Exploring near-infrared autofluorescence properties in parathyroid tissue: an analysis of fresh and paraffin-embedded thyroidectomy specimens.

Significance: Near-infrared autofluorescence (NIRAF) utilizes the natural autofluorescence of parathyroid glands (PGs) to improve their identification during thyroid surgeries, reducing the risk of inadvertent removal and subsequent complications such as hypoparathyroidism. This study evaluates NIRAF's effectiveness in real-world surgical settings, highlighting its potential to enhance surgical outcomes and patient safety. Aim: We evaluate the effectiveness of NIRAF in detecting PGs during thyroidectomy and central neck dissection and investigate autofluorescence characteristics in both fresh and paraffin-embedded tissues. Approach: We included 101 patients diagnosed with papillary thyroid cancer who underwent surgeries in 2022 and 2023. We assessed NIRAF's ability to locate PGs, confirmed via parathyroid hormone assays, and involved both junior and senior surgeons. We measured the accuracy, speed, and agreement levels of each method and analyzed autofluorescence persistence and variation over 10 years, alongside the expression of calcium-sensing receptor (CaSR) and vitamin D. Results: NIRAF demonstrated a sensitivity of 89.5% and a negative predictive value of 89.1%. However, its specificity and positive predictive value (PPV) were 61.2% and 62.3%, respectively, which are considered lower. The kappa statistic indicated moderate to substantial agreement (kappa = 0.478; P < 0.001 ). Senior surgeons achieved high specificity (86.2%) and PPV (85.3%), with substantial agreement (kappa = 0.847; P < 0.001 ). In contrast, junior surgeons displayed the lowest kappa statistic among the groups, indicating minimal agreement (kappa = 0.381; P < 0.001 ). Common errors in NIRAF included interference from brown fat and eschar. In addition, paraffin-embedded samples retained stable autofluorescence over 10 years, showing no significant correlation with CaSR and vitamin D levels. Conclusions: NIRAF is useful for PG identification in thyroid and neck surgeries, enhancing efficiency and reducing inadvertent PG removals. The stability of autofluorescence in paraffin samples suggests its long-term viability, with false positives providing insights for further improvements in NIRAF technology.

A copper missile-triggered power coalescence and death vortex within tumor cell mitochondria for synergistic cuproptosis/phototherapy/chemotherapy.

The importance of copper homeostasis in mitochondria and copper-triggered modality of mitochondrial cell death have been confirmed. However, the existing copper-based nanoplatforms are focused on synergistic therapies while the intracellular therapeutic targets are relatively scattered. Effective integration of all targets within mitochondria to generate power coalescence remains a challenge. Herein, we developed a novel copper-based delivery system to trigger power coalescence and death vortex within tumor cell mitochondria. Specifically, a mitochondrial targeting "copper missile" loaded with curcumin (termed as Cur@CuS-TPP-HA, CCTH) was designed for cuproptosis/phototherapy/chemotherapy synergistic anti-tumor therapy. Once the CCTH NPs are shuttled to the mitochondria, near-infrared (NIR) irradiation initiates the release of copper ions and curcumin for in situ drug accumulation in cancer cell mitochondria. An excess of copper ions and curcumin can activate cuproptosis and mitochondrial apoptosis pathways, respectively. When combined, they can cause an increase in reactive oxygen species (ROS), damage to mitochondrial DNA (mt-DNA), and a decrease in energy supply, thereby leading to a "vicious circle" of mitochondrial damage that further enhances the tumor-killing efficacy. As a consequence, this "copper missile" exhibits advanced anti-tumor effects as verified through in vitro assessments and in vivo evaluations using the 4T1 breast tumor model, providing a promising approach for cuproptosis-based synergistic anti-tumor therapy.

Triterpenoid and trichothecenes from a rhizospheric soil-derived Paramyrothecium sp. KMU22107.

A new phthalide derivative named paramlyktone (1) and a new arborinane-type triterpenoid named paramyrpenoid (2), together with ten previously described trichothecenes derivatives (3-12) were isolated and identified from a rhizospheric soil-derived Paramyrothecium sp. KMU22107 associated with Delphinium yunnanense. Their structural elucidation was achieved by the comprehensive analysis of spectroscopic data and comparison with literature values. Notably, paramyrpenoid (2) was the first example of an arborinane-type triterpenoid with a double bond at Δ12(13) and an additional methyl motif at C-8. This was the first report of arborinane-type triterpenoids from a fungus belonging to Paramyrothecium genus. In pharmacological studies, paramyrpenoid (2) demonstrated significant cytotoxic activity against the HL-60, SW480, A-549, MDA-MB-231 and SMMC-7721 cell lines, with IC50 values from 2.0 to 16.1 µM. Compounds 1 and 2 were also evaluated for anti-inflammatory, anti-acetylcholinesterase (AChE), and protein tyrosine phosphatase 1B (PTP1B) inhibitory activities in vitro.

Efficient Capture and Release of the Rare-Earth Element Neodymium in Aqueous Solution by Recyclable Covalent Organic Frameworks.

Rare-earth elements (REEs) are present in a broad range of critical materials. The development of solid adsorbents for REE capture could enable the cost-effective recycling of REE-containing magnets and electronics. In this context, covalent organic frameworks (COFs) are promising candidates for REE adsorption due to their exceptionally high surface area. Despite having attractive physical properties, COFs are heavily underutilized for REE capture applications due to their limited lifecycle in aqueous acidic environments, as well as synthetic challenges associated with the incorporation of ligands suitable for REE capture. Here, we show how the Ugi multicomponent reaction can be leveraged to postsynthetically modify imine-based COFs for the introduction of a diglycolic acid (DGA) moiety, an efficient scaffold for REE capture. The adsorption capacity of the DGA-functionalized COF was found to be more than 40 times higher than that of the pristine imine COF precursor and more than four times higher than that of the next-best reported DGA-functionalized solid support. This rationally designed COF has appealing characteristics of high adsorption capacity, fast and efficient capture and release of the REE ions, and reliable recyclability, making it one of the most promising adsorbents for solid-liquid REE ion extractions reported to date.

Chemical Upcycling of Polyolefin Plastics Using Structurally Well-defined Catalysts.

Single-use polyolefins are widely used in our daily life and industrial production due to their light weight, low cost, superior stability, and durability. However, the rapid accumulation of plastic waste and low-profit recycling methods resulted in a global plastic crisis. Catalytic hydrogenolysis is regarded as a promising technique, which can effectively and selectively convert polyolefin plastic waste to value-added products. In this perspective, we focus on the design and synthesis of structurally well-defined hydrogenolysis catalysts across mesoscopic, nanoscopic, and atomic scales, accompanied by our insights into future directions in catalyst design for further enhancing catalytic performance. These design principles can also be applied to the depolymerization of other polymers and ultimately realize the chemical upcycling of waste plastics.

Lightweight taxonomic profiling of long-read sequenced metagenomes with Lemur and Magnet.

Taxonomic profiling is a ubiquitous task in the analysis of clinical and environmental microbiomes. The advent of long-read sequencing of microbiomes necessitates the development of new taxonomic profilers tailored to long-read shotgun metagenomic datasets. Here, we introduce Lemur and Magnet, a pair of tools optimized for lightweight and accurate taxonomic profiling from long-read shotgun metagenomic datasets. Lemur is a marker-gene based method that leverages an EM algorithm to reduce false positive calls while preserving true positives; Magnet makes detailed presence/absence calls for bacterial genomes based on whole-genome read mapping. The tools work in sequence: Lemur estimates abundances conservatively, and Magnet operates on the genomes of identified organisms to filter out likely false positive taxa. The result is an increase in precision of as much as 70%, which far exceeds competing methods. By operating only on marker genes, Lemur is a comparatively lightweight software. We demonstrate that it can run in minutes to hours on a laptop with 32 GB of RAM, even for large inputs - a crucial feature given the portability of long-read sequencing machines. Furthermore, the marker gene database used by Lemur is only 4 GB and contains information from over 300,000 RefSeq genomes. The reference is available at https://zenodo.org/records/10802546, and the software is open-source and available at https://github.com/treangenlab/lemur.

A US perspective on closing the carbon cycle to defossilize difficult-to-electrify segments of our economy.

Electrification to reduce or eliminate greenhouse gas emissions is essential to mitigate climate change. However, a substantial portion of our manufacturing and transportation infrastructure will be difficult to electrify and/or will continue to use carbon as a key component, including areas in aviation, heavy-duty and marine transportation, and the chemical industry. In this Roadmap, we explore how multidisciplinary approaches will enable us to close the carbon cycle and create a circular economy by defossilizing these difficult-to-electrify areas and those that will continue to need carbon. We discuss two approaches for this: developing carbon alternatives and improving our ability to reuse carbon, enabled by separations. Furthermore, we posit that co-design and use-driven fundamental science are essential to reach aggressive greenhouse gas reduction targets.

Effects of Reishimmune-S, a Fungal Immunomodulatory Peptide Supplement, on the Quality of Life and Circulating Natural Killer Cell Profiles of Patients With Early Breast Cancer Receiving Adjuvant Endocrine Therapy.

OBJECTIVES: To evaluate the effects of Reishimmune-S, a fungal immunomodulatory peptide, on the quality of life (QoL) and natural killer (NK) cell subpopulations in patients receiving adjuvant endocrine therapy (ET) for breast cancer (BC). METHODS: Patients who received adjuvant ET for stage I-III hormone receptor-positive BC without active infection were enrolled in this prospective pilot study. Reishimmune-S was administered sublingually daily for 6 months. QoL scores, circulating immune cell levels, including lymphocyte/NK cell subpopulations, and plasma levels of interleukin (IL)-6 and tumor necrosis factor (TNF)-α were measured at baseline and every 4 weeks. Data were analyzed using linear mixed-effect regression models. RESULTS: Nineteen participants were included in the analyses. One patient with underlying asthma did not complete the study owing to the occurrence of skin rashes 15 days after the initiation of Reishimmune-S. No other adverse events were reported. Reishimmune-S supplementation significantly improved the cognitive function at 3 months and significantly decreased the fatigue and insomnia levels at 3 and 6 months, respectively. There was no significant change in the global health/QoL score between baseline and week 4 of treatment. The proportion of CD19+ lymphocytes was significantly higher at 3 and 6 months, and that of NKG2A+ and NKp30+ NK cells was significantly lower at 6 months than at baseline. In addition, fatigue positively correlated with the proportion of NKp30+ NK cells (ß ± standard error: 24.48 ± 8.75, P = .007 in the mixed-effect model). CONCLUSIONS: Short-term supplementation with Reishimmune-S affected the circulating immune cell composition and exerted positive effects on cognitive function, fatigue, and insomnia in patients with BC undergoing adjuvant ET, providing a potential approach for the management of treatment-related adverse reactions in this patient population.

Accurate preoperative prediction of nodal metastasis in papillary thyroid microcarcinoma: Towards optimal management of patients.

OBJECTIVE: To enhance the accuracy in predicting lymph node metastasis (LNM) preoperatively in patients with papillary thyroid microcarcinoma (PTMC), refining the "low-risk" classification for tailored treatment strategies. METHODS: This study involves the development and validation of a predictive model using a cohort of 1004 patients with PTMC undergoing thyroidectomy along with central neck dissection. The data was divided into a training cohort (n = 702) and a validation cohort (n = 302). Multivariate logistic regression identified independent LNM predictors in PTMC, leading to the construction of a predictive nomogram model. The model's performance was assessed through ROC analysis, calibration curve analysis, and decision curve analysis. RESULTS: Identified LNM predictors in PTMC included age, tumor maximum diameter, nodule-capsule distance, capsular contact length, bilateral suspicious lesions, absence of the lymphatic hilum, microcalcification, and sex. Especially, tumors larger than 7 mm, nodules closer to the capsule (less than 3 mm), and longer capsular contact lengths (more than 1 mm) showed higher LNM rates. The model exhibited AUCs of 0.733 and 0.771 in the training and validation cohorts respectively, alongside superior calibration and clinical utility. CONCLUSION: This study proposes and substantiates a preoperative predictive model for LNM in patients with PTMC, honing the precision of "low-risk" categorization. This model furnishes clinicians with an invaluable tool for individualized treatment approach, ensuring better management of patients who might be proposed observation or ablative options in the absence of such predictive information.

Catalytic Hydrogenolysis by Atomically Dispersed Iron Sites Embedded in Chemically and Redox Non-innocent N-Doped Carbon.

Atomically dispersed first-row transition metals embedded in nitrogen-doped carbon materials (M-N-C) show promising performance in catalytic hydrogenation but are less well-studied for reactions with more complex mechanisms, such as hydrogenolysis. Their ability to catalyze selective C-O bond cleavage of oxygenated hydrocarbons such as aryl alcohols and ethers is enhanced with the participation of ligands directly bound to the metal ion as well as longer-range contributions from the support. In this article, we describe how Fe-N-C catalysts with well-defined local structures for the Fe sites catalyze C-O bond hydrogenolysis. The reaction is facilitated by the N-C support. According to spectroscopic analyses, the as-synthesized catalysts contain mostly pentacoordinated FeIII sites, with four in-plane nitrogen donor ligands and one axial hydroxyl ligand. In the presence of 20 bar of H2 at 170-230 °C, the hydroxyl ligand is lost when N4FeIIIOH is reduced to N4FeII, assisted by the H2 chemisorbed on the support. When an alcohol binds to the tetracoordinated FeII sites, homolytic cleavage of the O-H bond is accompanied by reoxidation to FeIII and H atom transfer to the support. The role of the N-C support in catalytic hydrogenolysis is analogous to the behavior of chemically and redox-non-innocent ligands in molecular catalysts based on first-row transition metal ions and enhances the ability of M-N-Cs to achieve the types of multistep activations of strong bonds needed to upgrade renewable and recycled feedstocks.

Changes in microbial composition and interaction patterns of female urogenital tract and rectum in response to HPV infection.

BACKGROUND: Previous studies have shown that changes in the microbial community of the female urogenital tract are associated with Human papillomavirus (HPV) infection. However, research on this association was mostly focused on a single site, and there are currently few joint studies on HPV infection and multiple sites in the female urogenital tract. METHODS: We selected 102 healthy women from Yunnan Province as the research object, collected cervical exfoliation fluid, vaginal, urethral, and rectal swabs for microbial community analysis, and measured bacterial load, and related cytokine content. The link between HPV, microbiota, and inflammation was comprehensively evaluated using bioinformatics methods. FINDINGS: The impact of HPV infection on the microbial composition of different parts varies. We have identified several signature bacterial genera that respond to HPV infection in several detection sites, such as Corynebacterium, Lactobacillus, Campylobacter, and Cutibacterium have been detected in multiple sites, reflecting their potential significance in cross body sites HPV infection responses. There was a solid microbial interaction network between the cervix, vagina, and urethra. The interrelationships between inflammatory factors and different bacterial genera might also affect the immune system's response to HPV infection. INTERPRETATION: It might be an effective strategy to prevent and treat HPV infection by simultaneously understanding the correlation between the microbial changes in multiple parts of the female urogenital tract and rectum and HPV infection, and controlling the microbial network related to HPV infection in different parts.

Intraoperative AI-assisted early prediction of parathyroid and ischemia alert in endoscopic thyroid surgery.

BACKGROUND: The preservation of parathyroid glands is crucial in endoscopic thyroid surgery to prevent hypocalcemia and related complications. However, current methods for identifying and protecting these glands have limitations. We propose a novel technique that has the potential to improve the safety and efficacy of endoscopic thyroid surgery. PURPOSE: Our study aims to develop a deep learning model called PTAIR 2.0 (Parathyroid gland Artificial Intelligence Recognition) to enhance parathyroid gland recognition during endoscopic thyroidectomy. We compare its performance against traditional surgeon-based identification methods. MATERIALS AND METHODS: Parathyroid tissues were annotated in 32 428 images extracted from 838 endoscopic thyroidectomy videos, forming the internal training cohort. An external validation cohort comprised 54 full-length videos. Six candidate algorithms were evaluated to select the optimal one. We assessed the model's performance in terms of initial recognition time, identification duration, and recognition rate and compared it with the performance of surgeons. RESULTS: Utilizing the YOLOX algorithm, we developed PTAIR 2.0, which demonstrated superior performance with an AP50 score of 92.1%. The YOLOX algorithm achieved a frame rate of 25.14 Hz, meeting real-time requirements. In the internal training cohort, PTAIR 2.0 achieved AP50 values of 94.1%, 98.9%, and 92.1% for parathyroid gland early prediction, identification, and ischemia alert, respectively. Additionally, in the external validation cohort, PTAIR outperformed both junior and senior surgeons in identifying and tracking parathyroid glands (p < 0.001). CONCLUSION: The AI-driven PTAIR 2.0 model significantly outperforms both senior and junior surgeons in parathyroid gland identification and ischemia alert during endoscopic thyroid surgery, offering potential for enhanced surgical precision and patient outcomes.

Optimizing robotic thyroid surgery: lessons learned from an retrospective analysis of 104 cases.

Background: Robotic assistance in thyroidectomy is a developing field that promises enhanced surgical precision and improved patient outcomes. This study investigates the impact of the da Vinci Surgical System on operative efficiency, learning curve, and postoperative outcomes in thyroid surgery. Methods: We conducted a retrospective cohort study of 104 patients who underwent robotic thyroidectomy between March 2018 and January 2022. We evaluated the learning curve using the Cumulative Sum (CUSUM) analysis and analyzed operative times, complication rates, and postoperative recovery metrics. Results: The cohort had a mean age of 36 years, predominantly female (68.3%). The average body mass index (BMI) was within the normal range. A significant reduction in operative times was observed as the series progressed, with no permanent hypoparathyroidism or recurrent laryngeal nerve injuries reported. The learning curve plateaued after the 37th case. Postoperative recovery was consistent, with no significant difference in hospital stay duration. Complications were minimal, with a noted decrease in transient vocal cord palsy as experience with the robotic system increased. Conclusion: Robotic thyroidectomy using the da Vinci system has demonstrated a significant improvement in operative efficiency without compromising safety. The learning curve is steep but manageable, and once overcome, it leads to improved surgical outcomes and high patient satisfaction. Further research with larger datasets and longer follow-up is necessary to establish the long-term benefits of robotic thyroidectomy.

Population Balance Equations for Reactive Separation in Polymer Upcycling.

Many polymer upcycling efforts aim to convert plastic waste into high-value liquid hydrocarbons. However, the subsequent cleavage of middle distillates to light gases can be problematic. The reactor often contains a vapor phase (light gases and middle distillates) and a liquid phase (molten polymers and waxes with a suspended or dissolved catalyst). Because the catalyst resides in the liquid phase, middle distillates that partition into the vapor phase are protected against further cleavage into light gases. In this paper, we consider a simple reactive separation strategy, in which a gas outflow removes the volatile products as they form. We combine vapor-liquid equilibrium models and population balance equations (PBEs) to describe polymer upcycling in a two-phase semibatch reactor. The results suggest that the temperature, headspace volume, and flow rate of the reactor can be used to tune selectivity toward the middle distillates, in addition to the molecular mechanism of catalysis. We anticipate that two-phase reactor models will be important in many polymer upcycling processes and that reactive separation strategies will provide ways to boost the yield of the desired products in these cases.

Application of deep learning image reconstruction-high algorithm in one-stop coronary and carotid-cerebrovascular CT angiography with low radiation and contrast doses.

Background: For patients with suspected simultaneous coronary and cerebrovascular atherosclerosis, conventional single-site computed tomography angiography (CTA) for both sites can result in nonnegligible radiation and contrast agent dose. The purpose of this study was to validate the feasibility of one-stop coronary and carotid-cerebrovascular CTA (C&CC-CTA) with a "double-low" (low radiation and contrast) dose protocol reconstructed with deep learning image reconstruction with high setting (DLIR-H) algorithm. Methods: From February 2018 to January 2019, 60 patients referred to C&CC-CTA simultaneously in West China Hospital were recruited in this prospective cohort study. By random assignment, patients were divided into two groups: double-low dose group (n=30) used 80 kVp and 24 mgI/kg/s contrast dose with images reconstructed using DLIR-H; and routine-dose group (n=30) used 100 kVp and 32 mgI/kg/s contrast dose with images reconstructed using 50% adaptive statistical iterative reconstruction-V (ASIR-V50%). Radiation and contrast doses, subjective image quality score, CT attenuation values, noise, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were measured and compared between the groups. Results: The DLIR-H group used 30% less contrast dose (35.80±4.85 vs. 51.13±6.91 mL) and 48% less overall radiation dose (1.00±0.09 vs. 1.91±0.42 mSv) than the ASIR-V50% group (both P<0.001). There was no statistically significant difference on subjective quality score between the two groups (C-CTA: 4.38±0.67 vs. 4.17±0.81, P=0.337 and CC-CTA: 4.18±0.87 vs. 4.08±0.79, P=0.604). For coronary CTA, lower background noise (18.93±1.43 vs. 22.86±3.75 HU) was reached in DLIR-H group, and SNR and CNR at all assessed branches were significantly increased compared to ASIR-V50% group (all P<0.05), except SNR of left anterior descending (P>0.05). For carotid-cerebrovascular CTA, DLIR-H group was comparable in background noise (19.25±1.42 vs. 20.23±2.40 HU), SNR and CNR at all assessed branches with ASIR-V50% group (all P>0.05). Conclusions: The "double-low" dose one-stop C&CC-CTA with DLIR-H obtained higher image quality compared with the routine-dose protocol with ASIR-V50% while achieving 48% and 30% reduction in radiation and contrast dose, respectively.

Supported Platinum Nanoparticles Catalyzed Carbon-Carbon Bond Cleavage of Polyolefins: Role of the Oxide Support Acidity.

Supported platinum nanoparticle catalysts are known to convert polyolefins to high-quality liquid hydrocarbons using hydrogen under relatively mild conditions. To date, few studies using platinum grafted onto various metal oxide (MxOy) supports have been undertaken to understand the role of the acidity of the oxide support in the carbon-carbon bond cleavage of polyethylene under consistent catalytic conditions. Specifically, two Pt/MxOy catalysts (MxOy = SrTiO3 and SiO2-Al2O3; Al = 3.0 wt %, target Pt loading 2 wt % Pt ∼1.5 nm), under identical catalytic polyethylene hydrogenolysis conditions (T = 300 °C, P(H2) = 170 psi, t = 24 h; Mw = ∼3,800 g/mol, Mn = ∼1,100 g/mol, D = 3.45, Nbranch/100C = 1.0), yielded a narrow distribution of hydrocarbons with molecular weights in the range of lubricants (Mw = < 600 g/mol; Mn < 400 g/mol; D = 1.5). While Pt/SrTiO3 formed saturated hydrocarbons with negligible branching, Pt/SiO2-Al2O3 formed partially unsaturated hydrocarbons (<1 mol % alkenes and ∼4 mol % alkyl aromatics) with increased branch density (Nbranch/100C = 5.5). Further investigations suggest evidence for a competitive hydrocracking mechanism occurring alongside hydrogenolysis, stemming from the increased acidity of Pt/SiO2-Al2O3 compared to Pt/SrTiO3. Additionally, the products of these polymer deconstruction reactions were found to be independent of the polyethylene feedstock, allowing the potential to upcycle polyethylenes with various properties into a value-added product.

Undescribed cytotoxic bisabosqual-type meroterpenoids from an endophytic Stachybotryaceae sp. associated with Delphinium yunnanense.

Five undescribed bisabosqual-type meroterpenoids, bisabosquals E (1) and F (2), stachybisbins J-L (4-6), together with two known ones, were isolated from a novel endophytic fungus KMU22001 within the Stachybotryaceae family. Their structures with absolute configurations were elucidated by detailed interpretation of NMR spectroscopy, mass spectrometry, single-crystal X-ray diffraction and electronic circular dichroism calculations. Compounds 2, 4 and 6 exhibited significant cytotoxicities against five human cancer cell lines with IC50 values ranging from 1.80 ± 0.08 to 17.76 ± 0.97 µM.

Reaction-Initiated Single-Molecule Tracking of Mass Transfer in Core-Shell Mesoporous Silica Particles.

Understanding the host-guest interactions in porous materials is of great importance in the field of separation science. Probing it at the single-molecule level uncovers the inter- and intraparticle inhomogeneity and establishes structure-property relationships for guiding the design of porous materials for better separation performance. In this work, we investigated the dynamics of host-guest interactions in core-shell mesoporous silica particles under in situ conditions by using a fluorogenic reaction-initiated single-molecule tracking (riSMT) approach. Taking advantage of the low fluorescence background, three-dimensional (3D) tracking of the dynamics of the molecules inside the mesoporous silica pore was achieved with high spatial precision. Compared to the commonly used two-dimensional (2D) tracking method, the 3D tracking results show that the diffusion coefficients of the molecules are three times larger on average. Using riSMT, we quantitatively analyzed the mass transfer of probe molecules in the mesoporous silica pore, including the fraction of adsorption versus diffusion, diffusion coefficients, and residence time. Large interparticle inhomogeneity was revealed and is expected to contribute to the peak broadening for separation application at the ensemble level. We further investigated the impact of electrostatic interaction on the mass transfer of molecules in the mesoporous silica pore and discovered that the primary effect is on the fraction rather than their diffusion rates of resorufin molecules undergoing diffusion.

Breaking New Ground: MBene Route toward Selective Vinyl Double Bond Hydrogenation in Nitroarenes.

Doping, or incremental substitution of one element for another, is an effective way to tailor a compound's structure as well as its physical and chemical properties. Herein, we replaced up to 30% of Ni with Co in members of the family of layered LiNiB compounds, stabilizing the high-temperature polymorph of LiNiB while the room-temperature polymorph does not form. By studying this layered boride with in situ high-temperature powder diffraction, we obtained a distorted variant of LiNi0.7Co0.3B featuring a perfect interlayer placement of [Ni0.7Co0.3B] layers on top of each other─a structural motif not seen before in other borides. Because of the Co doping, LiNi0.7Co0.3B can undergo a nearly complete topochemical Li deintercalation under ambient conditions, resulting in a metastable boride with the formula Li0.04Ni0.7Co0.3B. Heating of Li0.04Ni0.7Co0.3B in anaerobic conditions led to yet another metastable boride, Li0.01Ni0.7Co0.3B, with a CoB-type crystal structure that cannot be obtained by simple annealing of Ni, Co, and B. No significant alterations of magnetic properties were detected upon Co-doping in the temperature-independent paramagnet LiNi0.7Co0.3B or its Li-deintercalated counterparts. Finally, Li0.01Ni0.7Co0.3B stands out as an exceptional catalyst for the selective hydrogenation of the vinyl C═C bond in 3-nitrostyrene, even in the presence of other competing functional groups. This research showcases an innovative approach to heterogeneous catalyst design by meticulously synthesizing metastable compounds.

Hydrogen spillover and substrate-support hydrogen bonding mediate hydrogenation of phenol catalyzed by palladium on reducible metal oxides.

Substrate-support interactions play an important role in the catalytic hydrogenation of phenolic compounds by ceria-supported palladium (Pd/CeO2). Here, we combine surface contrast solution NMR methods and reaction kinetic assays to investigate the role of substrate-support interactions in phenol (PhOH) hydrogenation catalyzed by titania-supported palladium (Pd/TiO2). We show that PhOH adsorbs on the catalyst via a weak hydrogen-bonding interaction between the -OH group of the substrate and one oxygen atom on the support. Interestingly, we observe that the addition of 20 mM inorganic phosphate results in a ∼2-fold destabilization of the PhOH-support interaction and a corresponding ∼2-fold inhibition of the catalytic reaction, suggesting an active role of the PhOH-TiO2 hydrogen bond in catalysis. A comparison of the data measured here with the results previously reported for a Pd/CeO2 catalyst indicates that the efficiency of the Pd-supported catalysts is correlated to the amount of PhOH hydrogen bonded to the metal oxide support. Since CeO2 and TiO2 have similar ability to uptake activated hydrogen from a noble metal site, these data suggest that hydrogen spillover is the main mechanism by which Pd-activated hydrogens are shuttled to the PhOH adsorbed on the surface of the support. Consistent with this hypothesis, Pd supported on a non-reducible metal oxide (silica) displays negligible hydrogenation activity. Therefore, we conclude that basic and reducible metal oxides are active supports for the efficient hydrogenation of phenolic compounds due to their ability to hydrogen bond to the substrate and mediate the addition of the activated hydrogens to the adsorbed aromatic ring.