Solar‐Powered High‐Performance Lignin‐Wood Evaporator for Solar Steam Generation
Bibliographic record
Abstract
Abstract Recent research on wood‐based solar evaporators has made great progress and significant breakthroughs have been made in using lignin as a photothermal material; however, the intensity change mechanism regarding the conjugate structure of lignin is almost never mentioned. This study innovatively proposes a mechanism to explain the changes in conjugate intensity that occur before and after lignin dissolution and fabricates a lignin/wood‐based solar evaporator (LWE) using an all‐wood‐based material that is salt‐tolerant and has long‐term serviceability. Lignin in the evaporator serves not only as a photothermal material for converting light energy into heat energy but also as a reinforcement for the evaporator's structural strength. Adding lignin changes the original structure of balsa wood, increasing the proportion of intermediate water in the LWE, thereby lowering the enthalpy of water evaporation. The optimized LWE with an enhanced desalination capability, dye removal property, and high stability exhibits full‐spectrum solar absorption of about 83.6%, a photothermal conversion efficiency of 91.74%, and an evaporation efficiency of 1.93 kg m −2 h −1 , which surpasses most wood‐based evaporators. This study demonstrates that all‐wood‐based materials can be used to prepare evaporators with excellent performance, providing a new approach to address freshwater depletion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".