Starch Particles, Energy Harvesting, and the “Goldilocks Effect”

This study reports on the unique water vapor adsorption properties of biomass-derived starch particles (SPs). SPs offer an alternative desiccant for air-to-air energy exchangers in heating, ventilation, and air conditioning systems because of their remarkable adsorption–desorption performance. SP₁₅ has a particle diameter (<i>d</i>_p) of 15 μm with a surface area (SA) of 2.89 m²/g and a pore width (<i>P</i>_w) of 80 Å. Microporous starch particles (SP₁₅) were compared with high amylose starch (HAS₁₅; SA = 0.56 m²/g, <i>d</i>_p = 15 μm, <i>P</i>_w = 46 Å) and silica gel (SG₁₃; SA = 478 m²/g, <i>d</i>_p = 13 μm, <i>P</i>_w = 62 Å). Transient water vapor tests were performed using a customized small-scale energy exchanger coated with SP₁₅, HAS₁₅, and SG₁₃. The water swelling (%) for SP₁₅ was ca. 2 orders of magnitude greater with markedly higher (ca. three- and six-fold) water vapor uptake compared to HAS₁₅ and SG₁₃, respectively. At similar desiccant coating levels on the energy exchanger, the latent effectiveness of the SP₁₅ system was much improved (4–31%) over the HAS₁₅ and SG₁₃ systems at controlled operating conditions. SP₁₅ is a unique desiccant material with high affinity for water vapor and superior adsorption properties where ca. 98% regeneration was achieved under mild conditions. Therefore, SPs display unique adsorption–desorption properties, herein referred to as the “Goldilocks effect”. This contribution reports on the utility of SPs as promising desiccant coatings in air-to-air energy exchangers for ventilation systems or as advanced materials for potential water/energy harvesting applications

The Chemistry of CO₂ Capture in an Amine-Functionalized Metal–Organic Framework under Dry and Humid Conditions

The use of two primary alkylamine functionalities covalently tethered to the linkers of IRMOF-74-III results in a material that can uptake CO₂ at low pressures through a chemisorption mechanism. In contrast to other primary amine-functionalized solid adsorbents that uptake CO₂ primarily as ammonium carbamates, we observe using solid state NMR that the major chemisorption product for this material is carbamic acid. The equilibrium of reaction products also shifts to ammonium carbamate when water vapor is present; a new finding that has impact on control of the chemistry of CO₂ capture in MOF materials and one that highlights the importance of geometric constraints and the mediating role of water within the pores of MOFs

Data_Sheet_1_Identifying novel phenotypes of elevated left ventricular end diastolic pressure using hierarchical clustering of features derived from electromechanical waveform data.CSV

IntroductionElevated left ventricular end diastolic pressure (LVEDP) is a consequence of compromised left ventricular compliance and an important measure of myocardial dysfunction. An algorithm was developed to predict elevated LVEDP utilizing electro-mechanical (EM) waveform features. We examined the hierarchical clustering of selected features developed from these EM waveforms in order to identify important patient subgroups and assess their possible prognostic significance.Materials and methodsPatients presenting with cardiovascular symptoms (N = 396) underwent EM data collection and direct LVEDP measurement by left heart catheterization. LVEDP was classified as non-elevated ( ≤ 12 mmHg) or elevated (≥25 mmHg). The 30 most contributive features to the algorithm output were extracted from EM data and input to an unsupervised hierarchical clustering algorithm. The resultant dendrogram was divided into five clusters, and patient metadata overlaid.ResultsThe cluster with highest LVEDP (cluster 1) was most dissimilar from the lowest LVEDP cluster (cluster 5) in both clustering and with respect to clinical characteristics. In contrast to the cluster demonstrating the highest percentage of elevated LVEDP patients, the lowest was predominantly non-elevated LVEDP, younger, lower BMI, and males with a higher rate of significant coronary artery disease (CAD). The next adjacent cluster (cluster 2) to that of the highest LVEDP (cluster 1) had the second lowest LVEDP of all clusters. Cluster 2 differed from Cluster 1 primarily based on features extracted from the electrical data, and those that quantified predictability and variability of the signal. There was a low predictability and high variability in the highest LVEDP cluster 1, and the opposite in adjacent cluster 2.ConclusionThis analysis identified subgroups of patients with varying degrees of LVEDP elevation based on waveform features. An approach to stratify movement between clusters and possible progression of myocardial dysfunction may include changes in features that differentiate clusters; specifically, reductions in electrical signal predictability and increases in variability. Identification of phenotypes of myocardial dysfunction evidenced by elevated LVEDP and knowledge of factors promoting transition to clusters with higher levels of left ventricular filling pressures could permit early risk stratification and improve patient selection for novel therapeutic interventions.</p

Table_1_Identifying novel phenotypes of elevated left ventricular end diastolic pressure using hierarchical clustering of features derived from electromechanical waveform data.DOCX

IntroductionElevated left ventricular end diastolic pressure (LVEDP) is a consequence of compromised left ventricular compliance and an important measure of myocardial dysfunction. An algorithm was developed to predict elevated LVEDP utilizing electro-mechanical (EM) waveform features. We examined the hierarchical clustering of selected features developed from these EM waveforms in order to identify important patient subgroups and assess their possible prognostic significance.Materials and methodsPatients presenting with cardiovascular symptoms (N = 396) underwent EM data collection and direct LVEDP measurement by left heart catheterization. LVEDP was classified as non-elevated ( ≤ 12 mmHg) or elevated (≥25 mmHg). The 30 most contributive features to the algorithm output were extracted from EM data and input to an unsupervised hierarchical clustering algorithm. The resultant dendrogram was divided into five clusters, and patient metadata overlaid.ResultsThe cluster with highest LVEDP (cluster 1) was most dissimilar from the lowest LVEDP cluster (cluster 5) in both clustering and with respect to clinical characteristics. In contrast to the cluster demonstrating the highest percentage of elevated LVEDP patients, the lowest was predominantly non-elevated LVEDP, younger, lower BMI, and males with a higher rate of significant coronary artery disease (CAD). The next adjacent cluster (cluster 2) to that of the highest LVEDP (cluster 1) had the second lowest LVEDP of all clusters. Cluster 2 differed from Cluster 1 primarily based on features extracted from the electrical data, and those that quantified predictability and variability of the signal. There was a low predictability and high variability in the highest LVEDP cluster 1, and the opposite in adjacent cluster 2.ConclusionThis analysis identified subgroups of patients with varying degrees of LVEDP elevation based on waveform features. An approach to stratify movement between clusters and possible progression of myocardial dysfunction may include changes in features that differentiate clusters; specifically, reductions in electrical signal predictability and increases in variability. Identification of phenotypes of myocardial dysfunction evidenced by elevated LVEDP and knowledge of factors promoting transition to clusters with higher levels of left ventricular filling pressures could permit early risk stratification and improve patient selection for novel therapeutic interventions.</p

Subgroup analyses of the performance of the machine-learned predictor.

Subgroup analyses of the performance of the machine-learned predictor.</p

Feature values.

(XLSX)</p

Propensity matching.

(DOCX)</p

2x2 contingency Tables & additional statistics.

(DOCX)</p

Histogram of study population LVEDP.

Histogram of study population LVEDP.</p

Relationship between pre-test (prior) probability and post-test (posterior) probabilities.

a) the machine-learned predictor, b) BNP when greater than 150pg/ml, or c) BNP when greater than 50pg/ml. A positive test is shown in red, and a negative test in green. The diagonal dashed black line represents no change from the pre-test to post-test probability. The vertical dashed black lines represent intermediate to high pre-test probabilities (vertical dashed lines at 30%, 50% and 70%) from left to right. The post-test probabilities were calculated based on a varying pre-test probability, and constant sensitivity, specificity, and corresponding likelihood ratios.</p