Sn0.4I3, and enhanced the PSCs performance by passivating the perovskite using 4-trifluoromethyl-phenylammonium (CF3-PA) additives. The highest efficiency of the champion device reached 13.04%, demonstrating its potential as a candidate for low bandgap PSCs.