Crystal Network Research Articles

Accelerating the Screening of Modified MA2Z4 Catalysts for Hydrogen Evolution Reaction by Deep Learning‐Based Local Geometric Analysis

Machine learning (ML) integrated with density functional theory (DFT) calculations have recently been used to accelerate the design and discovery of single‐atom catalysts (SACs) by establishing deep structure–activity relationships. The traditional ML models are always difficult to identify the structural differences among the single‐atom systems with different modification methods, leading to the limitation of the potential application range. Aiming to the structural properties of several typical two‐dimensional MA2Z4‐based single‐atom systems (bare MA2Z4 and metal single‐atom doped/supported MA2Z4), an improved crystal graph convolutional neural network (CGCNN) classification model was employed, instead of the traditional machine learning regression model, to address the challenge of incompatibility in the studied systems. The CGCNN model was optimized using crystal graph representation in which the geometric configuration was divided into active layer, surface layer, and bulk layer (ASB‐GCNN). Through ML and DFT calculations, five potential single‐atom hydrogen evolution reaction (HER) catalysts were screened from chemical space of 600 MA2Z4‐based materials, especially V1/HfSn2N4(S) with high stability and activity (ΔGH* is 0.06 eV). Further projected density of states (pDOS) analysis in combination with the wave function analysis of the SAC‐H bond revealed that the SAC‐dz2 orbital coincided with the H‐s orbital around the energy level of −2.50 eV, and orbital analysis confirmed the formation of σ bonds. This study provides an efficient multistep screening design framework of metal single‐atom catalyst for HER systems with similar two‐dimensional supports but different geometric configurations.

Regulation of fat crystals in water-in-oil emulsions by high-intensity ultrasound: Crystal size and tracing of droplet distribution

In this paper, two emulsion systems with high and low solid fat contents were prepared from 20 % water phase and 80 % oil phase by adjusting the palm oil/palm stearin/soybean oil ratio. Different ultrasonic power and time were used for the pretreatment of emulsion with different solid fat content, and the application characteristics of ultrasonic in W/O emulsions were explored and evaluated. Directly using high-intensity ultrasound to prepare fatty emulsions would weaken the hardness and storage modulus G' of the samples. Although ultrasound reduced the size of fat crystals in emulsions, the interaction between water droplets and fat crystals needs to be considered. After ultrasonic treatment, water droplets were difficult to immobilize on the crystal surface and thus acted as an active filler to stabilize the emulsion together with the fat crystal network. In high solid fat emulsion systems, an increase in ultrasound power (from 100 W to 200 W) could more affect the crystallization behavior of fats than an increase in ultrasound duration (from 30 s to 60 s), and the distribution of crystals and droplets was more uniform. In the low solid fat emulsion system, the texture of the sample after ultrasonic treatment was softer, and the surface was more delicate and smoother. However, the higher ultrasonic intensity (200 W) was not conducive to the preparation of the spread. Although the ultrasound with excessive intensity promoted the formation of small crystals, it would also lead to the aggregation of small crystals. These small crystals cannot form a uniform crystal network, which increases the fluidity of emulsions.

Machine learning-enabled chemical space exploration of all-inorganic perovskites for photovoltaics

The vast compositional and configurational spaces of multi-element metal halide perovskites (MHPs) result in significant challenges when designing MHPs with promising stability and optoelectronic properties. In this paper, we propose a framework for the design of B-site-alloyed ABX3 MHPs by combining density functional theory (DFT) and machine learning (ML). We performed generalized gradient approximation with Perdew–Burke–Ernzerhof functional for solids (PBEsol) on 3,159 B-site-alloyed perovskite structures using a compositional step of 1/4. Crystal graph convolution neural networks (CGCNNs) were trained on the 3159 DFT datasets to predict the decomposition energy, bandgap, and types of bandgaps. The trained CGCNN models were used to explore the compositional and configurational spaces of 41,400 B-site-alloyed ABX3 MHPs with a compositional step of 1/16, by accessing all possible configurations for each composition. The electronic band structures of the selected compounds were calculated using the hybrid functional (PBE0). Then, we calculated the optical absorption spectra and spectroscopic limited maximum efficiency of the selected compounds. Based on the DFT/ML-combined screening, 10 promising compounds with optimal bandgaps were selected, and from among these 10 compounds, CsGe0.3125Sn0.6875I3 and CsGe0.0625Pb0.3125Sn0.625Br3 were suggested as photon absorbers for single-junction and tandem solar cells, respectively. The design framework presented herein is a good starting point for the design of mixed MHPs for optoelectronic applications.

A Review on Development of Porous Aluminosilicate-Based Zeolite Adsorbent for Heavy Metal Pollution Treatment

Heavy metal contamination endangers human health and has been identified as a global issue. The rising concentration of heavy metals in industrial and domestic wastewater has prompted the quest for effective technologies and treatments to remove them. Zeolite is a promising material for eliminating heavy metals from diverse sources that pollute wastewater. Zeolite is a crystal comprised of hydrated aluminosilicates of alkali and alkaline earth metals in a threedimensional crystal network composed of easily controllable SiO4 4− and AlO4 5− . This review focuses on the critical analysis of the application of several zeolites for the adsorption of heavy metals from wastewater, namely Cd(II), As(III and V), Cr(III and VI), Cu(II), Pb(II), Ni(II), Mn(II), and Hg(II). The results of heavy metal removal reported by zeolites are summarized, and the chemical modification of zeolites with acid/base/salt reagents, surfactants, and metal reagents has been investigated, compared, and discussed. The adsorption/desorption capabilities, systems, operational parameters, isotherms, and kinetics of zeolites are then explained and compared.

From Nucleation to Fat Crystal Network: Effects of Stearic-Palmitic Sucrose Ester on Static Crystallization of Palm Oil.

Palm oil (PO), a semi-solid fat at room temperature, is a popular food ingredient. To steer the fat functionality, sucrose esters (SEs) are often used as food additives. Many SEs exist, varying in their hydrophilic-to-lipophilic balance (HLB), making them suitable for various food and non-food applications. In this study, a stearic-palmitic sucrose ester with a moderate HLB (6) was studied. It was found that the SE exhibited a complex thermal behavior consistent with smectic liquid crystals (type A). Small-angle X-ray scattering revealed that the mono- and poly-esters of the SE have different packings, more specifically, double and single chain-length packing. The polymorphism encountered upon crystallization was repeatable during successive heating and cooling cycles. After studying the pure SE, it was added to palm oil, and the crystallization behavior of the mixture was compared to that of pure palm oil. The crystallization conditions were varied by applying cooling at 20 °C/min (fast) and 1 °C/min (slow) to 0 °C, 20 °C or 25 °C. The samples were followed for one hour of isothermal time. Differential scanning calorimetry (DSC) showed that nucleation and polymorphic transitions were accelerated. Wide-angle X-ray scattering (WAXS) unraveled that the α-to-β' polymorphic transition remained present upon the addition of the SE. SAXS showed that the addition of the SE at 0.5 wt% did not significantly change the double chain-length packing of palm oil, but it decreased the domain size when cooling in a fast manner. Ultra-small-angle X-ray scattering (USAXS) revealed that the addition of the SE created smaller crystal nanoplatelets (CNPs). The microstructure of the fat crystal network was visualized by means of polarized light microscopy (PLM) and cryo-scanning electron microscopy (cryo-SEM). The addition of the SE created a finer and space-filling network without the visibility of separate floc structures.

On the structural and mechanical properties of mixed coconut and olive oil oleogels and bigels

The interaction of monoglycerides and phytosterols in olive- and coconut oil on the structuring of oleogels was analyzed. Specifically, bigels with gelatin hydrogel in different ratios (40:60 and 60:40 w/w) were formed. The physicochemical and microstructural attributes of these systems were assessed. The olive oil to coconut oil ratio (0–100 w/w) and the added oleogelators affected the crystal structure and the mechanical properties of the oleogels. Polarized light microscopy revealed that the addition of coconut oil created a denser triglycerides crystal network and the presence of phytosterols created more needle-like crystals, enhancing the textural properties of the oleogels and of the resulting bigels. The hardness of the oleogels ranged from 0.50 N to 1.24 N and for bigels was 5.96–36.75 N. Bigels hardness decreased as the oleogel ratio in the bigel increased. Microscopy and FTIR revealed that the addition of coconut oil in oleogels hampered the formation of a distinct crystalline monoglycerides network. Also, the absence of new peaks in the bigels indicated that the two structured phases interact with each other mostly physically, without the formation of new chemical bonds. Consequently, the oleogels and bigels developed, comprise a promising hard fat substitute with improved nutritional profile.

Conjugated safflower seed oil and its oleogels stabilization properties: Thermodynamic properties, rheology, oxidative stability

Conjugated safflower seed oil (CSSO) was used as the base oil, and beeswax (BW) was used as the gelling agent to prepare BW-CSSO oleogel. The effects of different additions of wax on the properties of BW-CSSO oleogel, as well as their oxidative stability, were investigated. The results indicated that an oleogel could be formed with CSSO at a BW concentration of 5 wt% at 25 °C, and the oil-holding capacity and hardness of the oleogel increased significantly as the BW concentration increased from 5 wt% to 11 wt%. Differential scanning calorimetry results showed that the peak crystallization and melting temperatures of the oleogel generally increased with the increase of wax addition, and both the crystallization and melting peaks became clearer and sharper. The rheological results indicated that all the samples showed shear-thinning behavior and higher concentrations of BW-CSSO oleogel were more capable of forming robust structures with lower frequency dependence. The free fatty acid release rate from BW-CSSO oleogel decreased with increasing BW concentration because the oleogel was stronger due to the formation of a more robust network, and the network of strong crystals influences the contact between lipase and oil, slowing down the release of oil from the interior of the oleogel structure and affecting the hydrolysis of oil. The network structure of the oleogel could inhibit the generation of CSSO secondary oxidation products and improve the oxidative stability of CSSO. This study provides the relevant parameters of the BW-CSSO oleogel and lays the foundation for subsequent studies.

Comparison of threshold algorithms for automatic processing of fat crystal microscopic images based on ImageJ

AbstractMicroscopic image analysis is a crucial tool in fat crystallization research, enabling the analysis of crystal size, network structure, fractal dimension and other parameters through binarization. It is essential to seek an appropriate thresholding algorithm to binarize fat crystal images, which plays a vital role in image segmentation. In this article, the effectiveness of 17 thresholding algorithms such as Default, Mean, IsoData, Otsu, Li and Triangle were analyzed in processing fat crystal images with different shapes, background colors and image intensities. This was expected to discover a stable and objective thresholding algorithm for the binarization of fat crystal images. The performance evaluation was conducted according to the peak signal noise ratio (PSNR), structural similarity index (SSIM) and region non‐uniformity (RNU) parameter. Moreover, the comparative analysis of crystal size error, crystal area fraction and intraclass correlation coefficients (ICC) for fractal dimension values would provide a foundation for the selection of thresholding techniques for fat crystal network images. The results indicated that the Default algorithm exhibited remarkable robustness and applicability with high‐quality and stable outputs in fat crystal image processing.

Dual-Wavelength Volumetric Microlithography for Rapid Production of 4D Microstructures.

4D microstructured actuators are micro-objects made of stimuli-responsive materials capable of induced shape deformations, with applications ranging from microrobotics to smart micropatterned haptic surfaces. The novel technology dual-wavelength volumetric microlithography (DWVML) realizes rapid printing of high-resolution 3D microstructures and so has the potential to pave the way to feasible manufacturing of 4D microdevices. In this work, DWVML is applied for the first time to printing stimuli-responsive materials, namely, liquid crystal networks (LCNs). An LCN photoresist is developed and characterized, and large arrays of up to 5625 LCN micropillars with programmable shape changes are produced by means of DWVML in the time span of seconds, over areas as large as ∼5.4 mm2. The production rate of 0.24 mm3 h-1 is achieved, exceeding speeds previously reported for additive manufacturing of LCNs by 2 orders of magnitude. Finally, a membrane with tunable, micrometer-sized pores is fabricated to illustrate the potential DWVML holds for real-world applications.

Splay aligned liquid crystal network photoactuators for integrated microfluidic pumps

As lab-on-a-chip devices aim to incorporate complex and multi-step microfluidic workflows, active fluid control within these platforms is critical. We use a splay aligned liquid crystal network (LCN) photoactuator, capable of deformation upon blue light illumination, as a stimuli responsive material to create integrated fluidic elements. After optimization of the manufacturing parameters, a well-defined actuator material is attained. Illumination with a 455 nm light emitting diode (LED) at 41 mW cm−2 achieves deflection of a 3 × 8 × 0.05 mm LCN film by 380 µm, generating forces of 1.1 mN. The actuator response is stable over time and scalable by light intensity, temperature, and size of the LCN film. By combining with a polydimethylsiloxane (PDMS) membrane, integration into a microfluidic chip is demonstrated and fluid movement of 77 nL in a 2 s stroke is attained. In a pumping setup, the LCN film functions as pumping membrane and two passive PDMS check valves complete the integrated micropump. Constant pumping rates of 0.1 µL min−1 are achieved. An advantage of this micropump setup lies in the non-contact actuation method, which allows for easy integration into microfluidic chips, without the need for chip-to-world connections. Furthermore, with the simple manufacturing procedure and the low operating power, important requirements for application in point-of-care settings are fulfilled.

Coarse-Grained Simulations on Polyethylene Crystal Network Formation and Microstructure Analysis.

Understanding and characterizing semi-crystalline models with crystalline and amorphous segments is crucial for industrial applications. A coarse-grained molecular dynamics (CGMD) simulations study probed the crystal network formation in high-density polyethylene (HDPE) from melt, and shed light on tensile properties for microstructure analysis. Modified Paul-Yoon-Smith (PYS/R) forcefield parameters are used to compute the interatomic forces among the PE chains. The isothermal crystallization at 300 K and 1 atm predicts the multi-nucleus crystal growth; moreover, the lamellar crystal stems and amorphous region are alternatively oriented. A one-dimensional density distribution along the alternative lamellar stems further confirms the ordering of the lamellar-stack orientation. Using this plastic model preparation approach, the semi-crystalline model density (ρcr) of ca. 0.913 g·cm-3 and amorphous model density (ρam) of ca. 0.856 g·cm-3 are obtained. Furthermore, the ratio of ρcr/ρam ≈ 1.06 is in good agreement with computational (≈1.096) and experimental (≈1.14) data, ensuring the reliability of the simulations. The degree of crystallinity (χc) of the model is ca. 52% at 300 K. Nevertheless, there is a gradual increase in crystallinity over the specified time, indicating the alignment of the lamellar stems during crystallization. The characteristic stress-strain curve mimicking tensile tests along the z-axis orientation exhibits a reversible sharp elastic regime, tensile strength at yield ca. 100 MPa, and a non-reversible tensile strength at break of 350%. The cavitation mechanism embraces the alignment of lamellar stems along the deformation axis. The study highlights an explanatory model of crystal network formation for the PE model using a PYS/R forcefield, and it produces a microstructure with ordered lamellar and amorphous segments with robust mechanical properties, which aids in predicting the microstructure-mechanical property relationships in plastics under applied forces.

Dry fractionation efficiency of milk fats from different sources and the characteristics of their fractions in chemical composition, thermal property, and crystal morphology

The physicochemical properties of anhydrous milk fats (AMF) often change according to different regions and seasons, inevitably affecting dry fractionation. This study analyzed the differences in the fraction yields and physicochemical characteristics of four AMFs from different sources. The results showed that single-stage dry fractionation conducted at 25 °C easily separated AMFs into liquid fractions (L25) and solid fractions (S25) via pressure filtration, both producing satisfactory yields. Moreover, all L25s exhibited few crystals with good fluidity at 25 °C, while S25s presented as semi-solids supported by β crystal networks with a certain hardness and plasticity. However, four AMFs displayed fractionation efficiency variation, while the thermal differences among them showed no obvious correlation with those among their fractions. Generally, more trisaturated triglycerides with 48 to 54 carbon atoms in the AMF increased the S25 yield and decreased the slip melting points (SMP) of both fractions.

Alterations in paraffin wax crystal networks induced by asphaltenes and pour point depressants, investigated by atomic force microscopy

Wax crystallization in pipelines in cold environments is a key factor affecting flow assurance during crude oil production. Polar crude oil components, such as asphaltenes, and polymeric pour point depressants (PPDs) have been demonstrated to interact with wax during gelation and crystallization through complex formation during nucleation or crystal growth alteration which leads to co-crystallization. This study introduces a methodology based on atomic force microscopy (AFM) to characterize features of solid surfaces precipitated from model wax, wax-PPD, wax-asphaltene and asphaltene systems. Height data collected from AFM measurements enabled the quantification of modifications in crystal sizes and shapes induced by the presence of asphaltenes and PPDs. The 2D Fourier transform of the height was used to generate statistics for the frequency of height amplitude, revealing information about the pore size and the height patterns. A stiffness/elasticity analysis was used to calculate the Young Modulus of the upper layers. In wax and wax-asphaltene systems, two nano-scale layers with different Young Modulus profiles emerged. The nano-scale layer on the top was softer and corresponded to more amorphous wax crystal networks with trapped dissolved wax. The nano-scale layer on the bottom was stiffer, comprising of more crystalline structures, corresponding to wax crystals with lower deviations from full crystallinity, with properties independent of asphaltene concentration. A bilayer did not form in the presence of PPD, which prevented the formation of amorphous phases with trapped dissolved wax or gel. After a methodology for model systems was established, the focus moved to systems based on production wax i.e. recovered from deposits. This was complemented by a comparison between model precipitation conditions and precipitation by cold finger, resembling industrial processes. Results highlighted the effect of change in chemical composition on topography, elasticity and adhesion of the surface. AFM provided new insights into wax crystallization patterns in the presence of inhibitors, which can be used to quantify the extent of wax-inhibitor interactions and project it on production systems. The method could be used to explain macro-scale rheological properties of oils during flow assurance in the future.

Two-dimensional silicon-based dielectric column photonic crystal point defect microcavity Neural network modelling

In order to solve the problem that it is difficult to predict the microcavity photonic energy bands of point defects in two-dimensional silicon-based dielectric column photonic crystals, this paper proposes a method to predict the microcavity photonic energy bands of point defects in two-dimensional silicon-based photonic crystals by using an artificial neural network model. In this paper, the energy band structure of triangular lattice point defects at different radii is calculated using MPB, and a neural network model is established based on this data and the accuracy of the established model is verified.

Photonic Interpenetrating Polymer Network Fibers Comprising Intertwined Solid-State Cholesteric Liquid Crystal and Polyelectrolyte Networks for Sensor Applications.

Uniform-sized photonic interpenetrating polymer network (IPN) fibers comprising intertwined solid-state cholesteric liquid crystal (CLCsolid) and anionic poly(acrylic acid) (PAA) or cationic poly(2-dimethylaminoethyl methacrylate) (PDMAEMA) networks (photonic IPNPAA or IPNPDMAEMA fibers) were developed for sensor applications. IPNPAA or IPNPDMAEMA fibers with a perfect photonic structure were fabricated inside Teflon tube templates without any treatments for realizing a planar orientation in those fibers. The dominant wavelength of the photonic color from a photograph taken with a cellular phone was used to measure the photonic color change. Photonic IPNPAA fibers treated with KOH (IPNKOH fibers) were used for sensing humidity and divalent metal ions. The linear ranges for relative humidity and Ca2+ detection were 21-92% and 0.5-3.5 mM, and their limits of detection (LODs) were 7.86% and 0.07 mM, respectively. The photonic IPNPAA (or IPNPDMAEMA) fiber immobilized with urease (IPNPAA-urease) (or glucose oxidase (IPNPDMAEMA-GOx)) was used for urea (or glucose) biosensor application. The photonic IPNPAA-urease (or IPNPDMAEMA-GOx) fiber was red-shifted in response to urea (or glucose) in the linear range of 10-60 mM (or 2-16 mM) with an LOD of 2.54 mM (or 0.76 mM). These photonic IPN fibers are promising because of their easy fabrication and miniaturization, battery-free device, cost-effectiveness, and visual detection without using sophisticated analytical instruments. The developed photonic IPN fibers provide new possibilities for the widespread use of photonic sensors in cutting-edge wearable technology and beyond.

Effects of fat unsaturation degree on whipping performance and foam stability of fat‐reduced aerated emulsions

SummaryAerated emulsions formulated with vegetable oils have been widely consumed with health demands of tea‐based drinks and coffee. In comparison with its traditional counterparts consisting of 30%–40% of fats, that is, whipping creams, the aerated emulsions containing less than 20% vegetable oils are classified as low fat‐cream‐like products and popular with consumers preferring fat‐reduced foods. However, unstable whipping capabilities are the typical quality defects of these low fat‐emulsions, which is highly related with partial coalescence of fat globules. In the present study, effects of unsaturation degree and fatty acid species of fat matrix on whipping capabilities of the aerated emulsions were evaluated by analysing crystal networks, partial coalescence behaviours, whipping performance, and foam structure and stabilities. Increase of fat unsaturation from 10% to 30% improved fat crystal structure and partial coalescence significantly. In particular, 10%–15% of medium chain‐fatty acids present in the emulsions with 30% unsaturated fats tended to crystallise into well‐packed thicker crystals, and to exhibit the highest partial coalescence rate with nearly 60% of solid fat content at whipping temperature. Both the fat species and unsaturation contributed to forming smooth appearance and desirable foam networks after whipping. In contrast, further increase in unsaturation resulted in significant reduction of adsorbed proteins, contributing to excessive aggregation of fat globules. Therefore, it presented negative effects on partial coalescence as well as foam firmness and stabilities. The results figured out characteristics of fat matrix for the manufacture of low fat‐aerated creams with improved whipping capabilities.

Ostwald ripening for designing time-dependent crystal hydrogels.

Ostwald ripening (OR), a classic solution theory describing molecular transfer from metastable crystal to stable one, is applied to design time-dependent crystal hydrogels that can automatically change their mechanical properties. Using a system made from crosslinked polyacrylamide (PAM) and sodium acetate (NaAc), we demonstrate that metastable fibrous crystal networks of NaAc preferably form in PAM hydrogels via a polymer-involving mismatch nucleation. These fibrous crystals would undergo OR and evolve into isolated bulk crystals, leading to a significant reduction in material rigidity (179 folds) and interfacial adhesion (20 folds). This transformation can be applied to program time-dependent self-recovery in shape and self-delamination. Since OR is a ubiquitous, robust feature of various crystals, the approach reported here represents a new direction for designing advanced transient soft materials.

Ostwald ripening for designing time‐dependent crystal hydrogels

AbstractOstwald ripening (OR), a classic solution theory describing molecular transfer from metastable crystal to stable one, is applied to design time‐dependent crystal hydrogels that can automatically change their mechanical properties. Using a system made from crosslinked polyacrylamide (PAM) and sodium acetate (NaAc), we demonstrate that metastable fibrous crystal networks of NaAc preferably form in PAM hydrogels via a polymer‐involving mismatch nucleation. These fibrous crystals would undergo OR and evolve into isolated bulk crystals, leading to a significant reduction in material rigidity (179 folds) and interfacial adhesion (20 folds). This transformation can be applied to program time‐dependent self‐recovery in shape and self‐delamination. Since OR is a ubiquitous, robust feature of various crystals, the approach reported here represents a new direction for designing advanced transient soft materials.

Large-Scale, Vertically Aligned 2D Subnanochannel Arrays by a Smectic Liquid Crystal Network for High-Performance Osmotic Energy Conversion.

The osmotic energy, an abundant renewable energy source, can be directly converted to electricity by nanofluidic devices with ion-selective membranes. 2D nanochannels constructed by nanosheets possess abundant lateral interfacial ion-exchange sites and exhibit great superiority in nanofluidic devices. However, the most accessible orientation of the 2D nanochannels is parallel to the membrane surface, undoubtedly resulting in the conductivity loss. Herein, first vertically aligned 2D subnanochannel arrays self-assembled by a smectic liquid crystal (LC) network that exhibit high-performance osmotic energy conversion are demonstrated. The 2D subnanochannel arrays are fabricated by in situ photopolymerization of monomers in the LC phase. The as-prepared membrane exhibits excellent water-resistance and mechanical strength. The 2D subnanochannels with excellent cation selectivity and conductivity show high-performance osmotic energy conversion. The power density reaches up to about 22.5W m-2 with NaCl solution under a 50-fold concentration gradient, which is among with ultrahigh power density. This membrane design concept provides promising applications in osmotic energy conversion.

Circularly Polarized Luminescence of Cholesteric Liquid Crystal Polymer Networks with a Europium(III) Complex as an Emitter.

A coordination complex, Eu(C12C12dbm)3(phen), with strong emission and a high quantum yield (QY ∼ 51.9%) was synthesized. The EuIII complex, as a fluorescent emitter, was embedded in cholesteric liquid crystal polymer networks (CLCNs). A series of free-standing EuIII-CLCN films were obtained, generating a typical sharp emission band corresponding to the EuIII complex. Tunable handedness of circularly polarized luminescence (CPL) with high |glum| values (up to 0.63) was observed. A series of CPL-active CLCN-coated PET films were also prepared (|glum| values up to 0.63), which can be used for large-area preparations. Moreover, by stacking an emitter-embedded PMMA layer and a CLCN layer, a composite system was built, and a large |glum| value (∼1.42) was achieved. Fluorescence patterns were prepared, and distinct images of CLCN films were recognized under both daylight and UV light. This work not only demonstrated that coordination compounds could be incorporated with CLCN films to prepare CPL-active materials with high |glum| values but also provided a new perspective for emissive CLCN materials used for anticounterfeiting and encryption.