Comparison of kinetic model and experimental data for absorption performance of P-100-90C absorbent.

<p>Comparison of kinetic model and experimental data for absorption performance of P-100-90C absorbent.</p

Kinetic analysis of an anion exchange absorbent for CO₂ capture from ambient air - Fig 7

<p>CO₂ desorption process of four absorbents (A) P-100-90C absorbent, (B) P-100-50C absorbent, (C) P-100-25C absorbent, (D) I-200 absorbent. Left Y-axis is absorbent weight, and right Y-axis is CO₂ concentration.</p

Comparison of CO₂ absorption half times and capacities of different sorbents.

<p>Hyperbranched aminosilica (HAS) with different amine loading (-), PEI/silica materials (□), and moisture-swing Ion Exchange Resin (IER) (●). The number of half time and absorption capacity of each absorbent has been shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0179828#pone.0179828.s001" target="_blank">S1 Table</a>.</p

The schematic of the experimental device.

<p>The total amount of CO₂ in the sample and in the gas volume is constant. The process of CO₂ absorption/desorption can be identified in the experimental device.</p

Diffusion coefficients of water, equilibrium times of water and CO₂ in four samples.

<p>Diffusion coefficients of water, equilibrium times of water and CO₂ in four samples.</p

Chemical structure of ion exchange resin containing two side chains.

<p>The exchanged anion is CO₃^2-.</p

Spontaneous Cooling Absorption of CO₂ by a Polymeric Ionic Liquid for Direct Air Capture

A polymeric ionic liquid (PIL), with quaternary ammonium ions attached to the polymer matrix, displays CO₂ affinity controlled by moisture. This finding led to the development of moisture swing absorption (MSA) for direct air capture of CO₂. This work aims to elucidate the role of water in MSA. For some humidity range, CO₂ absorption is an endothermic process associated with concurrent dehydration of the sorbent. The thermodynamic behavior of water indicates a decreased hydrophilicity of the PIL as the mobile anion transforms from CO₃^2– to HCO₃^– during CO₂ absorption. The decrease in hydrophilicity drives water out of the PIL, carrying heat away. The mechanism is elucidated by molecular modeling based on density functional theory. The finding of spontaneous cooling during absorption and its mechanism in the PIL opens new possibilities for designing an air capture sorbent with a strong CO₂ affinity but low absorption heat

Table_2_Machine learning-based identification of colorectal advanced adenoma using clinical and laboratory data: a phase I exploratory study in accordance with updated World Endoscopy Organization guidelines for noninvasive colorectal cancer screening tests.docx

ObjectiveThe recent World Endoscopy Organization (WEO) guidelines now recognize precursor lesions of colorectal cancer (CRC) as legitimate screening targets. However, an optimal screening method for detecting advanced adenoma (AA), a significant precursor lesion, remains elusive.MethodsWe employed five machine learning methods, using clinical and laboratory data, to develop and validate a diagnostic model for identifying patients with AA (569 AAs vs. 3228 controls with normal colonoscopy). The best-performing model was selected based on sensitivity and specificity assessments. Its performance in recognizing adenoma-carcinoma sequence was evaluated in line with guidelines, and adjustable thresholds were established. For comparison, the Fecal Occult Blood Test (FOBT) was also selected.ResultsThe XGBoost model demonstrated superior performance in identifying AA, with a sensitivity of 70.8% and a specificity of 83.4%. It successfully detected 42.7% of non-advanced adenoma (NAA) and 80.1% of CRC. The model-transformed risk assessment scale provided diagnostic performance at different positivity thresholds. Compared to FOBT, the XGBoost model better identified AA and NAA, however, was less effective in CRC.ConclusionThe XGBoost model, compared to FOBT, offers improved accuracy in identifying AA patients. While it may not meet the recommendations of some organizations, it provides value for individuals who are unable to use FOBT for various reasons.</p

Table_3_Machine learning-based identification of colorectal advanced adenoma using clinical and laboratory data: a phase I exploratory study in accordance with updated World Endoscopy Organization guidelines for noninvasive colorectal cancer screening tests.docx

ObjectiveThe recent World Endoscopy Organization (WEO) guidelines now recognize precursor lesions of colorectal cancer (CRC) as legitimate screening targets. However, an optimal screening method for detecting advanced adenoma (AA), a significant precursor lesion, remains elusive.MethodsWe employed five machine learning methods, using clinical and laboratory data, to develop and validate a diagnostic model for identifying patients with AA (569 AAs vs. 3228 controls with normal colonoscopy). The best-performing model was selected based on sensitivity and specificity assessments. Its performance in recognizing adenoma-carcinoma sequence was evaluated in line with guidelines, and adjustable thresholds were established. For comparison, the Fecal Occult Blood Test (FOBT) was also selected.ResultsThe XGBoost model demonstrated superior performance in identifying AA, with a sensitivity of 70.8% and a specificity of 83.4%. It successfully detected 42.7% of non-advanced adenoma (NAA) and 80.1% of CRC. The model-transformed risk assessment scale provided diagnostic performance at different positivity thresholds. Compared to FOBT, the XGBoost model better identified AA and NAA, however, was less effective in CRC.ConclusionThe XGBoost model, compared to FOBT, offers improved accuracy in identifying AA patients. While it may not meet the recommendations of some organizations, it provides value for individuals who are unable to use FOBT for various reasons.</p

Table_1_Machine learning-based identification of colorectal advanced adenoma using clinical and laboratory data: a phase I exploratory study in accordance with updated World Endoscopy Organization guidelines for noninvasive colorectal cancer screening tests.docx

ObjectiveThe recent World Endoscopy Organization (WEO) guidelines now recognize precursor lesions of colorectal cancer (CRC) as legitimate screening targets. However, an optimal screening method for detecting advanced adenoma (AA), a significant precursor lesion, remains elusive.MethodsWe employed five machine learning methods, using clinical and laboratory data, to develop and validate a diagnostic model for identifying patients with AA (569 AAs vs. 3228 controls with normal colonoscopy). The best-performing model was selected based on sensitivity and specificity assessments. Its performance in recognizing adenoma-carcinoma sequence was evaluated in line with guidelines, and adjustable thresholds were established. For comparison, the Fecal Occult Blood Test (FOBT) was also selected.ResultsThe XGBoost model demonstrated superior performance in identifying AA, with a sensitivity of 70.8% and a specificity of 83.4%. It successfully detected 42.7% of non-advanced adenoma (NAA) and 80.1% of CRC. The model-transformed risk assessment scale provided diagnostic performance at different positivity thresholds. Compared to FOBT, the XGBoost model better identified AA and NAA, however, was less effective in CRC.ConclusionThe XGBoost model, compared to FOBT, offers improved accuracy in identifying AA patients. While it may not meet the recommendations of some organizations, it provides value for individuals who are unable to use FOBT for various reasons.</p