Economic and Sustainability Impacts of Yield and Composition Variation in Bioenergy Crops: Switchgrass (Panicum virgatum L.).

Economically viable production of biobased products and fuels requires high-yielding, high-quality, sustainable process-advantaged crops, developed using bioengineering or advanced breeding approaches. Understanding which crop phenotypic traits have the largest impact on biofuel economics and sustainability outcomes is important for the targeted feedstock crop development. Here, we evaluated biomass yield and cell-wall composition traits across a large natural variant population of switchgrass (Panicum virgatum L.) grown across three common garden sites. Samples from 331 switchgrass genotypes were collected and analyzed for carbohydrate and lignin components. Considering plant survival and biomass after multiple years of growth, we found that 84 of the genotypes analyzed may be suited for commercial production in the southeastern U.S. These genotypes show a range of growth and compositional traits across the population that are apparently independent of each other. We used these data to conduct techno-economic analyses and life cycle assessments evaluating the performance of each switchgrass genotype under a standard cellulosic ethanol process model with pretreatment, added enzymes, and fermentation. We find that switchgrass yield per area is the largest economic driver of the minimum fuel selling price (MSFP), ethanol yield per hectare, global warming potential (GWP), and cumulative energy demand (CED). At any yield, the carbohydrate content is significant but of secondary importance. Water use follows similar trends but has more variability due to an increased dependence on the biorefinery model. Analyses presented here highlight the primary importance of plant yield and the secondary importance of carbohydrate content when selecting a feedstock that is both economical and sustainable.

The Genetic Architecture of Nitrogen Use Efficiency in Switchgrass (Panicum virgatum L.).

Switchgrass (Panicum virgatum L.) has immense potential as a bioenergy crop with the aim of producing biofuel as an end goal. Nitrogen (N)-related sustainability traits, such as nitrogen use efficiency (NUE) and nitrogen remobilization efficiency (NRE), are important factors affecting switchgrass quality and productivity. Hence, it is imperative to develop nitrogen use-efficient switchgrass accessions by exploring the genetic basis of NUE in switchgrass. For that, we used 331 diverse field-grown switchgrass accessions planted under low and moderate N fertility treatments. We performed a genome wide association study (GWAS) in a holistic manner where we not only considered NUE as a single trait but also used its related phenotypic traits, such as total dry biomass at low N and moderate N, and nitrogen use index, such as NRE. We have evaluated the phenotypic characterization of the NUE and the related traits, highlighted their relationship using correlation analysis, and identified the top ten nitrogen use-efficient switchgrass accessions. Our GWAS analysis identified 19 unique single nucleotide polymorphisms (SNPs) and 32 candidate genes. Two promising GWAS candidate genes, caffeoyl-CoA O-methyltransferase (CCoAOMT) and alfin-like 6 (AL6), were further supported by linkage disequilibrium (LD) analysis. Finally, we discussed the potential role of nitrogen in modulating the expression of these two genes. Our findings have opened avenues for the development of improved nitrogen use-efficient switchgrass lines.

Multiomics approach reveals a role of translational machinery in shaping maize kernel amino acid composition.

Maize (Zea mays) seeds are a good source of protein, despite being deficient in several essential amino acids. However, eliminating the highly abundant but poorly balanced seed storage proteins has revealed that the regulation of seed amino acids is complex and does not rely on only a handful of proteins. In this study, we used two complementary omics-based approaches to shed light on the genes and biological processes that underlie the regulation of seed amino acid composition. We first conducted a genome-wide association study to identify candidate genes involved in the natural variation of seed protein-bound amino acids. We then used weighted gene correlation network analysis to associate protein expression with seed amino acid composition dynamics during kernel development and maturation. We found that almost half of the proteome was significantly reduced during kernel development and maturation, including several translational machinery components such as ribosomal proteins, which strongly suggests translational reprogramming. The reduction was significantly associated with a decrease in several amino acids, including lysine and methionine, pointing to their role in shaping the seed amino acid composition. When we compared the candidate gene lists generated from both approaches, we found a nonrandom overlap of 80 genes. A functional analysis of these genes showed a tight interconnected cluster dominated by translational machinery genes, especially ribosomal proteins, further supporting the role of translation dynamics in shaping seed amino acid composition. These findings strongly suggest that seed biofortification strategies that target the translation machinery dynamics should be considered and explored further.

Sustainability Trait Modeling of Field-Grown Switchgrass (Panicum virgatum) Using UAV-Based Imagery.

Unmanned aerial vehicles (UAVs) provide an intermediate scale of spatial and spectral data collection that yields increased accuracy and consistency in data collection for morphological and physiological traits than satellites and expanded flexibility and high-throughput compared to ground-based data collection. In this study, we used UAV-based remote sensing for automated phenotyping of field-grown switchgrass (Panicum virgatum), a leading bioenergy feedstock. Using vegetation indices calculated from a UAV-based multispectral camera, statistical models were developed for rust disease caused by Puccinia novopanici, leaf chlorophyll, nitrogen, and lignin contents. For the first time, UAV remote sensing technology was used to explore the potentials for multiple traits associated with sustainable production of switchgrass, and one statistical model was developed for each individual trait based on the statistical correlation between vegetation indices and the corresponding trait. Also, for the first time, lignin content was estimated in switchgrass shoots via UAV-based multispectral image analysis and statistical analysis. The UAV-based models were verified by ground-truthing via correlation analysis between the traits measured manually on the ground-based with UAV-based data. The normalized difference red edge (NDRE) vegetation index outperformed the normalized difference vegetation index (NDVI) for rust disease and nitrogen content, while NDVI performed better than NDRE for chlorophyll and lignin content. Overall, linear models were sufficient for rust disease and chlorophyll analysis, but for nitrogen and lignin contents, nonlinear models achieved better results. As the first comprehensive study to model switchgrass sustainability traits from UAV-based remote sensing, these results suggest that this methodology can be utilized for switchgrass high-throughput phenotyping in the field.

Copper Tetracyanoquinodimethane (CuTCNQ): A Metal-Organic Semiconductor for Room-Temperature Visible to Long-Wave Infrared Photodetection.

Mid-wave and long-wave infrared (MWIR and LWIR) detection play vital roles in applications that include health care, remote sensing, and thermal imaging. However, detectors in this spectral range often require complex fabrication processes and/or cryogenic cooling and are typically expensive, which motivates the development of simple alternatives. Here, we demonstrate broadband (0.43-10 µm) room-temperature photodetection based on copper tetracyanoquinodimethane (CuTCNQ), a metal-organic semiconductor, synthesized via a facile wet-chemical reaction. The CuTCNQ crystals are simply drop-cast onto interdigitated electrode chips to realize photoconductors. The photoresponse is governed by a combination of interband (0.43-3.35 µm) and midgap (3.35-10 µm) transitions. The devices show response times (∼365 µs) that would be sufficient for many infrared applications (e.g., video rate imaging), with a frequency cutoff point of 1 kHz.

Long-Wave Infrared Photodetectors Based on 2D Platinum Diselenide atop Optical Cavity Substrates.

Long-wave infrared (LWIR) photodetection is of high technological importance, having a wide range of applications that include thermal imaging and spectroscopy. Two-dimensional (2D) noble-transition-metal dichalcogenides, platinum diselenide (PtSe2) in particular, have recently shown great promise for infrared detection. However, previous studies have mainly focused on wavelengths up to the short-wave infrared region. In this work, we demonstrate LWIR photodetectors based on multilayer PtSe2. In addition, we present an optical cavity substrate that enhances the light-matter interaction in 2D materials and thus their photodetection performance in the LWIR spectral region. The PtSe2 photoconductors fabricated on the TiO2/Au optical cavity substrate exhibit responsivities up to 54 mA/W to LWIR illumination at a wavelength of 8.35 µm. Moreover, these devices show a fast photoresponse with a time constant of 54 ns to white light illumination. The findings of this study reveal the potential of multilayer PtSe2 for fast and broadband photodetection from visible to LWIR wavelengths.

Evaporated Sex Te1- x Thin Films with Tunable Bandgaps for Short-Wave Infrared Photodetectors.

Semiconducting absorbers in high-performance short-wave infrared (SWIR) photodetectors and imaging sensor arrays are dominated by single-crystalline germanium and III-V semiconductors. However, these materials require complex growth and device fabrication procedures. Here, thermally evaporated Sex Te1- x alloy thin films with tunable bandgaps for the fabrication of high-performance SWIR photodetectors are reported. From absorption measurements, it is shown that the bandgaps of Sex Te1- x films can be tuned continuously from 0.31 eV (Te) to 1.87 eV (Se). Owing to their tunable bandgaps, the peak responsivity position and photoresponse edge of Sex Te1- x film-based photoconductors can be tuned in the SWIR regime. By using an optical cavity substrate consisting of Au/Al2 O3 to enhance its absorption near the bandgap edge, the Se0.32 Te0.68 film (an optical bandgap of ≈0.8 eV)-based photoconductor exhibits a cut-off wavelength at ≈1.7 µm and gives a responsivity of 1.5 AW-1 and implied detectivity of 6.5 × 1010 cm Hz1/2 W-1 at 1.55 µm at room temperature. Importantly, the nature of the thermal evaporation process enables the fabrication of Se0.32 Te0.68 -based 42 × 42 focal plane arrays with good pixel uniformity, demonstrating the potential of this unique material system used for infrared imaging sensor systems.

HAPPI GWAS: Holistic Analysis with Pre- and Post-Integration GWAS.

MOTIVATION: Advanced publicly available sequencing data from large populations have enabled informative genome-wide association studies (GWAS) that associate SNPs with phenotypic traits of interest. Many publicly available tools able to perform GWAS have been developed in response to increased demand. However, these tools lack a comprehensive pipeline that includes both pre-GWAS analysis, such as outlier removal, data transformation and calculation of Best Linear Unbiased Predictions or Best Linear Unbiased Estimates. In addition, post-GWAS analysis, such as haploblock analysis and candidate gene identification, is lacking. RESULTS: Here, we present Holistic Analysis with Pre- and Post-Integration (HAPPI) GWAS, an open-source GWAS tool able to perform pre-GWAS, GWAS and post-GWAS analysis in an automated pipeline using the command-line interface. AVAILABILITY AND IMPLEMENTATION: HAPPI GWAS is written in R for any Unix-like operating systems and is available on GitHub (https://github.com/Angelovici-Lab/HAPPI.GWAS.git). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

mGWAS Uncovers Gln-Glucosinolate Seed-Specific Interaction and its Role in Metabolic Homeostasis.

Gln is a key player in plant metabolism. It is one of the major free amino acids that is transported into the developing seed and is central for nitrogen metabolism. However, Gln natural variation and its regulation and interaction with other metabolic processes in seeds remain poorly understood. To investigate the latter, we performed a metabolic genome-wide association study (mGWAS) of Gln-related traits measured from the dry seeds of the Arabidopsis (Arabidopsis thaliana) diversity panel using all potential ratios between Gln and the other members of the Glu family as traits. This semicombinatorial approach yielded multiple candidate genes that, upon further analysis, revealed an unexpected association between the aliphatic glucosinolates (GLS) and the Gln-related traits. This finding was confirmed by an independent quantitative trait loci mapping and statistical analysis of the relationships between the Gln-related traits and the presence of specific GLS in seeds. Moreover, an analysis of Arabidopsis mutants lacking GLS showed an extensive seed-specific impact on Gln levels and composition that manifested early in seed development. The elimination of GLS in seeds was associated with a large effect on seed nitrogen and sulfur homeostasis, which conceivably led to the Gln response. This finding indicates that both Gln and GLS play key roles in shaping the seed metabolic homeostasis. It also implies that select secondary metabolites might have key functions in primary seed metabolism. Finally, our study shows that an mGWAS performed on dry seeds can uncover key metabolic interactions that occur early in seed development.

Mid- to long-wave infrared computational spectroscopy with a graphene metasurface modulator.

In recent years there has been much interest concerning the development of modulators in the mid- to long-wave infrared, based on emerging materials such as graphene. These have been frequently pursued for optical communications, though also for other specialized applications such as infrared scene projectors. Here we investigate a new application for graphene modulators in the mid- to long-wave infrared. We demonstrate, for the first time, computational spectroscopy in the mid- to long-wave infrared using a graphene-based metasurface modulator. Furthermore, our metasurface device operates at low gate voltage. To demonstrate computational spectroscopy, we provide our algorithm with the measured reflection spectra of the modulator at different gate voltages. We also provide it with the measured reflected light power as a function of the gate voltage. The algorithm then estimates the input spectrum. We show that the reconstructed spectrum is in good agreement with that measured directly by a Fourier transform infrared spectrometer, with a normalized mean-absolute-error (NMAE) of 0.021.

Can metabolic tightening and expansion of co-expression network play a role in stress response and tolerance?

Plants respond and adapt to changes in their environment by employing a wide variety of genetic, molecular, and biochemical mechanisms. When so doing, they trigger large-scale rearrangements at the metabolic and transcriptional levels. The dynamics and patterns of these rearrangements and how they govern a stress response is not clear. In this opinion, we discuss a plant's response to stress from the perspective of the metabolic gene co-expression network and its rearrangement upon stress. As a case study, we use publicly available expression data of Arabidopsis thaliana plants exposed to heat and drought stress to evaluate and compare the co-expression networks of metabolic genes. The analysis highlights that stress conditions can lead to metabolic tightening and expansion of the co-expression network. We argue that this rearrangement could play a role in a plant's response to stress and thus may be an additional tool to assess and understand stress tolerance/sensitivity. Additional studies are needed to evaluate the metabolic network in response to multiple stresses at various intensities and across different genetic backgrounds (e.g., intra- and inter-species, sensitive and tolerant eco/genotypes).

The complex response of free and bound amino acids to water stress during the seed setting stage in Arabidopsis.

Free amino acids (FAAs) and protein-bound amino acids (PBAAs) in seeds play an important role in seed desiccation, longevity, and germination. However, the effect that water stress has on these two functional pools, especially when imposed during the crucial seed setting stage is unclear. To better understand these effects, we exposed Arabidopsis plants at the seed setting stage to a range of water limitation and water deprivation conditions and then evaluated physiological, metabolic, and proteomic parameters, with special focus on FAAs and PBAAs. We found that in response to severe water limitation, seed yield decreased, while seed weight, FAA, and PBAA content per seed increased. Nevertheless, the composition of FAAs and PBAAs remained unaltered. In response to severe water deprivation, however, both seed yield and weight were reduced. In addition, major alterations were observed in both FAA and proteome compositions, which indicated that both osmotic adjustment and proteomic reprogramming occurred in these naturally desiccation-tolerant organs. However, despite the major proteomic alteration, the PBAA composition did not change, suggesting that the proteomic reprogramming was followed by a proteomic rebalancing. Proteomic rebalancing has not been observed previously in response to stress, but its occurrence under stress strongly suggests its natural function. Together, our data show that the dry seed PBAA composition plays a key role in seed fitness and therefore is rigorously maintained even under severe water stress, while the FAA composition is more plastic and adaptable to changing environments, and that both functional pools are distinctly regulated.

Single-plant GWAS coupled with bulk segregant analysis allows rapid identification and corroboration of plant-height candidate SNPs.

BACKGROUND: Genome wide association studies (GWAS) are a powerful tool for identifying quantitative trait loci (QTL) and causal single nucleotide polymorphisms (SNPs)/genes associated with various important traits in crop species. Typically, GWAS in crops are performed using a panel of inbred lines, where multiple replicates of the same inbred are measured and the average phenotype is taken as the response variable. Here we describe and evaluate single plant GWAS (sp-GWAS) for performing a GWAS on individual plants, which does not require an association panel of inbreds. Instead sp-GWAS relies on the phenotypes and genotypes from individual plants sampled from a randomly mating population. Importantly, we demonstrate how sp-GWAS can be efficiently combined with a bulk segregant analysis (BSA) experiment to rapidly corroborate evidence for significant SNPs. RESULTS: In this study we used the Shoepeg maize landrace, collected as an open pollinating variety from a farm in Southern Missouri in the 1960's, to evaluate whether sp-GWAS coupled with BSA can efficiently and powerfully used to detect significant association of SNPs for plant height (PH). Plant were grown in 8 locations across two years and in total 768 individuals were genotyped and phenotyped for sp-GWAS. A total of 306 k polymorphic markers in 768 individuals evaluated via association analysis detected 25 significant SNPs (P ≤ 0.00001) for PH. The results from our single-plant GWAS were further validated by bulk segregant analysis (BSA) for PH. BSA sequencing was performed on the same population by selecting tall and short plants as separate bulks. This approach identified 37 genomic regions for plant height. Of the 25 significant SNPs from GWAS, the three most significant SNPs co-localize with regions identified by BSA. CONCLUSION: Overall, this study demonstrates that sp-GWAS coupled with BSA can be a useful tool for detecting significant SNPs and identifying candidate genes. This result is particularly useful for species/populations where association panels are not readily available.

Mid- to long-wave infrared computational spectroscopy using a subwavelength coaxial aperture array.

Miniaturized spectrometers are advantageous for many applications and can be achieved by what we term the filter-array detector-array (FADA) approach. In this method, each element of an optical filter array filters the light that is transmitted to the matching element of a photodetector array. By providing the outputs of the photodetector array and the filter transmission functions to a reconstruction algorithm, the spectrum of the light illuminating the FADA device can be estimated. Here, we experimentally demonstrate an array of 101 band-pass transmission filters that span the mid- to long-wave infrared (6.2 to 14.2 µm). Each filter comprises a sub-wavelength array of coaxial apertures in a gold film. As a proof-of-principle demonstration of the FADA approach, we use a Fourier transform infrared (FTIR) microscope to record the optical power transmitted through each filter. We provide this information, along with the transmission spectra of the filters, to a recursive least squares (RLS) algorithm that estimates the incident spectrum. We reconstruct the spectrum of the infrared light source of our FTIR and the transmission spectra of three polymer-type materials: polyethylene, cellophane and polyvinyl chloride. Reconstructed spectra are in very good agreement with those obtained via direct measurement by our FTIR system.

Structural color filters based on an all-dielectric metasurface exploiting silicon-rich silicon nitride nanodisks.

An all-dielectric metasurface is deemed to serve a potential platform to demonstrate spectral filters. Silicon-rich silicon nitride (SRN), which contains a relatively large portion of silicon, can exhibit higher refractive indices, when compared to silicon nitride. Meanwhile, the extinction coefficient of SRN is smaller than that of hydrogenated amorphous silicon, leading to reduced absorption loss in the shorter wavelength. SRN is therefore recommended as a scattering element from the perspective of realizing all-dielectric metasurfaces. In this work, we propose and embody a suite of highly efficient structural color filters, capitalizing on a dielectric metasurface that consists of a two-dimensional array of SRN nanodisks that are embedded in a polymeric layer. The SRN nanodisks may support the electric dipole (ED) and magnetic dipole (MD) resonances via Mie scattering, thereby leading to appropriate spectral filtering characteristics. The ED and MD are identified from field profile observation with the assistance of finite-difference time-domain simulations. The manufactured color filters are observed to produce various colors in both transmission and reflection modes throughout the visible band, giving rise to a high transmission of around 90% in the off-resonance region and a reflection ranging up to 60%. A variety of colors can be realized by tuning the resonance by adjusting the structural parameters such as the period, diameter, and height of the SRN nanodisks. The spectral position of resonances might be flexibly tuned by tailoring the polymer surrounding the SRN nanodisks. It is anticipated that the proposed coloring devices will be actively used for color displays, imaging devices, and photorealistic color printing.

Experimental demonstration of infrared spectral reconstruction using plasmonic metasurfaces.

We computationally reconstruct short- to long-wave infrared spectra using an array of plasmonic metasurface filters. We illuminate the filter array with an unknown spectrum and measure the optical power transmitted through each filter with an infrared microscope to emulate a filter-detector array system. We then use the recursive least squares method to determine the unknown spectrum. We demonstrate our method with light from a blackbody. We also demonstrate it with spectra generated by passing the light from the blackbody through various materials. Our approach is a step towards miniaturized spectrometers spanning the short- to long-wave infrared based on filter-detector arrays.

Solution-Synthesized High-Mobility Tellurium Nanoflakes for Short-Wave Infrared Photodetectors.

Two-dimensional (2D) materials, particularly black phosphorus (bP), have demonstrated themselves to be excellent candidates for high-performance infrared photodetectors and transistors. However, high-quality bP can be obtained only via mechanical exfoliation from high-temperature- and high-pressure-grown bulk crystals and degrades rapidly when exposed to ambient conditions. Here, we report solution-synthesized and air-stable quasi-2D tellurium (Te) nanoflakes for short-wave infrared (SWIR) photodetectors. We perform comprehensive optical characterization via polarization-resolved transmission and reflection measurements and report the absorbance and complex refractive index of Te crystals. It is found that this material is an indirect semiconductor with a band gap of 0.31 eV. From temperature-dependent electrical measurements, we confirm this band-gap value and find that 12 nm thick Te nanoflakes show high hole mobilities of 450 and 1430 cm2 V-1 s-1 at 300 and 77 K, respectively. Finally, we demonstrate that despite its indirect band gap, Te can be utilized for high-performance SWIR photodetectors by employing optical cavity substrates consisting of Au/Al2O3 to dramatically increase the absorption in the semiconductor. By changing the thickness of the Al2O3 cavity, the peak responsivity of Te photoconductors can be tuned from 1.4 µm (13 A/W) to 2.4 µm (8 A/W) with a cutoff wavelength of 3.4 µm, fully capturing the SWIR band. An optimized room-temperature specific detectivity ( D*) of 2 × 109 cm Hz1/2 W-1 is obtained at a wavelength of 1.7 µm.

All Dielectric Transmissive Structural Multicolor Pixel Incorporating a Resonant Grating in Hydrogenated Amorphous Silicon.

All dielectric transmissive type polarization-tuned structural multicolor pixels (MCPs) are proposed and demonstrated based on a one-dimensional hydrogenated amorphous silicon (a-Si:H) grating integrated with a silicon nitride waveguide. Both bandpass and bandstop transmission filtering characteristics in the visible regime, centered at the same wavelength, have been achieved by tailoring the structural parameters including the duty ratio of the grating and the thickness of the dielectric waveguide. For the three manufactured MCPs, the transmission peak exceeds 70% for the transverse electric (TE) polarization and 90% for the transverse magnetic (TM) polarization as observed at the resonance and off-resonance wavelength, respectively. The polarization-switched transmissions are attributed to the guided mode resonance initiated by the interaction of the a-Si:H grating and the dielectric waveguide. A broad color palette covering the entire visible band was successfully realized from a suite of MCPs with varying grating pitches. The proposed structural color pixels are expected to facilitate the construction of dynamic displays, image sensors, optical data storage, security tags, and so forth.

Structural Color Filters Enabled by a Dielectric Metasurface Incorporating Hydrogenated Amorphous Silicon Nanodisks.

It is advantageous to construct a dielectric metasurface in silicon due to its compatibility with cost-effective, mature processes for complementary metal-oxide-semiconductor devices. However, high-quality crystalline-silicon films are difficult to grow on foreign substrates. In this work, we propose and realize highly efficient structural color filters based on a dielectric metasurface exploiting hydrogenated amorphous silicon (a-Si:H), known to be lossy in the visible regime. The metasurface is comprised of an array of a-Si:H nanodisks embedded in a polymer, providing a homogeneously planarized surface that is crucial for practical applications. The a-Si:H nanodisk element is deemed to individually support an electric dipole (ED) and magnetic dipole (MD) resonance via Mie scattering, thereby leading to wavelength-dependent filtering characteristics. The ED and MD can be precisely identified by observing the resonant field profiles with the assistance of finite-difference time-domain simulations. The completed color filters provide a high transmission of around 90% in the off-resonance band longer than their resonant wavelengths, exhibiting vivid subtractive colors. A wide range of colors can be facilitated by tuning the resonance by adjusting the structural parameters like the period and diameter of the a-Si:H nanodisk. The proposed devices will be actively utilized to implement color displays, imaging devices, and photorealistic color printing.

Polarization-Controlled Broad Color Palette Based on an Ultrathin One-Dimensional Resonant Grating Structure.

Highly efficient polarization-tuned structural color filters, which are based on a one- dimensional resonant aluminum grating that is integrated with a silicon nitride waveguide, are proposed and demonstrated to feature a broad color palette. For such a metallic grating structure, transmissive color filtering is only feasible for the incident transverse-magnetic (TM) polarization due to its high reflection regarding the transverse-electric (TE) case; however, polarization-tuned customized colors can be efficiently achieved by optimizing the structural parameters like the duty ratio of the metallic grating. For the fabricated color filters, the transmission peaks, which are imputed to the resonance between the incident light and the guided modes that are supported by the dielectric waveguide, provided efficiencies as high as 90% and 70% for the TM and TE polarizations, respectively, as intended. Through the tailoring of the polarization, a group of filters with different grating periods were successfully exploited to produce a broad color palette spanning the entire visible band. Lastly, a nanoscale alphabetic pattern featuring a flexible combination of colorations was practically constructed via an arrangement of horizontal and vertical gratings.