Quantum annealing-aided design of an ultrathin-metamaterial optical diode.

Thin-film optical diodes are important elements for miniaturizing photonic systems. However, the design of optical diodes relies on empirical and heuristic approaches. This poses a significant challenge for identifying optimal structural models of optical diodes at given wavelengths. Here, we leverage a quantum annealing-enhanced active learning scheme to automatically identify optimal designs of 130 nm-thick optical diodes. An optical diode is a stratified volume diffractive film discretized into rectangular pixels, where each pixel is assigned to either a metal or dielectric. The proposed scheme identifies the optimal material states of each pixel, maximizing the quality of optical isolation at given wavelengths. Consequently, we successfully identify optimal structures at three specific wavelengths (600, 800, and 1000 nm). In the best-case scenario, when the forward transmissivity is 85%, the backward transmissivity is 0.1%. Electromagnetic field profiles reveal that the designed diode strongly supports surface plasmons coupled across counterintuitive metal-dielectric pixel arrays. Thereby, it yields the transmission of first-order diffracted light with a high amplitude. In contrast, backward transmission has decoupled surface plasmons that redirect Poynting vectors back to the incident medium, resulting in near attenuation of its transmission. In addition, we experimentally verify the optical isolation function of the optical diode.

Enhanced Thermal Boundary Conductance across GaN/SiC Interfaces with AlN Transition Layers.

Heat dissipation plays a crucial role in the performance and reliability of high-power GaN-based electronics. While AlN transition layers are commonly employed in the heteroepitaxial growth of GaN-on-SiC substrates, concerns have been raised about their impact on thermal transport across GaN/SiC interfaces. In this study, we present experimental measurements of the thermal boundary conductance (TBC) across GaN/SiC interfaces with varying thicknesses of the AlN transition layer (ranging from 0 to 73 nm) at different temperatures. Our findings reveal that the addition of an AlN transition layer leads to a notable increase in the TBC of the GaN/SiC interface, particularly at elevated temperatures. Structural characterization techniques are employed to understand the influence of the AlN transition layer on the crystalline quality of the GaN layer and its potential effects on interfacial thermal transport. To gain further insights into the trend of TBC, we conduct molecular dynamics simulations using high-fidelity deep learning-based interatomic potentials, which reproduce the experimentally observed enhancement in TBC even for atomically perfect interfaces. These results suggest that the enhanced TBC facilitated by the AlN intermediate layer could result from a combination of improved crystalline quality at the interface and the "phonon bridge" effect provided by AlN that enhances the overlap between the vibrational spectra of GaN and SiC.

Direct observation and identification of nanoplastics in ocean water.

Millions of tons of plastics enter the oceans yearly, and they can be fragmented by ultraviolet and mechanical means into nanoplastics. Here, we report the direct observation of nanoplastics in global ocean water leveraging a unique shrinking surface bubble deposition (SSBD) technique. SSBD involves optically heating plasmonic nanoparticles to form a surface bubble and leveraging the Marangoni flow to concentrate suspended nanoplastics onto the surface, allowing direct visualization using electron microscopy. With the plasmonic nanoparticles co-deposited in SSBD, the surface-enhanced Raman spectroscopy effect is enabled for direct chemical identification of trace amounts of nanoplastics. In the water samples from two oceans, we observed nanoplastics made of nylon, polystyrene, and polyethylene terephthalate-all common in daily consumables. The plastic particles have diverse morphologies, such as nanofibers, nanoflakes, and ball-stick nanostructures. These nanoplastics may profoundly affect marine organisms, and our results can provide critical information for appropriately designing their toxicity studies.

Anisotropic fluid flows in black phosphorus nanochannels.

With the development of advanced micro/nanoscale technologies, two-dimensional materials have emerged from laboratories and have been applied in practice. To investigate the mechanisms of solid-liquid interactions in potential applications, molecular dynamics simulations are employed to study the flow behavior of n-dodecane (C12) molecules confined in black phosphorus (BP) nanochannels. Under the same external conditions, a significant difference in the velocity profiles of fluid molecules is observed when flowing along the armchair and zigzag directions of the BP walls. The average velocity of C12 molecules flowing along the zigzag direction is 9-fold higher than that along the armchair direction. The friction factor at the interface between C12 molecules and BP nanochannels and the orientations of C12 molecules near the BP walls are analyzed to explain the differences in velocity profiles under various flow directions, external driving forces, and nanochannel widths. The result shows that most C12 molecules are oriented parallel to the flow direction along the zigzag direction, leading to a relatively smaller friction factor hence a higher average velocity. In contrast, along the armchair direction, most C12 molecules are oriented perpendicular to the flow direction, leading to a relatively larger friction factor and thus a lower average velocity. This work provides important insights into understanding the anisotropic liquid flows in nanochannels.

Bubble nucleation and growth on microstructured surfaces under microgravity.

Understanding the dynamics of surface bubble formation and growth on heated surfaces holds significant implications for diverse modern technologies. While such investigations are traditionally confined to terrestrial conditions, the expansion of space exploration and economy necessitates insights into thermal bubble phenomena in microgravity. In this work, we conduct experiments in the International Space Station to study surface bubble nucleation and growth in a microgravity environment and compare the results to those on Earth. Our findings reveal significantly accelerated bubble nucleation and growth rates, outpacing the terrestrial rates by up to ~30 times. Our thermofluidic simulations confirm the role of gravity-induced thermal convective flow, which dissipates heat from the substrate surface and thus influences bubble nucleation. In microgravity, the influence of thermal convective flow diminishes, resulting in localized heat at the substrate surface, which leads to faster temperature rise. This unique condition enables quicker bubble nucleation and growth. Moreover, we highlight the influence of surface microstructure geometries on bubble nucleation. Acting as heat-transfer fins, the geometries of the microstructures influence heat transfer from the substrate to the water. Finer microstructures, which have larger specific surface areas, enhance surface-to-liquid heat transfer and thus reduce the rate of surface temperature rise, leading to slower bubble nucleation. Our experimental and simulation results provide insights into thermal bubble dynamics in microgravity, which may help design thermal management solutions and develop bubble-based sensing technologies.

VarCards2: an integrated genetic and clinical database for ACMG-AMP variant-interpretation guidelines in the human whole genome.

VarCards, an online database, combines comprehensive variant- and gene-level annotation data to streamline genetic counselling for coding variants. Recognising the increasing clinical relevance of non-coding variations, there has been an accelerated development of bioinformatics tools dedicated to interpreting non-coding variations, including single-nucleotide variants and copy number variations. Regrettably, most tools remain as either locally installed databases or command-line tools dispersed across diverse online platforms. Such a landscape poses inconveniences and challenges for genetic counsellors seeking to utilise these resources without advanced bioinformatics expertise. Consequently, we developed VarCards2, which incorporates nearly nine billion artificially generated single-nucleotide variants (including those from mitochondrial DNA) and compiles vital annotation information for genetic counselling based on ACMG-AMP variant-interpretation guidelines. These annotations include (I) functional effects; (II) minor allele frequencies; (III) comprehensive function and pathogenicity predictions covering all potential variants, such as non-synonymous substitutions, non-canonical splicing variants, and non-coding variations and (IV) gene-level information. Furthermore, VarCards2 incorporates 368 820 266 documented short insertions and deletions and 2 773 555 documented copy number variations, complemented by their corresponding annotation and prediction tools. In conclusion, VarCards2, by integrating over 150 variant- and gene-level annotation sources, significantly enhances the efficiency of genetic counselling and can be freely accessed at http://www.genemed.tech/varcards2/.

Prioritizing de novo potential non-canonical splicing variants in neurodevelopmental disorders.

BACKGROUND: Genomic variants outside of the canonical splicing site (±2) may generate abnormal mRNA splicing, which are defined as non-canonical splicing variants (NCSVs). However, the clinical interpretation of NCSVs in neurodevelopmental disorders (NDDs) is largely unknown. METHODS: We investigated the contribution of NCSVs to NDDs from 345,787 de novo variants (DNVs) in 47,574 patients with NDDs. We performed functional enrichment and protein-protein interaction analysis to assess the association between genes carrying prioritised NCSVs and NDDs. Minigene was used to validate the impact of NCSVs on mRNA splicing. FINDINGS: We observed significantly more NCSVs (p = 0.02, odds ratio [OR] = 2.05) among patients with NDD than in controls. Both canonical splicing variants (CSVs) and NCSVs contributed to an equal proportion of patients with NDD (0.76% vs. 0.82%). The candidate genes carrying NCSVs were associated with glutamatergic synapse and chromatin remodelling. Minigene successfully validated 59 of 79 (74.68%) NCSVs that led to abnormal splicing in 40 candidate genes, and 9 of the genes (ARID1B, KAT6B, TCF4, SMARCA2, SHANK3, PDHA1, WDR45, SCN2A, SYNGAP1) harboured recurrent NCSVs with the same variant present in more than two unrelated patients with NDD. Moreover, 36 of 59 (61.02%) NCSVs are novel clinically relevant variants, including 34 unreported and 2 clinically conflicting interpretations or of uncertain significance NCSVs in the ClinVar database. INTERPRETATION: This study highlights the common pathology and clinical importance of NCSVs in unsolved patients with NDD. FUNDING: The present study was funded by grants from the National Natural Science Foundation of China, China Postdoctoral Science Foundation, the Hunan Youth Science and Technology Innovation Talent Project, the Provincial Natural Science Foundation of Hunan, The Scientific Research Program of FuRong laboratory, and the Natural Science Project of the University of Anhui Province.

Effect of polyene phosphatidylcholine/ursodeoxycholic acid/ademetionine on pregnancy outcomes in intrahepatic cholestasis.

BACKGROUND: Intrahepatic cholestasis of pregnancy (ICP) is a liver disorder that occurs in pregnant women and can lead to a range of adverse pregnancy outcomes. The condition is typically marked by pruritus (itching) and elevated levels of liver enzymes and bile acids. The standard treatment for ICP has generally been ursodeoxycholic acid and ademetionine 1,4-butanedisulfonate, but the efficacy of this approach remains less than optimal. Recently, polyene phosphatidylcholine has emerged as a promising new therapeutic agent for ICP due to its potential hepatoprotective effects. AIM: To evaluate the effect of polyene phosphatidylcholine/ursodeoxycholic acid/ ademetionine 1,4-butanedisulfonate on bile acid levels, liver enzyme indices, and pregnancy outcomes in patients with ICP. METHODS: From June 2020 to June 2021, 600 patients with ICP who were diagnosed and treated at our hospital were recruited and assigned at a ratio of 1:1 via random-number table method to receive either ursodeoxycholic acid/ademetionine 1,4-butanedisulfonate (control group, n = 300) or polyene phosphatidylcholine/ursodeoxycholic acid/ademetionine 1,4-butanedisulfonate (combined group, n = 300). Outcome measures included bile acids levels, liver enzyme indices, and pregnancy outcomes. RESULTS: Prior to treatment, no significant differences were observed between the two groups (P > 0.05). Post-treatment, patients in both groups had significantly lower pruritus scores, but the triple-drug combination group had lower scores than the dual-drug combination group (P < 0.05). The bile acid levels decreased significantly in both groups, but the decrease was more significant in the triple-drug group (P < 0.05). The triple-drug group also exhibited a greater reduction in the levels of certain liver enzymes and a lower incidence of adverse pregnancy outcomes compared to the dual-drug group (P < 0.05). CONCLUSION: Polyene phosphatidylcholine/ursodeoxycholic acid/ademetionine 1,4-butanedisulfonate effectively relieves pruritus and reduces bile acid levels and liver enzyme indices in patients with ICP, providing a positive impact on pregnancy outcome and a high safety profile. Further clinical trials are required prior to clinical application.

Eggshell Biowaste-Derived Flexible and Self-Cleaning Films for Efficient Subambient Daytime Radiative Cooling.

The management of the abundant eggshell biowaste produced worldwide has become a problematic issue due to the generated odor and microorganisms after direct disposal of the eggshell biowaste in landfills. Herein, we propose a new method to convert the hazardous eggshell biowaste to valuable resources for energy management applications. Eggshell-based films are fabricated by embedding eggshell powders into a polymer matrix to achieve highly efficient subambient daytime radiative cooling. Benefiting from the Mie scattering of the eggshell particles/air pores in the solar spectrum and the strong emission of the eggshell in the mid-infrared (mid-IR) range, the eggshell-based films present a high reflection of 0.96 in the solar spectrum and a high emission of 0.95 in the mid-IR range, with notable average temperature reductions of 4.1 and 11 °C below the ambient temperature during daytime and nighttime, respectively. Moreover, the eggshell-based films exhibit excellent flexibility and self-cleaning properties, which are beneficial for practical long-term outdoor applications. Our proposed design provides a new means for environmentally friendly and sustainable management of eggshell biowaste.

Design of a High-Performance Titanium Nitride Metastructure-Based Solar Absorber Using Quantum Computing-Assisted Optimization.

Metastructures of titanium nitride (TiN), a plasmonic refractory material, can potentially achieve high solar absorptance while operating at elevated temperatures, but the design has been driven by expert intuition. Here, we design a high-performance solar absorber based on TiN metastructures using quantum computing-assisted optimization. The optimization scheme includes machine learning, quantum annealing, and optical simulation in an iterative cycle. It designs an optimal structure with solar absorptance > 95% within 40 h, much faster than an exhaustive search. Analysis of electric field distributions demonstrates that combined effects of Fabry-Perot interferences and surface plasmonic resonances contribute to the broadband high absorption efficiency of the optimally designed metastructure. The designed absorber may exhibit great potential for solar energy harvesting applications, and the optimization scheme can be applied to the design of other complex functional materials.

Suppressing Dendrite Growth and Side Reactions via Mechanically Robust Laponite-Based Electrolyte Membranes for Ultrastable Aqueous Zinc-Ion Batteries.

The development of aqueous zinc-ion batteries (AZIBs) faces significant challenges because of water-induced side reactions arising from the high water activity in aqueous electrolytes. Herein, a quasi-solid-state electrolyte membrane with low water activity is designed based on a laponite (LP) nanoclay for separator-free AZIBs. The mechanically robust LP-based membrane can perform simultaneously as a separator and a quasi-solid-state electrolyte to inhibit dendrite growth and water-induced side reactions at the Zn/electrolyte interface. A combination of density functional theory calculations, theoretical analyses, and experiments ascertains that the water activities associated with self-dissociation, byproduct formation, and electrochemical decomposition could be substantially suppressed when the water molecules are absorbed by LP. This could be attributed to the high water adsorption and hydration capabilities of LP nanocrystals, resulting from the strong Coulombic and hydrogen-binding interactions between water and LP. Most importantly, the separator-free AZIBs exhibit high capacity retention rates of 94.10% after 2,000 cycles at 1 A/g and 86.32% after 10,000 cycles at 3 A/g, along with enhanced durability and record-low voltage decay rates over a 60-day storage period. This work provides a fundamental understanding of water activity and demonstrates that LP nanoclay is promising for ultrastable separator-free AZIBs for practical energy storage applications.

Hybrid Data-Driven Discovery of High-Performance Silver Selenide-Based Thermoelectric Composites.

Optimizing material compositions often enhances thermoelectric performances. However, the large selection of possible base elements and dopants results in a vast composition design space that is too large to systematically search using solely domain knowledge. To address this challenge, a hybrid data-driven strategy that integrates Bayesian optimization (BO) and Gaussian process regression (GPR) is proposed to optimize the composition of five elements (Ag, Se, S, Cu, and Te) in AgSe-based thermoelectric materials. Data is collected from the literature to provide prior knowledge for the initial GPR model, which is updated by actively collected experimental data during the iteration between BO and experiments. Within seven iterations, the optimized AgSe-based materials prepared using a simple high-throughput ink mixing and blade coating method deliver a high power factor of 2100 µW m-1 K-2 , which is a 75% improvement from the baseline composite (nominal composition of Ag2 Se1 ). The success of this study provides opportunities to generalize the demonstrated active machine learning technique to accelerate the development and optimization of a wide range of material systems with reduced experimental trials.

Molecular Alignment-Mediated Stick-Slip Poiseuille Flow of Oil in Graphene Nanochannels.

The flow behavior of oil in nanochannels has attracted extensive attention for oil transport applications. In most, if not all, of the prior theoretical simulations, oil molecules were observed to flow steadily in nanochannels under pressure gradients. In this study, non-equilibrium molecular dynamics simulations are conducted to simulate the Poiseuille flow of oil with three different hydrocarbon chain lengths in graphene nanochannels. Contrary to the conventional perception of steady flows of oil in nanochannels, we find that oil molecules with the longest hydrocarbon chain (i.e., n-dodecane) exhibit notable stick-slip flow behavior. An alternation between the high average velocity of n-dodecane in the slip motion and the low average velocity in the stick motion is observed, with a drastic, abrupt velocity jolt of up to 40 times occurring at the transition in a stick-slip motion. Further statistical analyses show that the stick-slip flow behavior of n-dodecane molecules originates from the molecular alignment change of oil near the graphene wall. The molecular alignment of n-dodecane shows different statistical distributions under stick and slip motion states, leading to significant changes of friction forces and thus notable velocity fluctuations. This work provides new insights into the Poiseuille flow behavior of oil in graphene nanochannels and may offer useful guidelines for other mass transport applications.

Analytical model of optical force on supercavitating plasmonic nanoparticles.

Optical manipulation of nanoparticles (NPs) in liquid has garnered increasing interest for various applications, ranging from biological systems to nanofabrication. A plane wave as an optical source has recently been shown to be capable of pushing or pulling an NP when the NP is encapsulated by a nanobubble (NB) in water. However, the lack of an accurate model to describe the optical force on NP-in-NB systems hinders a comprehensive understanding of NP motion mechanisms. In this study, we present an analytical model using vector spherical harmonics to accurately capture the optical force and the resultant trajectory of an NP in an NB. We test the developed model using a solid Au NP as an example. By visualizing the vector field line of the optical force, we reveal the possible moving paths of the NP in the NB. This study can provide valuable insights for designing experiments to manipulate supercaviting NPs using plane waves.

Interactions of genetic risks for autism and the broad autism phenotypes.

Background: Common polygenic risk and de novo variants (DNVs) capture a small proportion of autism spectrum disorder (ASD) liability, and ASD phenotypic heterogeneity remains difficult to explain. Integrating multiple genetic factors contribute to clarifying the risk and clinical presentation of ASD. Methods: In our study, we investigated the individual and combined effects of polygenic risk, damaging DNVs (including those in ASD risk genes), and sex among 2,591 ASD simplex families in the Simons Simplex Collection. We also explored the interactions among these factors, along with the broad autism phenotypes of ASD probands and their unaffected siblings. Finally, we combined the effects of polygenic risk, damaging DNVs in ASD risk genes, and sex to explain the total liability of ASD phenotypic spectrum. Results: Our findings revealed that both polygenic risk and damaging DNVs contribute to an increased risk for ASD, with females exhibiting higher genetic burdens than males. ASD probands that carry damaging DNVs in ASD risk genes showed reduced polygenic risk. The effects of polygenic risk and damaging DNVs on autism broad phenotypes were inconsistent; probands with higher polygenic risk exhibited improvement in some behaviors, such as adaptive/cognitive behaviors, while those with damaging DNVs exhibited more severe phenotypes. Siblings with higher polygenic risk and damaging DNVs tended to have higher scores on broader autism phenotypes. Females exhibited more severe cognitive and behavioral problems compared to males among both ASD probands and siblings. The combination of polygenic risk, damaging DNVs in ASD risk genes, and sex explained 1-4% of the total liability of adaptive/cognitive behavior measurements. Conclusion: Our study revealed that the risk for ASD and the autism broad phenotypes likely arises from a combination of common polygenic risk, damaging DNVs (including those in ASD risk genes), and sex.

Phonon Dominated Thermal Transport in Metallic Niobium Diselenide from First Principles Calculations.

Niobium diselenide (NbSe2) is a layered transition metal dichalcogenide material which possesses unique electrical and superconducting properties for future nanodevices. While the superconducting, electrical, and bulk thermal transport properties of NbSe2 have been widely studied, the in-plane thermal transport property of NbSe2, which is important for potential thermoelectric applications, has not been thoroughly investigated. In this report, we study the lattice in-plane thermal transport of 2D NbSe2 by solving the phonon Boltzmann transport equation with the help of the first principles calculation. The thermal conductivity obtained at room temperature is 12.3 W/mK. A detailed analysis shows that the transverse acoustic phonon dominates the lattice thermal transport, and an anomalously small portion of electron contribution to the total thermal conductivity is observed for this metallic phase. The results agree well with experimental measurements and provide detailed mode-by-mode thermal conductivity contribution from different phonon modes. This study can provide useful information for integrating NbSe2 in nanodevices where both electrical and thermal properties are critical, showing great potential for integrating monolayer NbSe2 to thermoelectric devices.

Severity of Autism Spectrum Disorder Symptoms Associated with de novo Variants and Pregnancy-Induced Hypertension.

Genetic factors, particularly, de novo variants (DNV), and an environment factor, exposure to pregnancy-induced hypertension (PIH), were reported to be associated with risk of autism spectrum disorder (ASD); however, how they jointly affect the severity of ASD symptom is unclear. We assessed the severity of core ASD symptoms affected by functional de novo variants or PIH. We selected phenotype data from Simon's Simplex Collection database, used genotypes from previous studies, and created linear regression models. We found that ASD patients carrying DNV with PIH exposure had increased adaptive and cognitive ability, decreased social problems, and enhanced repetitive behaviors; however, there was no difference in patients without DNV between those with or without PIH exposure. In addition, the DNV genes carried by patients exposed to PIH were enriched in ubiquitin-dependent proteolytic processes, highlighting how candidate genes in pathways and environments interact. The results indicate the joint contribution of DNV and PIH to ASD.

Performance evaluation of differential splicing analysis methods and splicing analytics platform construction.

A proportion of previously defined benign variants or variants of uncertain significance in humans, which are challenging to identify, may induce an abnormal splicing process. An increasing number of methods have been developed to predict splicing variants, but their performance has not been completely evaluated using independent benchmarks. Here, we manually sourced ∼50 000 positive/negative splicing variants from > 8000 studies and selected the independent splicing variants to evaluate the performance of prediction methods. These methods showed different performances in recognizing splicing variants in donor and acceptor regions, reminiscent of different weight coefficient applications to predict novel splicing variants. Of these methods, 66.67% exhibited higher specificities than sensitivities, suggesting that more moderate cut-off values are necessary to distinguish splicing variants. Moreover, the high correlation and consistent prediction ratio validated the feasibility of integration of the splicing prediction method in identifying splicing variants. We developed a splicing analytics platform called SPCards, which curates splicing variants from publications and predicts splicing scores of variants in genomes. SPCards also offers variant-level and gene-level annotation information, including allele frequency, non-synonymous prediction and comprehensive functional information. SPCards is suitable for high-throughput genetic identification of splicing variants, particularly those located in non-canonical splicing regions.

Molecular understanding of the effect of hydrogen on graphene growth by plasma-enhanced chemical vapor deposition.

Plasma-enhanced chemical vapor deposition (PECVD) provides a low-temperature, highly-efficient, and catalyst-free route to fabricate graphene materials by virtue of the unique properties of plasma. In this paper, we conduct reactive molecular dynamics simulations to theoretically study the detailed growth process of graphene by PECVD at the atomic scale. Hydrocarbon radicals with different carbon/hydrogen (C/H) ratios are employed as dissociated precursors in the plasma environment during the growth process. The simulation results show that hydrogen content in the precursors significantly affects the growth behavior and properties of graphene (e.g., the quality of obtained graphene, which is indicated by the number of hexagonal carbon rings formed in the graphene sheets). Moreover, increasing the content of hydrogen in the precursors is shown to reduce the growth rate of carbon clusters, and prevent the formation of curved carbon structures during the growth process. The findings provide a detailed understanding of the fundamental mechanisms regarding the effects of hydrogen on the growth of graphene in a PECVD process.

Effect of side-chain π-π stacking on the thermal conductivity switching in azobenzene polymers: a molecular dynamics simulation study.

The light switchable thermal conductivity displayed by some polymers makes them promising for applications like data storage, temperature regulation and light switchable devices. In this study, the mechanism of thermal conductivity switching in poly[6-(4-phenyldiazenyl phenoxy)hexyl metharylate] is studied using molecular dynamics (MD) simulations. The π-π stacking and amorphous polymer structures are specifically prepared through different simulation procedures, and the thermal conductivity of these structures is calculated. It is found that due to the π-π stacking structure, the thermal conductivity along the side-chain direction can change by 30-50% (from 0.34 to 0.51 W m-1 K-1). Through heat flux decomposition, it is found that the thermal conductivity change is dominated by the contribution from bonding interactions. This is because π-π stacking, which enforces the trans conformation, extends the side-chains of azobenzene polymers, making thermal transport in the side-chain direction more efficient. Along the polymer main-chain direction, the thermal conductivity is not affected by the π-π stacking of the side chains. This mechanism may be generalized to other types of polymers with azobenzene side-chains, which will develop a class of photo-responsive polymers.